FACULTEIT FARMACEUTISCHE...

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FACULTEIT FARMACEUTISCHE WETENSCHAPPEN Vakgroep Farmaceutische Analyse Laboratory of Drug Quality and Registration (DruQuaR) Academiejaar 2011-2012 Lipopeptides: column comparison and dry heat stress of polymyxin B sulphate Apr. Matthias VAN LAETHEM Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B. De Spiegeleer

Transcript of FACULTEIT FARMACEUTISCHE...

Page 1: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

FACULTEIT FARMACEUTISCHE WETENSCHAPPEN

Vakgroep Farmaceutische Analyse

Laboratory of Drug Quality and Registration (DruQuaR)

Academiejaar 2011-2012

Lipopeptides column comparison and dry heat stress of polymyxin B sulphate

Apr Matthias VAN LAETHEM

Master na Master in de Industrieumlle Farmacie

Promotor Prof Dr Apr B De Spiegeleer

FACULTEIT FARMACEUTISCHE WETENSCHAPPEN

Vakgroep Farmaceutische Analyse

Laboratory of Drug Quality and Registration (DruQuaR)

Academiejaar 2011-2012

Lipopeptides column comparison and dry heat stress of polymyxin B sulphate

Apr Matthias VAN LAETHEM

Master na Master in de Industrieumlle Farmacie

Promotor Prof Dr Apr B De Spiegeleer

i

ACKNOWLEDGEMENTS

Het tot stand brengen van een thesis is een boeiende opdracht maar is niet altijd

even vanzelfsprekend Het is dan ook niet mogelijk om dit te doen zonder hulp en steun

van mensen die mij als student omringd hebben

In de eerste plaats wil ik de heer Bart De Spiegeleer bedanken Hij maakte het

mogelijk om mijn kennis te verruimen door in contact te komen met allerlei technieken die

ik ervoor niet of enkel in theorie kende

Matthias DrsquoHondt bedankt voor de algemene begeleiding en het leveren van

constructieve commentaren en suggesties die mijn wetenschappelijke kritische houding

heeft aangescherpt Verder wil ik ook collegarsquos en personeel bedanken voor de

aangename werksfeer

Uiteraard wil ik ook vrienden en kennissen bedanken voor de ontspanning na soms

lange dagen in het labo Als laatste wil ik nog mijn vriendin Lore en mijn ouders bedanken

die de grootste steunpilaar waren voor het volbrengen van deze thesis hiervoor bedankt

ii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

TABLE OF CONTENTS ii

LIST OF ABBREVATIONS AND ACRONYMS v

1 INTRODUCTION 1

11 Peptides 1

111 Amino acids 1

112 Peptides 2

1121 Peptide bond 2

1122 Production of peptides 2

1123 Structure of peptides 3

1124 Peptides versus proteins 3

113 Therapeutic peptides 4

12 Lipopeptides 6

121 Lipopeptide anti-infectives 6

122 Lipopeptide vaccines 7

13 Polymyxin 7

131 History and structure 7

132 Mechanism of action 8

133 Toxicity 8

134 Commercial formulations 9

135 Polymyxin B 10

1351 Production of polymyxin B 11

14 Heat stress 11

141 Voluntary heat treatment 11

1411 Hot-melt extrusion 11

1412 Dry heat sterilization 13

142 Involuntary heat treatment 13

2 OBJECTIVES 14

3 MATERIALS AND METHODS 15

31 Materials 15

32 Lipopeptide clustering 15

iii

33 Column comparison 17

331 Column selection 17

332 Chromatography 18

333 Chromatographic response factors 19

34 Gradient optimization and method verification of polymyxin B sulphate analysis 20

341 Chromatography 21

3411 Scouting gradient and UPLC gradient optimization 21

3412 Final UPLC method 22

342 Chromatographic response factors 23

343 Method verification 25

3431 LoD and LoQ 25

3432 Linearity of analytical response 25

3433 Precision 25

3434 Carry-over 25

3435 Analytical stability 26

35 Dry heat stress kinetics of polymyxin B sulphate 26

351 Chromatography 26

352 Dry heat stress conditions 26

353 Quantitative dry heat stress experiments 27

3531 Calculation of degradation constants 27

3532 Calculation of Arrhenius parameters Ea and A 27

4 RESULTS AND DISCUSSION 28

41 Lipopeptide clustering 28

42 Column comparison 30

421 Chromatographic response factors 30

4211 Asymmetry factor 31

4212 Limit of detection 31

4213 Time-corrected resolution product 31

4214 Separation factor 32

4215 Peak-to-valley ratio 32

4216 Peak capacity 32

4217 Chromatographic response factor 33

422 Overall performance 33

43 Gradient optimization and method-verification of polymyxin B sulphate analysis 34

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 2: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

FACULTEIT FARMACEUTISCHE WETENSCHAPPEN

Vakgroep Farmaceutische Analyse

Laboratory of Drug Quality and Registration (DruQuaR)

Academiejaar 2011-2012

Lipopeptides column comparison and dry heat stress of polymyxin B sulphate

Apr Matthias VAN LAETHEM

Master na Master in de Industrieumlle Farmacie

Promotor Prof Dr Apr B De Spiegeleer

i

ACKNOWLEDGEMENTS

Het tot stand brengen van een thesis is een boeiende opdracht maar is niet altijd

even vanzelfsprekend Het is dan ook niet mogelijk om dit te doen zonder hulp en steun

van mensen die mij als student omringd hebben

In de eerste plaats wil ik de heer Bart De Spiegeleer bedanken Hij maakte het

mogelijk om mijn kennis te verruimen door in contact te komen met allerlei technieken die

ik ervoor niet of enkel in theorie kende

Matthias DrsquoHondt bedankt voor de algemene begeleiding en het leveren van

constructieve commentaren en suggesties die mijn wetenschappelijke kritische houding

heeft aangescherpt Verder wil ik ook collegarsquos en personeel bedanken voor de

aangename werksfeer

Uiteraard wil ik ook vrienden en kennissen bedanken voor de ontspanning na soms

lange dagen in het labo Als laatste wil ik nog mijn vriendin Lore en mijn ouders bedanken

die de grootste steunpilaar waren voor het volbrengen van deze thesis hiervoor bedankt

ii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

TABLE OF CONTENTS ii

LIST OF ABBREVATIONS AND ACRONYMS v

1 INTRODUCTION 1

11 Peptides 1

111 Amino acids 1

112 Peptides 2

1121 Peptide bond 2

1122 Production of peptides 2

1123 Structure of peptides 3

1124 Peptides versus proteins 3

113 Therapeutic peptides 4

12 Lipopeptides 6

121 Lipopeptide anti-infectives 6

122 Lipopeptide vaccines 7

13 Polymyxin 7

131 History and structure 7

132 Mechanism of action 8

133 Toxicity 8

134 Commercial formulations 9

135 Polymyxin B 10

1351 Production of polymyxin B 11

14 Heat stress 11

141 Voluntary heat treatment 11

1411 Hot-melt extrusion 11

1412 Dry heat sterilization 13

142 Involuntary heat treatment 13

2 OBJECTIVES 14

3 MATERIALS AND METHODS 15

31 Materials 15

32 Lipopeptide clustering 15

iii

33 Column comparison 17

331 Column selection 17

332 Chromatography 18

333 Chromatographic response factors 19

34 Gradient optimization and method verification of polymyxin B sulphate analysis 20

341 Chromatography 21

3411 Scouting gradient and UPLC gradient optimization 21

3412 Final UPLC method 22

342 Chromatographic response factors 23

343 Method verification 25

3431 LoD and LoQ 25

3432 Linearity of analytical response 25

3433 Precision 25

3434 Carry-over 25

3435 Analytical stability 26

35 Dry heat stress kinetics of polymyxin B sulphate 26

351 Chromatography 26

352 Dry heat stress conditions 26

353 Quantitative dry heat stress experiments 27

3531 Calculation of degradation constants 27

3532 Calculation of Arrhenius parameters Ea and A 27

4 RESULTS AND DISCUSSION 28

41 Lipopeptide clustering 28

42 Column comparison 30

421 Chromatographic response factors 30

4211 Asymmetry factor 31

4212 Limit of detection 31

4213 Time-corrected resolution product 31

4214 Separation factor 32

4215 Peak-to-valley ratio 32

4216 Peak capacity 32

4217 Chromatographic response factor 33

422 Overall performance 33

43 Gradient optimization and method-verification of polymyxin B sulphate analysis 34

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Pharmaceutics and Biopharmaceutics 77 297-305

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

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Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

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Derringer G Suich R (1980) Simultaneous-optimization of several response variables

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DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

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Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

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Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

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Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

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51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

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tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

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Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

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Configurations of Polypeptide Chains PNAS 37 235-240

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antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 3: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

i

ACKNOWLEDGEMENTS

Het tot stand brengen van een thesis is een boeiende opdracht maar is niet altijd

even vanzelfsprekend Het is dan ook niet mogelijk om dit te doen zonder hulp en steun

van mensen die mij als student omringd hebben

In de eerste plaats wil ik de heer Bart De Spiegeleer bedanken Hij maakte het

mogelijk om mijn kennis te verruimen door in contact te komen met allerlei technieken die

ik ervoor niet of enkel in theorie kende

Matthias DrsquoHondt bedankt voor de algemene begeleiding en het leveren van

constructieve commentaren en suggesties die mijn wetenschappelijke kritische houding

heeft aangescherpt Verder wil ik ook collegarsquos en personeel bedanken voor de

aangename werksfeer

Uiteraard wil ik ook vrienden en kennissen bedanken voor de ontspanning na soms

lange dagen in het labo Als laatste wil ik nog mijn vriendin Lore en mijn ouders bedanken

die de grootste steunpilaar waren voor het volbrengen van deze thesis hiervoor bedankt

ii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

TABLE OF CONTENTS ii

LIST OF ABBREVATIONS AND ACRONYMS v

1 INTRODUCTION 1

11 Peptides 1

111 Amino acids 1

112 Peptides 2

1121 Peptide bond 2

1122 Production of peptides 2

1123 Structure of peptides 3

1124 Peptides versus proteins 3

113 Therapeutic peptides 4

12 Lipopeptides 6

121 Lipopeptide anti-infectives 6

122 Lipopeptide vaccines 7

13 Polymyxin 7

131 History and structure 7

132 Mechanism of action 8

133 Toxicity 8

134 Commercial formulations 9

135 Polymyxin B 10

1351 Production of polymyxin B 11

14 Heat stress 11

141 Voluntary heat treatment 11

1411 Hot-melt extrusion 11

1412 Dry heat sterilization 13

142 Involuntary heat treatment 13

2 OBJECTIVES 14

3 MATERIALS AND METHODS 15

31 Materials 15

32 Lipopeptide clustering 15

iii

33 Column comparison 17

331 Column selection 17

332 Chromatography 18

333 Chromatographic response factors 19

34 Gradient optimization and method verification of polymyxin B sulphate analysis 20

341 Chromatography 21

3411 Scouting gradient and UPLC gradient optimization 21

3412 Final UPLC method 22

342 Chromatographic response factors 23

343 Method verification 25

3431 LoD and LoQ 25

3432 Linearity of analytical response 25

3433 Precision 25

3434 Carry-over 25

3435 Analytical stability 26

35 Dry heat stress kinetics of polymyxin B sulphate 26

351 Chromatography 26

352 Dry heat stress conditions 26

353 Quantitative dry heat stress experiments 27

3531 Calculation of degradation constants 27

3532 Calculation of Arrhenius parameters Ea and A 27

4 RESULTS AND DISCUSSION 28

41 Lipopeptide clustering 28

42 Column comparison 30

421 Chromatographic response factors 30

4211 Asymmetry factor 31

4212 Limit of detection 31

4213 Time-corrected resolution product 31

4214 Separation factor 32

4215 Peak-to-valley ratio 32

4216 Peak capacity 32

4217 Chromatographic response factor 33

422 Overall performance 33

43 Gradient optimization and method-verification of polymyxin B sulphate analysis 34

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 4: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

ii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

TABLE OF CONTENTS ii

LIST OF ABBREVATIONS AND ACRONYMS v

1 INTRODUCTION 1

11 Peptides 1

111 Amino acids 1

112 Peptides 2

1121 Peptide bond 2

1122 Production of peptides 2

1123 Structure of peptides 3

1124 Peptides versus proteins 3

113 Therapeutic peptides 4

12 Lipopeptides 6

121 Lipopeptide anti-infectives 6

122 Lipopeptide vaccines 7

13 Polymyxin 7

131 History and structure 7

132 Mechanism of action 8

133 Toxicity 8

134 Commercial formulations 9

135 Polymyxin B 10

1351 Production of polymyxin B 11

14 Heat stress 11

141 Voluntary heat treatment 11

1411 Hot-melt extrusion 11

1412 Dry heat sterilization 13

142 Involuntary heat treatment 13

2 OBJECTIVES 14

3 MATERIALS AND METHODS 15

31 Materials 15

32 Lipopeptide clustering 15

iii

33 Column comparison 17

331 Column selection 17

332 Chromatography 18

333 Chromatographic response factors 19

34 Gradient optimization and method verification of polymyxin B sulphate analysis 20

341 Chromatography 21

3411 Scouting gradient and UPLC gradient optimization 21

3412 Final UPLC method 22

342 Chromatographic response factors 23

343 Method verification 25

3431 LoD and LoQ 25

3432 Linearity of analytical response 25

3433 Precision 25

3434 Carry-over 25

3435 Analytical stability 26

35 Dry heat stress kinetics of polymyxin B sulphate 26

351 Chromatography 26

352 Dry heat stress conditions 26

353 Quantitative dry heat stress experiments 27

3531 Calculation of degradation constants 27

3532 Calculation of Arrhenius parameters Ea and A 27

4 RESULTS AND DISCUSSION 28

41 Lipopeptide clustering 28

42 Column comparison 30

421 Chromatographic response factors 30

4211 Asymmetry factor 31

4212 Limit of detection 31

4213 Time-corrected resolution product 31

4214 Separation factor 32

4215 Peak-to-valley ratio 32

4216 Peak capacity 32

4217 Chromatographic response factor 33

422 Overall performance 33

43 Gradient optimization and method-verification of polymyxin B sulphate analysis 34

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

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Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

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extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 5: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

iii

33 Column comparison 17

331 Column selection 17

332 Chromatography 18

333 Chromatographic response factors 19

34 Gradient optimization and method verification of polymyxin B sulphate analysis 20

341 Chromatography 21

3411 Scouting gradient and UPLC gradient optimization 21

3412 Final UPLC method 22

342 Chromatographic response factors 23

343 Method verification 25

3431 LoD and LoQ 25

3432 Linearity of analytical response 25

3433 Precision 25

3434 Carry-over 25

3435 Analytical stability 26

35 Dry heat stress kinetics of polymyxin B sulphate 26

351 Chromatography 26

352 Dry heat stress conditions 26

353 Quantitative dry heat stress experiments 27

3531 Calculation of degradation constants 27

3532 Calculation of Arrhenius parameters Ea and A 27

4 RESULTS AND DISCUSSION 28

41 Lipopeptide clustering 28

42 Column comparison 30

421 Chromatographic response factors 30

4211 Asymmetry factor 31

4212 Limit of detection 31

4213 Time-corrected resolution product 31

4214 Separation factor 32

4215 Peak-to-valley ratio 32

4216 Peak capacity 32

4217 Chromatographic response factor 33

422 Overall performance 33

43 Gradient optimization and method-verification of polymyxin B sulphate analysis 34

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 6: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

iv

431 Gradient scouting 34

432 UPLC gradient optimization 35

433 Method verification 37

4331 LoDLoQ 37

4332 Linearity 38

4333 Precision 38

4334 Carry-over 38

4335 Analytical stability 38

44 Dry heat stress kinetics of polymyxin B sulphate 39

441 Calculation of degradation constants 39

442 Calculation of Arrhenius parameters 40

443 Related degradation products 42

444 Mass balance 45

445 Application in HME 46

5 Conclusions 47

6 References 49

7 Attachments 56

71 Dry heat stress kinetics of polymyxin B sulphate 56

711 Calculation of degradation constants 56

712 Calculation of Arrhenius parameters 57

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

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Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

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extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 7: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

v

LIST OF ABBREVATIONS AND ACRONYMS

A Frequency factor

ACN Acetonitrile

API Active pharmaceutical agent

As Symmetry factor

AU Absorbance units

CMS Colistin methanesulfonate

CI Confidence interval

CRF Chromatographic response factor

CSF Caspofungin

CV Column volume

D Desirability

Da Dalton

DNA Deoxyribonucleic acid

DPM Daptomycin

Ea Activation energy

EDQM European directorate for the quality of medecines

FA Fatty acid

GRM Gramicidin

HCA Hierarchical cluster analysis

HME Hot-melt extrusion

HPLC High performance liquid chromatography

IEC Ion exchange chromatography

Ile Isoleucine

k Degradation constant

L-Dab L-αγ-diaminobutyric acid

LC Liquid chromatography

Leu Leucine

LoD Limit of detection

LoQ Limit of quantification

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 8: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

vi

LogD Distribution coefficient

LogP Partition coefficient

LPS Lipopolysaccharide

Mr Relative molecular mass

MP Mobile phase

MS Mass spectrometry

PV Peak-to-valley ratio

P3CSS N-palmitoyl-56-dipalmitoyl-S-glycerylcysteinyl-seryl-serine

PC Peak capacity

PCA Principal component analysis

PDA Photodiode array

Ph Eur European Pharmacopoeia

Phe Phenylalanine

pI Isoelectric point

PMX Polymyxin

R Universal gas constant

R2 Coefficient of determination

RRF Relative response factor

RSD Residual standard deviation

Rep Repeat

Rs Resolution

Rs corr Time-corrected resolution product

RT max Retention time of the last peak

S Separation factor

SN Signal-to-noise ratio

SD Standard deviation

Sig Significance

SMILES Simplified molecular-input line-entry system

SPPS Solid-phase peptide synthesis

SPSS Statistical Package for the Social Sciences

T Temperature

Thr Threonine

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

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Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

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Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

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Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

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Derringer G Suich R (1980) Simultaneous-optimization of several response variables

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Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

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Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

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Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

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51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

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tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

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Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

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Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

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Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

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Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

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Configurations of Polypeptide Chains PNAS 37 235-240

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antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

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Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 9: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

vii

UHPLC Ultra high performance liquid chromatography

UV Ultraviolet

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 10: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

1

1 INTRODUCTION

11 Peptides

111 Amino acids

Amino acids are the basic chemical units or monomers of all peptides and

proteins They contain a general structure with a basic amino group an acidic carboxyl

group and a hydrogen atom attached to a central α-carbon atom (for the α-amino acids)

The different amino acids are distinguished by in the residual group (R) which occupy the

fourth position attached to the α-carbon (Jalkanen et al 2004)

Twenty naturally occurring amino acids are encoded by the genetic code and are

incorporated into peptides or proteins The amino acids that the human body cannot

synthesize by itself called essential amino acids must be obtained from the human diet

Those that can be synthesized in human body from metabolites are called nonessential

amino acids (Wu et al 2009)

The naturally occurring amino acids can be subdivided into different classes based

on the properties of the residual groups (1) amino acids with no (glycine) or aliphatic

(alanine valine leucine isoleucine) side chain (2) amino acids with OH- or S-containing

side chains (serine cysteine threonine methionine) (3) aromatic amino acids

(phenylalanine tyrosine tryptophan) (4) basic amino acids (histidine lysine arginine)

and (5) acidic amino acids and their amides (aspartic acid glutamic acid asparagine

glutamine) With the exception of glycine where the side chain is a hydrogen atom the

central carbon atom of amino acids is asymmetric (Matthews 2000)

Next to the naturally occurring amino acids other amino acids can be found in

proteins eg 4-hydroxyproline γ-carboxyglutamic acid L-ornithine These are not directly

encoded by DNA but formed by post-translational modification during protein synthesis

Note that all amino acids incorporated into human peptides and proteins have the L

enantiomeric form However using synthetic methods it has been possible to synthesize

proteins consisting of all D-amino acids Unlike human polypeptides amino acids in

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 11: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

2

microbial organisms may consist of the D enantiomeric form (Vermeer 1990 Curis et al

2005 Friedman 2010)

112 Peptides

1121 Peptide bond

Peptides are short polymers consisting of amino acids linked together trough

peptide bonds The first chemical formation of a peptide bond between two glycine

amino acids by Fischer amp Fourneau (1901) was considered to be the beginning of the

peptide chemistry (Kimmerlin amp Seebach 2004) In peptides the amino acids are linked

together by formation of an amide bond between the carboxyl group of one amino acid

and the amino group of another amino acid (dehydration) The linked amino acids are

referred to as amino acid residues The peptide end containing the free amino group is

called the N-terminus whereas the peptide end with the free carboxyl group is called the

C-terminus Conventionally the amino acid sequence of peptides is written beginning

from the N-terminus to the C-terminus (Degim amp Celebi 2007)

1122 Production of peptides

Solid-phase peptide synthesis (SPPS) is a peptide production technique originally

developed by Merrifield (1963) and was a major breakthrough in the peptide field

(Kimmerlin amp Seebach 2004) In SPPS the growing peptide chains are linked to an

insoluble polystyrene resin and amino acids are step-by-step attached to the peptide

chain Initially an amino-protected amino acid is covalently bounded to the resin Then

after a washing procedure the protecting group is removed revealing a free N-terminus

to which a new amino acid may be attached This process of deprotection washing and

coupling is repeated until the desired sequence is obtained Note that several amino acids

have reactive functional groups in their side chains which also have to be protected in

order to avoid unwanted reaction in the side chains The final step in SPPS is the cleavage

of the peptide chain from the resin concurrent with the cleavage of the protection groups

from the side chains Finally the newly synthesized peptide may be purified out of the

reactant solution using filtration and chromatographic methods (Merrifield 1963

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 12: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

3

Guzman et al 2007) Next to SPPS other synthesis and production methods are available

(Van Dorpe et al 2011)

1123 Structure of peptides

Most peptides and proteins exhibit higher levels of structural organization The

structure of polypeptides is distinguished at four distinct levels The sequence of the

amino acid residues is referred to as the primary structure (Degim amp Celebi 2007)

Pauling et al discovered in 1951 the presence of regular conformations in

polypeptide chains The two major secondary structure types α helix and β sheet are

formed spontaneously and stabilize the polypeptide chain In the α helix hydrogen bonds

are formed between adjacent amino acid residues whereas in the β sheet hydrogen

bonds are formed between adjacent chains Besides α helix and β sheet other

conformations are rarely formed like the 310 and π helix (Pauling et al 1951)

The tertiary structure refers to the folding of the chain into a three-dimensional

globular protein as a result of charge-charge interactions (ie salt bridges) internal

hydrogen bonds van der Waals interactions hydrophobic interactions and disulfide

bonds

The quaternary structure of proteins is the organization into specific multisubunit

structures consisting of several polypeptide chains These structures can be an association

of identical subunits ie homotypic multimers or an association of different subunits ie

heterotypic multimers For instance hemoglobin is an association of two identical α

chains and two identical β chains The stabilization of these multisubunit proteins occurs

through the same interactions that are described for the tertiary structure (Degim amp

Celebi 2007)

1124 Peptides versus proteins

The difference between peptides and proteins is not unambiguously described in

literature as no general definition of a protein vs peptide is described Generally proteins

are defined as compounds with particular characteristics such as a three-dimensional

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 13: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

4

structure crystallization properties and others (Honda et al 2008) On the other hand

peptides typically refer to an oligo- or polypeptide with fewer than 50 amino acids in

length or 5000 Da in weight Insulin which consists of 51 amino acids and having a

molecular weight of about 5800 Da is regarded as one of the smallest proteins

(Malavolta et al 2011) or a large peptide (Guzman et al 2007) However the distinction

between peptides and proteins based on this arbitrary borderline of 50 or fewer amino

acids is rather subjective

113 Therapeutic peptides

To date three major classes of compounds are used in the treatment of human

diseases ie small molecules large biomolecules like proteins and the intermediate

group of compounds like oligo- and polypeptides Currently the vast majority of

therapeutics developed are still small molecules (Bulet 2008) However peptides are

considered to be a new generation of therapeutics as they are key regulators of most

physiological processes (Malavolta et al 2011) and allow a wide chemical diversity (Van

Dorpe et al 2011)

Therapeutic peptides have several advantages over the classical small molecules

First of all they possess a superior selectivity for biological targets causing less side-

effects Secondly their ultimate degradation products ie individual amino acids

generally contain a significantly lower toxicity profile Furthermore they do not

accumulate in organs nor show drug-drug interactions to the same extent as the classic

small molecules (Bulet 2008) Compared to the larger proteins and antibodies peptides

show a higher degree of penetration in tissues related to their smaller size better

stability higher activity and lower immunogenicity (Vlieghe et al 2009)

However peptide-based drug discovery has been hampered in the past because of

the presence of several drawbacks They have a short half-life in plasma due to low

metabolic stability caused by proteolytic enzymes and rapid removal from plasma

(Vlieghe et al 2009) Therapeutic peptides have other drawbacks including a lack of oral

bioavailability the presence of potential immunogenic sequences possibly lower potency

compared to antibodies and a higher productionquality cost (Sato et al 2006)

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

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extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 14: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

5

In recent years technological improvements and the development of alternative

routes of administration have overcome some of these drawbacks resulting in a revival of

interest in peptides as potential drugs Currently the market for therapeutic peptides is

rapidly evolving hundreds of peptides are in clinical development and even more in

preclinical development (Vlieghe et al 2009) In Table 11 some examples of old and

newer marketed peptide derivatives are given

Table 11 Typical examples of marketed therapeutic peptides

Peptide Brand Length Indication Production

Buserelin acetate Suprefactreg 9 Prostate cancer Synthesis

Cyclosporine Neoralreg 11

Immunosuppressant drug used in

organ transplant and auto-

immune diseases

Fermentation

Desmopressin acetate

(vasopressin analogue) Minirin

reg 9 Diabetes insipidus Synthesis

Enfuvirtide Fuzeonreg 36 HIV-1 infection Synthesis

Exenatide Byettareg 39 Diabetes mellitus type 2 Synthesis

Glucagon Glucagenreg 29 Hypoglycemia Fermentation

Insulin Humulinreg 51 Diabetes mellitus Fermentation

Lanreotide acetate

(somatostatin analogue) Somatuline

reg 8 Acromegaly carcinoid syndrome Synthesis

Lisinopril Zestrilreg 3

Hypertension congestive heart

failure Synthesis

Oxytocin Syntocinonreg 9

Improvement of uterine

contractions haemorrhage

control

Synthesis

Polymyxin B Maxitrolreg 10 Bacterial infection Fermentation

Salmon calcitonin Miacalcicreg 32

Pagetrsquos disease postmenopausal

osteoporosis hypercalcaemia Synthesis

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 15: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

6

12 Lipopeptides

Lipopeptides are compounds consisting of a peptide linked to a lipidic part They

have promising properties as anti-infective agents and in vaccine therapy applications

The polymyxins originally discovered in 1947 were one of the first lipopeptides

described in literature (Balaji et al 2011)

121 Lipopeptide anti-infectives

In recent years antibiotic resistance and their consequences which were amplified

by excessive antibiotic use has become a major issue for public health welfare and

economy Consequently new antibiotics have to be developed in order to suppress this

increasing phenomenon (Grundmann et al 2011)

A number of lipopeptides produced in bacteria contain surfactant antibacterial or

antifungal properties These synthesized antimicrobial peptides are intensively modified

by peptide cyclization addition of fatty acid moieties incorporation of non-standard and

amino acids and incorporation of D-amino acids (Perron et al 2006)

Natural semi-synthetic and synthetic lipopeptides show increased antimicrobial

potency together with a low rate of microbial resistance (Jerala 2007) Therefore

lipopeptides seem to be a promising class of anti-infectives Most antimicrobial

lipopeptides are secondary metabolites purified from the fermentation broth which can

than be modified semi-synthetically (Pirri et al 2009)

The bacterial cell membrane is the major target of lipopeptides These amphiphilic

structures interact with the hydrophilic head and the fatty acyl chains of phospholipids

This insertion in the bacterial cell membrane leads to disrupture of the physical integrity

of the membrane resulting in leakage of cellular material and eventually cell death This

mechanism of action explains the low susceptibility towards antibiotic resistance because

the absence of a specific receptor site makes it difficult to develop resistance (Pirri et al

2009 Hancock amp Sahl 2006)

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 16: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

7

122 Lipopeptide vaccines

In the last decade peptide-based vaccines were introduced as new therapeutics

These synthetic peptides have potential advantages over traditional vaccines including

high safety high ability for encouraging immune responses and low cost of production

However the administration of these antigenic formulations resulted in weak

immunogenicity (BenMohamed et al 2002) Therefore powerful adjuvants are required

to enhance the cellular immunity Many adjuvants are used in biomedical investigation

but unfortunately only a few of them are non-toxic thus not suitable for human

application Therefore new non-toxic and potent adjuvants for human use are required

The covalent attachment of lipid moieties to peptide antigens has been

investigated as a method for formation of self-adjuvanting vaccines These lipopeptide

vaccines have promising properties as excellent adjuvant effects are achieved including

improvement of immunogenicity and a low degree of toxicity (Toth et al 2008)

13 Polymyxin

131 History and structure

Polymyxins are a class of antibiotics isolated for the first time in 1947 from Bacillus

polymyxa They became available for clinical use in the early 1960s Originally five

polymyxins were described polymyxin A B C D and E (colistin) Amongst them only

polymyxin B and colistin were used for clinical purposes (Kwa et al 2008) Although the

effectiveness of polymyxins was never discussed their systematic use was reduced in the

late 1970s and early 1980s because serious toxic effects were reported (Falagas et al

2006)

The structure of polymyxin B is given in Table 12 Structurally polymyxins are

lipopeptides consisting of a decapeptide and a hydrophobic fatty acid tail The

hydrophobic region is bound to a cyclic heptapeptide ring through a tripeptide side chain

The decapeptide exists of characteristic amino acid constituents such as L-αγ-

diaminobutyric acid (L-Dab) D-leucine and D-phenylalanine Especially of interest is the

presence of the L-Dab This non-standard amino acid determines the highly polar

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

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Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

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Derringer G Suich R (1980) Simultaneous-optimization of several response variables

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Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

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Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

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Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

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51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

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tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

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Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

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Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

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Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

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Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

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Configurations of Polypeptide Chains PNAS 37 235-240

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antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

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perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 17: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

8

character of the polymyxins as the free amino groups of L-Dab are positively charged at

physiological pH Polymyxin B and colistin have a similar structure They differ only in the

fatty acid moiety and in one amino acid of the cyclic ring polymyxin B contains the amino

acid D-phenylalanine while colistin contains D-leucine (Kwa et al 2007)

132 Mechanism of action

Polymyxins are cationic detergents that interact with the outer membrane of

various Gram-negative bacteria In particular they interact with the anionic phosphate

moieties of lipopolysaccharide (LPS) a structural component of Gram-negative bacteria

consisting of a polysaccharide a core oligosaccharide and lipid A (Falagas et al 2010)

The high affinity between lipid A and the fatty acid tail of the antibiotic results in

destabilization and disrupture of the membrane integrity by displacing Mg2+ and Ca2+

ions which are crucial for membrane integrity from LPS Consequently this bacterial

membrane disrupture not only leads to loss of cellular material but also to an increased

susceptibility to other antibiotics Therefore the use of synergistic combination therapy

is under investigation (Landman et al 2008 Balaji et al 2011)

Polymyxins are active against a variety of Gram-negative bacteria Furthermore

most important nosocomial pathogens are susceptible to polymyxins such as

Pseudomonas aeruginosa Escherichia coli Acinetobacter spp Klebsiella spp and

Enterobacter spp Nevertheless some species possess intrinsic resistance such as Proteus

spp Neisseiria spp and Providencia spp because of modifications in bacterial outer

membrane structure resulting in reduced binding Polymyxins do not possess activity

against Gram-positive or anaerobic bacteria (Landman et al 2008 Falagas et al 2010)

133 Toxicity

The exact mechanism of toxicity is not known In studies from several decades

ago severe adverse effects such as nephrotoxicity and neurotoxicity appeared to be

caused by the systemic administration of polymyxins Moreover life-threatening

reactions such as respiratory paralysis and acute renal failure with death as result were

attributed to the use of polymyxins (Landman et al 2008) With this in mind it was not

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 18: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

9

surprising that the use of polymyxins fell sharply when newer and less toxic antibiotics

were developed in the 1970s (Falagas et al 2006)

The emergence of multidrug-resistant bacteria such as Pseudomonas aeruginosa

and Acinetobacter baumanii together with the shortage of therapeutic antibiotic

innovations has led to renewed interest in polymyxins in the last decades (Molina et al

2009) For the sake of toxicity reasons systemic administration of polymyxins should be

seen as the drugs of last resort to treat patients with serious infections caused by

multidrug-resistant Gram-negative pathogens when other treatment options are no

longer available (Giuliani et al 2007)

However more recent data suggest that polymyxins have a better therapeutic

index than previously assumed Possible explanations are (1) the avoidance of co-

administration of other nephrotoxic or neurotoxic agents (2) dosage adjustment to

patients with renal dysfunction and monitoring of toxicity symptoms (3) enhancement in

supportive treatment and (4) decrease in recommended dosages In conclusion further

research is needed to better understand the pharmacology therapeutic use risk factors

and optimum dosing strategies of polymyxins in order to maximize efficacy and minimize

resistance formation and toxicity (Falagas amp Kasiakou 2006)

134 Commercial formulations

Commercially polymyxin B is available as polymyxin B sulphate and is used for the

topical treatment of cutaneous otic and superficial ocular infections Oral applications

are only an option for infections in the gastro-intestinal tract because polymyxins have a

poor bioavailability Furthermore parentally and intrathecally administrations for the

treatment of multi-drug resistant Gram-negative infections are also used in hospital

environment (Falagas amp Kasiakou 2006)

Colistin is available in two forms colistin sulphate and colistimethate sodium

(CMS) a pro-drug that is hydrolyzed in vivo to form the active component colistin Colistin

sulphate is administered topically for cutaneous infections or orally for bowel

decontamination (Falagas amp Kasiakou 2006) CMS is less potent but also less toxic than

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

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Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

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Derringer G Suich R (1980) Simultaneous-optimization of several response variables

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Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

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Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

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Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

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51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

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tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

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Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

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Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

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paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

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Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

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Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

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Configurations of Polypeptide Chains PNAS 37 235-240

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contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

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perspective Central European Journal of Biology 3 258-273

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54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

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Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

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Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

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55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

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and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 19: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

10

colistin sulphate Therefore CMS can be used in intramuscular intravenous and

aerosolized formulations Intravenous administration of CMS is mostly used for

nosocomial infections with multi-drug resistant Gram-negative bacteria especially P

aeruginosa and A baumanii (Balaji et al 2011) Adjunctive therapy for treatment of

persistent bronchopulmonary infections with aerosolized colistin show encouraging

results (Landman et al 2008)

135 Polymyxin B

Commercially polymyxin B sulphate is an antibiotic complex mixture consisting of

a variety of components The main components are polymyxin B1 B2 B3 and Ile-B1

Polymyxin B1 is by far the principal component Polymyxin Ile-B1 only differs from

polymyxin B1 in one amino acid in the cyclic ring Polymyxin B2 and B3 differ from

polymyxin B1 in the fatty acid moiety linked to the tripeptide side chain Additionally the

polymyxin complex contains several minor components such as polymyxin B4 B5 and B6

(Orwa et al 2002)

Furthermore in 2002 Govaerts et al characterized seven other impurities and

recently eight new impurities were identified (Van den Bossche et al 2011) However

the authors indicated that further characterization of the impurities is required in order

to determine the exact fatty acid and the amino acid configuration An overview of the

partial and fully characterized components of polymyxin B are given below in Table 12

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 20: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

11

Table 12 Chemical structure and mz of the major components of polymyxin B (Van den

Bossche et al 2011)1

Compound W X Y Z FA mz

B1 L-Thr L-Leu D-Phe L-Thr 6-methyloctanoyl 6024

I-B1 L-Thr L-Ile D-Phe L-Thr 6-methyloctanoyl 6024

B2 L-Thr L-Leu D-Phe L-Thr 6-methylheptanoyl 5954

B3 L-Thr L-Leu D-Phe L-Thr octanoyl 5954

1 FA fatty acyl group Ile isoleucine L-Dab L-αγ-diaminobutyric acid Leu leucine Phe phenylalanine Thr

threonine

1351 Production of polymyxin B

Polymyxin B is an antibiotic complex mixture obtained as a fermentation product

from various strains of Bacillus polymyxa and related species Without going into further

detail the procedures for recovery and purification of polymyxin B from the fermentation

broth are based on the application of ion exchange chromatography (IEC) adsorption to

eg activated carbon Subsequently the productis recovered using coagulation and

lyofilisation techniques (httpwwwfreepatentsonlinecomWO2010058427html)

14 Heat stress

141 Voluntary heat treatment

1411 Hot-melt extrusion

Hot-melt extrusion (HME) is a processing technology often used in the

pharmaceutical industry for the production of various drug delivery systems including

granules pellets sustained-release tablets implants transdermal and transmucosal

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 21: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

12

applications (Crowley et al 2007) These matrix formulations consist of one (or more)

drug(s) which is (are) homogeneously incorporated in a release-controlling molten

polymer under elevated temperature (Vervaet et al 2008)

The HME apparatus consists of a rotating screw inside a hollow barrel surrounded

by a temperature-controling heating system (Figure 11) The starting materials are

conveyed to the barrel through a feed hopper The heat required to melt the polymer is

supplied by the heat generated in the heating system and by friction resulting from shear

stress caused by the rotating screw This screw also homogenizes the drugs in the molten

polymer Finally this drug-polymer mixture is forced through a die (Breitenbach 2002)

Figure 11 Schematic diagram of a single screw extruder (Crowley et al 2007)

The formation of solid dispersions or solutions can increase drug solubility and

dissolution rate Therefore in the past the majority of HME processed Active

Pharmaceutical Agents (APIs) were class II drugs according to the biopharmaceutical

classification system eg itraconazole (Verreck et al 2003)

HME applications are relatively new to the pharmaceutical setting but has several

advantages compared to conventional techniques HME is a continuous process in which

all production steps are performed in one single apparatus This allows a high degree of

automation resulting in a high throughput production process Moreover no solvents or

water are required thus rendering cost and labor intensive drying steps obsolete

(Crowley et al 2007)

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

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Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

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Derringer G Suich R (1980) Simultaneous-optimization of several response variables

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Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

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Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

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Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

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51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

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tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

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Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

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Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

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Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

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Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

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Configurations of Polypeptide Chains PNAS 37 235-240

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antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

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perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 22: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

13

Potential disadvantages of HME are the influence of heat stress and shear stress

on the stability of drug and matrix However degradation can be reduced by optimizing

process parameters such as temperature and screw rotation speed or by selecting low

melting polymers and the addition of plasticizers Moreover extruder and screw design

can reduce shear forces and the process time This resulted in the successful processing of

thermolabile drugs eg hydrocortisone (Repka et al 1999)

1412 Dry heat sterilization

Dry heat sterilization is a one of the first sterilization methods developed in

pharmaceutical industry It involves the use of high temperature to destroy all micro-

organisms by coagulation of proteins Standard conditions for sterilization are at least two

hours at 160 degC or one hour at 170 degC In contrast with steam sterilization where

saturated steam is the carrier of thermal energy the heat transfer in dry heat sterilization

is accomplished by conduction Initially the heat permeates the surface layers of

materials and afterwards progressively enters the deeper layers Dry heat sterilization has

the advantages of being a non-corrosive method and a low operating cost Possible

drawbacks include prolonged exposure time uneven penetration and destruction of heat

labile materials (Tietjen et al 1992)

142 Involuntary heat treatment

During processing of pharmaceuticals drug substances are constantly exposed to

involuntary heat stress For instance the pressure during tablet compaction is partially

transformed into heat caused by friction between particles or friction between particles

and die Consequently this local temperature increase can result in alterations in tablet

structure or degradation of active drugs and excipients (Picker-Freyer amp Schmidt 2004)

Other typical processes where mechanical stress can give rise to involuntary heat

exposure are mixing milling and grinding (DrsquoHondt et al 2011)

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 23: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

14

2 OBJECTIVES

Lipopeptides are becoming an increasingly important class of peptides attracting

more and more pharmaceutical-biomedical attention However due to their specific

structure chromatographic analysis often requires the use of ldquospecialrdquo mobile phase

systems containing eg sodium sulphate (Orwa et al 2000 Mageshwaran et al 2012

Ph Eur 70) or trifluoroacetic acid (Deng et al 2010 Gikas et al 2009

Sivapathasekaran et al 2009 Wang et al 2010) These systems are not directly

compatible with mass spectrometry which is currently an essential technique in the

analytical field or can cause quantification problems due to ion suppression (Gustavsson

et al 2001) Therefore in a first part of this study the best LC-MS compatible system for

general lipopeptide analysis was investigated

To achieve this a selection of model lipopeptides was made from a list of 18

pharmaceutical-biomedically relevant lipopeptides by clustering techniques (HCA and

PCA) based on chemical descriptors Moreover four LC columns were selected based on

their pharmacopoeial and general use in lipopeptide analysis Using similar

chromatographic conditions differences in performance for the four columns were

evaluated and ranked using a Derringer desirability function combining 8 individual

chromatographic response factors

In a second part of this thesis a well characterized lipopeptide representative ie

polymyxin B sulphate was selected for the determination of the stability in voluntary

heat treatments such as hot-melt extrusion Currently peptide stability in dry state has

not been extensively been reported in the literature (DrsquoHondt et al 2011) First an

appropriate (U)HPLC column was selected for the analysis of stressed and unstressed

polymxin B sulphate and a general scouting gradient was optimized based on differences

in gradient composition Furthermore the final method was evaluated with a basic

method-verification Finally a kinetic profile was established using different short-term

dry heat stress conditions This profile revealed information about the heat-stability of

polymyxin B sulphate in dry state

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 24: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

15

3 MATERIALS AND METHODS

31 Materials

Polymyxin B sulphate was bought at Genaxxon BioScience (Ulm Germany)

Gramicidin A formic acid and anhydrous sodium sulphate were obtained from Sigma

Aldrich (Bornem Belgium) Cubicinreg (daptomycin - Novartis) and Cancidasreg (caspofungin ndash

MSD) were purchased from Care4Pharma (Schiphol Netherlands) Acetonitrile (LC-MS

grade) was acquired from Fisher Scientific (Aalst Belgium) Concentrated (85)

phosphoric acid was obtained form Fluka (Buchs Switserland) Potassium permanganate

and sodium borohydride were purchased form Merck Schuchardt OHG (Hohenbrunn

Germany) Water was purified using an Arium 611 purification system (Sartorius

Gottingen Germany) yielding ge 182 MΩcm quality water The YMC-Pack Pro C18 YMC-

Triart C18 HPLC ACE C18 (all 250 times 46 mm ID 5 microm particle size) and YMC-Triart C18

UHPLC (100 times 20 mm ID 19 microm particle size) columns were obtained from Achrom

(Machelen Belgium)

32 Lipopeptide clustering

Molecular structures obtained in SMILES format

(httppubchemncbinlmnihgov) of a selection of 18 lipopeptides (Table 31) based on

pharmaceutical-biomedical relevance were imported into MarvinSketch (version 5411

ChemAxon Ltd) thus obtaining a two-dimensional peptide structure

Three-dimensional structure optimization was performed using HyperChem

(version 80 Hypercube) The Polak-Ribiere conjugate gradient was used as termination

condition (Van Dorpe et al 2010 Belka et al 2012 Koba amp Baczek 2011) Using the 3-

D optimized structures 5 descriptors were calculated using MarvinSketch software (pI

and LogD at pH 20 55 74 and 100) 7 descriptors were calculated using HyperChem

software (Surface area (Approx) Surface area (Grid) Volume Hydration energy LogP

Refractivity and Polarizability) and 3224 descriptors were calculated in Dragon (version

50 Talete) thus obtaining 3236 descriptors in total

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 25: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

16

Constant descriptors ie identical value for all lipopeptides were eliminated thus

reducing the number of descriptors to 1440 Each descriptor data-set was then

transformed into a N(01) distribution using z-score normalization

In this equation x is the individual value of a data point is the mean and SD is

the standard deviation of the data-set

Table 31 Selection of 18 lipopeptides

Compound Formula Average Mr

1 Amphomycin C58H91N13O20 129042

2 Anidulafungin C58H73N7O17 114024

3 Arthrofactin C64H111N11O20 135463

4 Caspofungin C52H88N10O15 109331

5 Colistin A C53H100N16O13 116946

6 Colistin B C52H98N16O13 115543

7 Daptomycin C72H101N17O26 162067

8 Echinocandin B C52H81N7O16 106024

9 Gramicidin A1 C99H140N20O17 188229

10 Iturin A2 C47H72N12O14 104317

11 Micafungin C56H71N9O23S 127027

12 MX-2401 C67H101N15O22 146861

13 P3CSS C60H113N3O11S 108462

14 Plipastatin C72H110N12O20 146371

15 Polymyxin B1 C56H98N16O13 120348

16 Surfactin C53H93N7O13 103634

17 Syringomycin E C53H85ClN14O17 122578

18 Telavancin C80H106Cl2N11O27P 175564

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 26: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

17

Lipopeptide clustering was performed using HCA analysis with SPSS software (SPSS

19 IBM) and PCA using SIMCA-P+ software (version 120 Umetrics) HCA is used for

pattern recognition based on similarities between objects according to the Euclidean

distance between them and the result is visualized in a dendrogram Starting from the

individual components branches are build up to form clusters The length of the branches

are inversely related to their similarity thus short branches mean high similarity PCA is a

multivariate tool for the visualisation and interpretation of large data sets Based on

commercial availability a lipopeptide representative of the obtained clusters was used for

further column comparison

33 Column comparison

331 Column selection

Four different stationary phases were selected based on their pharmacopoeial

and general use in lipopeptide analysis for evaluation of the lipopeptide separation The

YMC-Pack Pro C18 HPLC column was selected based on the work of Orwa et al (2000)

where this column chemistry performed the best in chromatographic separation of

polymyxin B sulphate The second and third column ie YMC-Triart C18 have comparable

hydrophobicity as the YMC-Pack Pro C18 column but have 20 lower hydrogen bonding

capacity due to a multi-stage endcapping procedure of the residual silanol groups

(httpwwwymcde) This stationary chemistry was obtained both in HPLC and UHPLC

compatible format of which the latter due to lower particle size (19 microm) has the

additional benefit of its ultra-fast analysis time The last column ie the ACE C18 was

selected based on a column comparison which indicated better peak shape and column

efficiency when compared to the YMC-Pack Pro column for basic compounds

(httpwwwmz-atdepdface_comparison_guidepdf) An overview of the column

characteristics of the selected chromatographic columns as given by the suppliers is

given in Table 32

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 27: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

18

Table 32 Column characteristics of the 4 selected (U)HPLC columns

Characterisation parameter

YMC-Pack Pro ACE YMC-Triart

(HPLC)

YMC-Triart (UHPLC)

HPLCUHPLC HPLC HPLC HPLC UHPLC

Column length 250 mm 250 mm 250 mm 100 mm

Dead volume 2125 ml 1968 ml 2082 ml 0219 ml

Internal diameter

46 mm 46 mm 46 mm 20 mm

Particle size 5 microm 5 microm 5 microm 19 microm

Pore size 120 Aring 100 Aring 120 Aring 120 Aring

Surface area 340 m2g-1 300 m2g-1 360 m2g-1 -

Carbon load 16 155 20 20

pH stability 20 ndash 80 20 ndash 80 10 ndash 120 10 ndash 120

End-capping ++ + +++ +++

Metal content Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm) Low (lt 10 ppm)

332 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Mobile phases

consisted of 01 formic acid in water (A) and 01 formic acid in acetonitrile (B) A

general linear gradient was implemented running from 10 B to 90 B in 25 column

volumes followed by returning to the initial conditions and re-equilibration A 10 mgml

caspofungin solution was prepared in 5050 H2O ACN solvent containing 01 formic

acid The same solvent was used to prepare a 01 mgml solution of polymyxin B sulphate

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 28: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

19

gramicidin A and daptomycin Column temperature was set at 40degC (plusmn 5degC) and sample

compartment at 5degC (plusmn 3degC) The injection volume for HPLC and UPLC analysis was set at

20 microl and 2 microl respectively UV detection was performed at 215 nm

333 Chromatographic response factors

The lipopeptide chromatographic characteristics were quantified into eight

different response factors containing both single and multiple responses and are given in

Table 33 (Van Dorpe et al 2010 Ph Eur 70 2246)

Table 33 Selected chromatographic response factors and formulas

Response factor Formula 1

1 Asymmetry factor (As)

4

2 Limit of detection (LoD)

(microgml)

4

3 Time-corrected resolution

product (Rs corr)

1

4 Separation factor (S)

3

5 Peak-to-valley ratio (PV) 2

1

6 Peak capacity (PC)

1

7 Chromatographic response

function (CRF)

1

1 number of responses obtained per column

2 calculated for polymyxin B sulphate and

gramicidin A

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

tR retention time of the peak corresponding to

the component

n number of components

t0 column dead time

RT max t0-corrected tR of the last peak expressed

in column volume

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

a 1

b 1

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 29: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

20

As per column four lipopeptides were analyzed 3 to 4 separate responses were

obtained per column for certain single factors ie As LoD and S These separate

individual values as well as the multiple responses were re-expressed as a dimensionless

desirability scale (d) using two linear desirability functions depending whether the

desired chromatographic response is minimal eg LoD or maximal eg peak-to-valley

ratio For the single responses the mean value for the different lipopeptides was

calculated as column d-value The geometric mean of aforementioned separate d-values

was calculated finally into in one lsquoaveragersquo single D-value response per column in order to

assess the overall performance of each column thereby appointing equal weights to each

of the 8 response factors (Derringer amp Suich 1980)

di minimized

maximized

Desirability function

d = desirability value

D = geometric mean of the desirability values

Yi = experimental response value

Ymin = minimal response value within the experimental data set

Ymax = maximum response value within the experimental data set

x = number of response parameters

34 Gradient optimization and method verification of polymyxin B

sulphate analysis

In the second part of this thesis polymyxin B sulphate a well characterized

lipopeptide representative was selected for the determination of its stability under dry

heat stress conditions For this objective a stability-indicating assay method was

developed

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 30: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

21

341 Chromatography

The UPLC apparatus consisted of a Waters Acquity H UPLC Class Quaternary

Solvent Manager a Waters Acquity Sample Manager combined with a Flow Through

Needle and a Waters Acquity Ultra Performance LC PDA detector with Empower 2

software for data acquisition The HPLC apparatus consisted of a Waters Alliance 2695

separations module and a Waters 2487 dual wavelength absorbance UV detector with

Empower 2 software for data acquisition (all Waters Milford MA USA) Column

temperature was set at 30degC (plusmn 5degC) and sample compartment at 10degC (plusmn 5degC) The

injection volume for HPLC and UPLC analysis was set at 20 microl and 2 microl respectively The

flow rate for HPLC and UPLC analysis was set at 10 mlmin and 05 mlmin respectively

UV detection was performed at 215 nm

3411 Scouting gradient and UPLC gradient optimization

Mobile phases consisted of sodium sulphate 446 gl water pH adjusted to 23

using dilute phosphoric acid (A) and acetonitrile (B) (Ph Eur 70 p 2753-2754) For the

scouting gradient a general linear gradient was implemented running from 10 to 90 B

in 25 column volumes as the polarity range of the degradation products in the dry heat

stressed samples are unknown (Snyder et al 2001 Dolan 2007) For the further gradient

optimization a gradient was performed running from 15 to 50 acetonitrile with

adapted alterations in gradient slope Furthermore the alterations from the steepest to

the flattest slope were started at different time points corresponding to different solvent

strengths The gradient compositions used during optimization are depicted in Figure 31

Both scouting and optimization gradient compositions were followed by returning to the

initial conditions (5 column volumes) and re-equilibration (10 column volumes)

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the original

slope of the gradient scouting (731min) in order to enhance selectivity However in

the central part of the gradient still no efficient separation was obtained for the gradient

run with the flattest slope of 244min In an attempt to enhance selectivity the slope in

this central part was decreased to 1min This 1 slope was started at different solvent

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 31: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

22

strenghts (which in practice is equivalent to after 45 min 4 min 35 min and 3 min)

and stopped at 35 acetonitrile

Figure 31 Gradient compositions used during optimization on the YMC-Triart UHPLC column

Both unstressed and extreme stressed (180degC 30 min) samples were prepared

These samples were considered to be model samples for method development An

appropriate amount of polymyxin B sulphate was weighed and transferred into HPLC

glass vials A Stuart SBH200D Digital Block Heater was used for stressing the polymyxin B

sulphate sample A 10 mgml polymyxin B sulphate solution was prepared in 9010 H2O

ACN solvent containing 01 formic acid Subsequently the obtained solution was

sonicated and filtered (045 microm) before UPLC injection

3412 Final UPLC method

Additionally to the former gradient compositions an isocratic step is introduced at

the begin of the run Mobile phase A consisted of 90 volumes of a buffer (sodium

sulphate 446 gl water pH adjusted to 23 using dilute phosphoric acid) and 10 volumes

of acetonitrile while mobile phase B consisted of 10 volumes of the buffer and 90

volumes of acetonitrile The final gradient composition is represented below in Table 34

15

50

15

50

15

50

15

25

35

50

15

23

35

50

15

22

35

50

15

21

35

50

15

20

25

30

35

40

45

50

0 5 10 15 20

ACN

Time (min)

731min = Sc Gr

366min

244min

45

4

35

3

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

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Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

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54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

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Therapeutics 14 333-340

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impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

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Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

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Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

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55

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International Journal of Pharmaceutics 251 165-174

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European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 32: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

23

Table 34 Final gradient composition on the YMC-Triart UHPLC column

Retention time (min)

ACN MP A MP B Comments

000 15 938 62 Isocratic

075 15 938 62 Gradient

(244min)

401 223 846 154 Gradient

(1min)

1671 35 688 312 Gradient

(244min)

2341 50 500 500 Isocratic

(3 CV)

2472 50 500 500 Switch to intitial

composition (2 CV)

2560 15 938 938 Re-equilibration

(10 CV)

3000 15 938 938 Restart

MP mobile phase

CV column volume

342 Chromatographic response factors

The column characterization parameters were quantified into seven different

response factors containing both single and multiple responses and are given in Table

35 (Van Dorpe et al 2010 Ph Eur 70 2246)

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 33: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

24

Table 35 Selected chromatographic response factors and applied formulas

Response factor Formula

1 Asymmetry factor (As)

2 Limit of detection (LoD) (microgml)

3 Peak capacity (PC)

4 Peak-to-valley ratio (PV) 2

5 Number of peaks above RT -

6 Total analysis time (T) (min) -

7 Required solvent (S) (ml) -

w005 peak width at one-twentieth of the peak

height

wh width of the peak at half-height

d distance between the perpendicular dropped

from the peak maximum and the leading edge of

the peak at one-twentieth of the peak height

H height of the peak

h range of the noise

n number of components

RT reporting threshold of degradants here

defined as 1 relative the unstressed polymyxin

B1 peak area

Hp height above the extrapolated baseline of the

minor peak

Hv height above the extrapolated baseline of the

lowest point of the curve separating the minor

and major peaks

tg defined gradient run time expressed in column

volume

The asymmetry factor and limit of detection were calculated using the polymyxin

B1 peak of the unstressed polymyxin sample Peak capacity was calculated from the

major polymyxin components ie polymyxin B1 B2 B3 and I-B1 of the unstressed

sample The peak-to-valley ratio and the number of peaks above reporting threshold

were calculated on the stressed polymyxin B sulphate sample ie 30 min at 180degC

Acceptance limit values were assigned to three response factors ie As LoD and

T Acceptable values for As vary between 08 and 15 The LoD should be lower than the

reporting threshold ie 1 relative to the peak area of unstressed polymyxin B1

Maximal total run time was set at 30 minutes

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 34: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

25

343 Method verification

3431 LoD and LoQ

Signal-to-noise ratio was calculated using the polymyxin B1 peak of the

unstressed polymyxin sample The reporting threshold was set at 1 relative to the

unstressed polymyxin B1 peak area (10 mgml) thus LoD and LoQ should be less than

10 microgml

3432 Linearity of analytical response

A series of analyte concentrations corresponding to 1 10 80 90 100

110 and 120 of a 100 mgml polymyxin B sulphate solution were subjected to linear

regression analysis The HPLC vials contents of the 80 till 120 samples were obtained

at the desired concentration by dissolving the contents in solvent solution The 01

mgml (10) solutions are obtained by dilution of the 100 mgml solutions The 001

mgml solutions (1) are obtained by dilution of the 01 mgml (10) solutions Three

independent replicates were prepared for the 1 10 80 100 and 120 samples

The 90 and 110 samples were done in singular

3433 Precision

The precision was determined as the relative standard deviation (RSD) of the

peak area for three independent replicates at three different concentrations ie 80

100 and 120 of a 100 mgml polymyxin B sulphate solution

3434 Carry-over

The carry-over was determined in accordance to the limit defined by EDQM

(PAPHOMCL (11) 04 Annex 1) the percentage of the peak area corresponding to

polymyxin B1 in the blank injection does not exceed 005 of the peak area of polymyxin

B1 in the chromatogram obtained with the reference solution (100 mgml)

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

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Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

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Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

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Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

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Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

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55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

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International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

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Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

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European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 35: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

26

3435 Analytical stability

For the determination of the analytical stability equimolar quantities of a

reductive agent (NaBH4) and an oxidative agent (KMnO4) were added to HPLC vials

containing 1 mg of polymyxin B sulphate Samples were incubated for 12hrs at 10degC and

protected from light The analytical stability was measured as the relative response

factor (RRF) relative to unstressed polymyxin B1

35 Dry heat stress kinetics of polymyxin B sulphate

351 Chromatography

UPLC apparatus column temperature flow rate (sect341) sample preparation

(sect3411) mobile phase and gradient composition (sect3412) are described above

352 Dry heat stress conditions

The stress conditions used in this study were derived from pilot experiments As

the objective is to quantify the polymyxin B sulphate degradation kinetics minimal and

maximal amounts of polymyxin degradation in relevant stressed samples were set to

10 and 90 respectively in order to obtain pharmaceutically relevant degradation

profiles

Table 36 Dry heat stress conditions

Time point

Temperature (degC)

160 170 180 190

1 30 min 20 min 15 min 10 min

2 60 min 40 min 30 min 20 min

3 90 min 60 min 45 min 30 min

4 120 min 80 min 60 min 40 min

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 36: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

27

353 Quantitative dry heat stress experiments

3531 Calculation of degradation constants

Using linear regression analysis degradation constants (k) are determined for the

different temperatures (160 170 180 and 190degC) The overall degradation kinetic order

ie first- or second-order is examined using the correlation coefficients of the

regression analysis

First-order rate equation

Second-order rate equation

C = area of the polymyxin peak after exposure to dry heat stress

C0 = area of the polymyxin peak of the unstressed sample

k = degradation constant (first-order min-1

- second-order M-1

min-1

)

t = time (min)

3532 Calculation of Arrhenius parameters Ea and A

The calculated degradation constants (k) together with the corresponding

temperatures are subjected to the Arrhenius equation Out of this equation the

activation energy (Ea) and frequency factor (A) are determined

Arrhenius equation

k = degradation constant (min-1

)

A = frequency factor (min-1

)

Ea = activation energy (Jmol)

R = universal gas constant (8314 J K-1

mol-1

)

T = temperature (K)

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

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Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

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806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

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and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

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Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

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Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

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Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

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extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 37: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

28

4 RESULTS AND DISCUSSION

41 Lipopeptide clustering

The results of the HCA ie the resulting dendrogram and PCA visualized by

means of score plots are shown in Figures 41 and 42

Figure 41 HCA dendrogram for the 18 selected lipopeptides using average linkage between groups

From the PCA score plot (PCA1-PCA2) and HCA dendrogram it can be seen that

three major lipopeptide clusters are formed (clusters 1 2 and 3) as well as the presence

of four structural deviant lipopeptides (micafungin P3CSS gramicidin A and telavancin)

Based on commercial availability lipopeptide representatives ie polymyxin B sulphate

caspofungin daptomycin and gramicidin A were obtained for clusters 1 2 3 and 6

respectively thus representing the three major lipopeptide clusters as well as one

structurally different cluster

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 38: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

29

Figure 42 PCA score plot (PCA1-PCA2) for the 18 lipopeptides

The quality of the PCA model is described in Figure 43 by R2 and Q2 values R2 is

defined as the proportion of variance in the data which is explained by the model thus

indicates goodness of fit Q2 is defined as the proportion of variance in the data

predictable by the model thus indicates goodness of prediction

Figure 43 R

2 and Q

2 of the first four principle components

043

060

072

079

026 030

041 046

000

010

020

030

040

050

060

070

080

PC1 PC2 PC3 PC4

Pe

rce

nta

ge (

)

R2 (cum)

Q2 (cum)

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

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Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

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806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

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and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

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paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

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Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

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Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

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Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

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Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

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extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

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Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

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European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 39: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

30

A Q2 above of 05 is considered to be a good model However for the purpose of

this study predictive ability is less meaningful The cumulative values of the first four

principle components explained approximately 80 of the structural variability (R2) of

the 18 selected lipopeptides

42 Column comparison

421 Chromatographic response factors

The chromatographic responses together with their calculated desirability values

and overall desirability value are presented in Table 41

Table 41 Chromatographic response values calculated desirability values (d) and

overall desirability (D)

Parameter ACE C18 YMC-Pack Pro

C18

YMC-Triart

C18 HPLC

YMC-Triart

C18 UHPLC

As

Response 2929 1579 2835 2926

di 0227 0951 0323 0240

LoD Response 6895 9958

5976 0172

di 0260 0242 0234 0998

Rs corr Response 10614 180694 9834 9542

di 0011 0905 0007 0005

S

Response 1823 2537 1853 1616

di 0263 0768 0305 0153

PVGRM

Response 1818 2500 2083 3400

di 0086 0411 0212 0838

PVPMX

Response 2643 1000 1000 1000

di 0861 0059 0059 0059

PC Response 6740 10993 6146 28509

di 0047 0211 0024 0890

CRF Response 5152 10616 5406 5742

di 0073 0849 0109 0157

D Overall 0152 0446 0119 0221

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 40: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

31

4211 Asymmetry factor

The calculated average lipopeptide asymmetry factor for the selected columns

showed large variability (average 60 RSD) which is as expected as the four lipopeptide

compounds were selected based on structural diversity resulting in different

interactions with the stationary phase The best results ie closest to 1 were obtained

with the YMC-Pack Pro C18 column

4212 Limit of detection

The limit of detection is the smallest amount of substance that is accurately

detectable having a SN ratio of 3 As the formula stipulates the signal (or the height of

the peak) which can be correlated to the lsquosharpnessrsquo of the peak as well as the amount

of noise determine the LoD value The average noise value of the three HPLC columns is

calculated to be 2607times10-3 AU (863 RSD) whereas the noise value of the UPLC

column was calculated to be 0057times10-3 AU The LoD obtained using the UPLC column is

approximately 45 fold lower than the average LoD obtained from the three HPLC

columns Therefore the lower LoD value can be attributed to the UPLC technology

reducing the noise level rather than to the new column chemistry

The large LoD variability within one column (average 75 RSD) is caused by the

difference in the obtained lipopeptide signal ie peak height As the quantification

wavelength is set at 215 nm the signal is derived from the peptide bond Seen as the

structurally diverse lipopeptide set contains different amounts of peptide bonds this

variability is as to be expected

4213 Time-corrected resolution product

The resolution (Rs) between 2 peaks takes the individual retention time and the

width at half peak height into account UPLC analysis generally results in sharper peaks

ie smaller peak width at half maximum However peaks are also much faster eluted

reducing the time between the elution of two peaks These two factors cancel each

other out resulting in the largest resolution to be seen with the YMC-Pack Pro HPLC

column

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

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the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

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Therapeutics 14 333-340

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impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

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Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

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55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

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Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

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European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 41: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

32

The three individual resolution values obtained for each column are

recalculated into the time-corrected resolution product (Rs corr) which also takes the

column dead volume corrected retention time (expressed in column volume) of the last

eluting lipopeptide into account This TR max was similar for all columns ie 1884 Vc

(586 RSD)

4214 Separation factor

Calculation of the separation factor S only takes the column dead volume

corrected TR of the eluting components into account The YMC-Pack Pro column

performs the best The average separation factors of the other three columns showed

high similarity as was also noticed for Rs corr parameter

4215 Peak-to-valley ratio

The peak-to-valley ratio can be calculated for gramicidin and polymyxin as both

lipopeptides contain structurally related components For polymyxin B sulphate this is a

demethylation product ie polymyxin B2 and B3 For gramicidin A this is gramicidin C in

which a tryptophan amino acid (gramicidin A) is replaced by a tyrosine amino acid

(gramicidin C) Not all columns are able to separate these component couples under

similar operational conditions in which case a value of 1 is assigned ie peak and value

height are the same The ACE column shows best performance as it is able to separate

both polymyxin and gramicidin from their respective related compounds All other

columns were unable to separate the polymyxin lipopeptide mixture thus have a PV

ratio of 1

4216 Peak capacity

Peak capacity is determined by the total gradient run time and by the individual

peak widths at half maximum The total gradient run time (expressed in column

volumes) is equal to 25 for all columns Therefore the peak capacity as calculated here

can be correlated with the individual peak widths at half maximum The YMC-Triart

UPLC column performed as best due to the fact that sharper peaks ie smaller peak at

half maximum are obtained using UPLC based analysis

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 42: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

33

4217 Chromatographic response factor

The chromatographic response factor takes into calculation the three resolution

results obtained for each column and the retention time of the last eluting peak The

YMC-Pack Pro column showed the highest CRF value which is expected as the column

was also characterized by the highest resolution values The other three columns show a

comparable CRF value which is statistically significant lower than the YMC-Pack Pro CRF

value

422 Overall performance

As can be seen from the calculated D-value the YMC-Pack Pro C18 column

performed overall best under similar operational conditions in the chromatographic

separation of commercially available lipopeptides followed by the YMC-Triart C18

UHPLC column The performance of the ACE C18 and YMC-Triart C18 HPLC columns was

found to be rather similar (Table 42)

Table 42 Overall desirability of the different columns under similar operational

conditions

Rank Column D

1 YMC-Pack Pro 0403

2 YMC-Triart (UHPLC) 0183

3 ACE 0120

4 YMC-Triart (HPLC) 0091

The overall desirability value for chromatographic separation of polymyxin B

sulphate under ldquonormalrdquo similar operational conditions eg no modifications of

pressure or changing of injection volume was calculated as the geometric mean of

three polymyxin specific desirability values ie PMX As PMX LoD and PVPMX Other

parameters were discarded seen as they contain experimental input obtained from

analysis of other lipopeptides From this it was found that intrinsically the ACE column

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 43: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

34

chemistry is best suited for analysis of polymyxin compounds (D-value 0785) However

the YMC-Triart UHPLC column has the advantage of a much faster analysis time

allowing further gradient optimization when compared to the HPLC columns Therefore

the D-value of the UHPLC column increases on the desirability ranking

43 Gradient optimization and method-verification of polymyxin

B sulphate analysis

431 Gradient scouting

Initially a gradient scouting run was performed on a selection of different

chromatographic columns using a full-range acetonitrile (mobile phase B) gradient of 10

to 90 No peaks above the reporting threshold (1 relative to unstressed polymyxin B1

peak area) were eluted after the gradient composition reached 50 ACN Moreover no

peaks eluted prior to 15 ACN Therefore the gradient was adjusted to run from 15 to

50 ACN using the same steepness thus reducing total analysis time The column

characterisation parameters of this adjusted gradient scouting run for the different

columns are presented in Table 43

Table 43 Chromatographic properties of the adjusted gradient scouting run of

polymyxin B sulphate for the different chromatographic columns

As SN PC PV

PMX B1

PV

PMX B2 P T (min) S (ml)

ACE 1041 4796 5967 2947 2753 30 5105 5105

YMC-Pack

Pro 1173 6535 5651 1985 1765 26 5512 5512

YMC-Triart

(HPLC) 1429 4185 50431 NA2 2159 26 5400 5400

YMC-Triart

(UHPLC) 2083 39861 52281 NA2 2077 26 1136 568

1 Peak capacity is calculated for the PMX B1 B2 and I-B1 because wh cannot be calculated for PMX B3

because there is no sufficient separation between PMX B2 and B3 2 Uncertainty of peak assigned

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

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55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

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International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

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Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

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European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

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04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 44: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

35

The ACE column has the highest number of peaks above reporting treshold the

best As PC and PV values and is therefore the most appropriate for the further

optimization of polymyxin B sulphate This was also seen in the calculated D-values

specific for polymyxin B sulphate However as a large number of stressed samples will

be generated during dry heat stress experiments the total analysis time was limited to

30 min For this a steeper gradient composition would be required with a

corresponding decrease of selectivity as result Therefore gradient optimization of all

HPLC columns was not performed The UHPLC column was selected for the further

gradient optimization of polymyxin B sulphate despite that this gradient scouting

doesnrsquot show the best separation efficiency However there is still room for

improvement because the total analysis time (plusmn 11 min) is far below the specified limit

of 30 minutes

432 UPLC gradient optimization

The first step in the gradient optimization of the UHPLC column was the

adjustment of the gradient to half (366min) and to a third (244min) of the

original slope of the gradient scouting (731min) in order to enhance selectivity

However in the central part consisting of the major polymyxin compounds still no

efficient separation was obtained for the gradient run with the flattest slope of

244min In an attempt to enhance selectivity the slope in this central part was

decreased to 1min This 1 slope was started at different solvent strenghts (which in

practice is equivalent to after 45 min 4 min 35 min and 3 min) and stopped at 35

ACN whilst keeping the parts before and after this central part of the gradient at

244min

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

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Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

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806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

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Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

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paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

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Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

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Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

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Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

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Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

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Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

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European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 45: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

36

Table 44 Chromatographic properties of the gradient compositions of polymyxin B

sulphate for the different chromatographic columns

As SN PC PV

PMX B1 PV

PMX B2 P

T (min)

S (ml)

731min - - - - - 26 1136 568

366min - - - - - 26 1615 808

244min - - - - - 27 2094 1047

45rsquo 1438 21871 4470 NA1 NA1 33 2772 1386

4rsquo 1250 16941 3556 1284 1223 37 2884 1442

35rsquo 1250 15778 3545 1333 1250 37 2940 1470

3rsquo 1270 12222 3549 1243 1164 37 2995 1498

1 Uncertainty of peak assigned

In Table 44 the chromatographic response factors of the different gradient

compositions are given In Figure 43 optimal cut-off times are predicted for SN As PV

PMX B1 and PV PMX B2 Based on the number of peaks the cut-off at 45rsquo shows

inferior separation when compared to the other cut-off points The signal-to-noise ratio

increases when the run time before cut-off increases This is logical because the gradient

slope remains higher for a longer time resulting in more compressed peaks However

the model of the SN ratio shows a bending point between cut-off at 35 and 4 min

indicating some kind of robustness of the method between these cut-off times The

prediction of the asymmetry factor reached an optimal value closest to 1 ie perfect

symmetry at the cut-off times between 35 and 4 min The peak-to-valley ratios of PMX

B1 and PMX2 reached a maximal value around the cut-off of 35 min

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 46: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

37

Figure 44 Chromatographic properties of polymyxin B sulphate on the UHPLC column after different cut-

off (after 3 35 4 and 45 min)

Based on the model of the asymmetry factor the cut-off time reached an optimal

value after 38 min The peak-to-valley ratios of polymyxin B1 and B2 were predicted to

reach a maximum after a cut-off time at 36 min Furthermore the optimal cut-off time

for the SN model indicative for the most robust method was at the bending point of the

model ie after 37 min Equal weight was attributed to the parameters As SN and PV

PMX B1 and B2 Therefore the overall chromatographic properties were predicted to

reach an optimum after the cut-off time at 37 min

433 Method verification

4331 LoDLoQ

The limit of detection (LoD) was calculated to be 0018 relative to a 100 mgml

(100) polymyxin concentration The limit of quantification (LoQ) was calculated to be

y = 0224x3 - 2312x2 + 7876x - 7598

1200

1250

1300

1350

1400

1450

3 35 4 45

As

y = 82133x3 - 91026x2 + 338008x - 404327

10000

12000

14000

16000

18000

20000

22000

3 35 4 45

SN

y = -0278x2 + 1987x - 2216

1220

1240

1260

1280

1300

1320

1340

3 35 4

PV PMX B1

y = -0226x2 + 1641x - 1725

1140

1160

1180

1200

1220

1240

1260

3 35 4

PV PMX B2

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

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Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

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1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

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Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

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Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 47: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

38

0062 Consequently the LoD and LoQ are lower than the reporting threshold of 1

relative to unstressed polymyxin B1

4332 Linearity

The linearity results for the major polymyxin B sulphate components are

summarized in Table 45 The analytical procedure has a suitable level of linearity over the

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis shows a good correlation coefficient

Table 45 Summary of linear regression data for the major polymyxin components

Polymyxin R2 F Sig Regression line (95 CI)

PMX B1 09944 2654 lt 10-3 Y = 1131102 (plusmn 46796) X + 3533 (plusmn 38136)

PMX B2 09944 2645 lt 10-3 Y = 219440 (plusmn 9094) X + 600 (plusmn 7411)

PMX B3 09952 3104 lt 10-3 Y = 40520 (plusmn 1550) X + 478 (plusmn 1263)

PMX I-B1 09945 2726 lt 10-3 Y = 148767 (plusmn 6073) X + 2 (plusmn 4949)

4333 Precision

The calculated residual standard deviation for polymyxin B1 was 112 502 and

491 for respectively the 80 100 and 120 solutions respectively thus the total

RSD varies between 0 and 5 per cent

4334 Carry-over

The carry-over does not exceed 005 of the peak area of polymyxin B1 obtained

with the reference solution (100 mgml) and therefore is in accordance to the limit

defined by EDQM (PAPHOMCL (11) 04 Annex 1)

4335 Analytical stability

In the presence of the reductive agent NaBH4 for 12hrs at 10degC no degradation is

seen for polymyxin B sulphate peak in aqueous solution Moreover the peak area

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 48: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

39

percentage of polymyxin B1 is 443 higher than the average peak area of unstressed

polymyxin However the peak area is not significantly higher because the RSD varies

between 0 and 5 In the presence of the oxidative agent KMnO4 for 12hrs the peak area

is 638 lower than the average peak area of unstressed polymyxin B1 indicating that

there are stability problems in a KMnO4 or oxidative environment

44 Dry heat stress kinetics of polymyxin B sulphate

441 Calculation of degradation constants

The experimental data assuming first-order kinetics of the quantitative dry heat

experiments for polymyxin B1 is given in Table 46

Table 46 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (first-order regression)

k (min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -00048 -00088 to -00009 08331 150 00305

Rep2 -00089 -00166 to -00011 09239 243 00388

pooled -00058 00090 to -00026 07210 181 00038

Temperature (degC) 170

Rep1 -00110 -00160 to -00060 09427 493 00059

Rep2 -00111 -00200 to -00022 08408 158 00284

pooled -00111 -00146 to -00075 08688 530 00001

Temperature (degC) 180

Rep1 -00221 -00342 to -00099 09175 334 00103

Rep2 -00255 -00364 to -00146 09489 558 00050

pooled -00238 -00294 to -00182 09226 953 00000

Temperature (degC) 190

Rep1 -00485 -00657 to -00313 09642 807 00029

Rep2 -00491 -00731 to -00252 09344 428 00073

pooled -00488 -00585 to -00392 09446 1365 00000

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 49: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

40

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 47 The degradation constants (k) at different temperatures

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 47 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (first-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (min-1) -00050 -00109 -00233 -00482

R2 05825 08622 09202 09446

PMX B3

k (min-1) -00044 -00085 -00178 -00358

R2 04068 07139 08258 08764

PMX I-B1

k (min-1) -00036 -00081 -00179 -00377

R2 04698 08252 09027 09447

442 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 45 the Arrhenius plot is presented for polymyxin B1

assuming first-order degradation

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

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oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 50: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

41

Figure 45 Arrhenius plot for polymyxin B1 (first-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

In Table 48 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1022 - 1369) and frequency factor (CI 1381E+10 - 1523E+14) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Until now only first-order kinetics was investigated However second-order

degradation has to be verified as well The experimental data and calculations for the

second-order rate equation and Arrhenius parameters is given in Attachment 71 When

comparing first-order with second-order degradation kinetics the Arrhenius regression of

the first-order kinetics is characterized by a larger correlation coefficient compared to

second-order assumption This is also reflected in the 95 confidence intervals of the

calculated Arrhenius parameters Ea and A In conclusion the results show that

degradation of polymyxin B sulphate during dry heat stress follow first-order kinetics

y = -14380x + 28003 Rsup2 = 09977

-7000

-6000

-5000

-4000

-3000

-2000

00022 00022 00023 00023 00024 ln

k (

min

-1)

1T (K-1)

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 51: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

42

Table 48 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (first-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1196 1022 - 1369

09977 A (min-1) 1450E+12 1381E+10 - 1523E+14

PMX B2

Ea (kJmol) 1256 1222 - 1290

09999

A (min-1) 7065E+12 2851E+12 - 1751E+13

PMX B3

Ea (kJmol) 1159 1043 - 1275

09989

A (min-1) 4117E+11 1837E+10 - 9227E+12

PMX I-B1

Ea (kJmol) 1308 1286 - 1329

1000

A (min-1) 2115E+13 1185E+13 - 3774E+13

443 Related degradation products

The reporting threshold for polymyxin impurities and degradation products in

unstressed and stressed samples was set at 1 relative to the unstressed polymyxin B1

peak area The peaks observed in the degradation profile of stressed samples can be

classified into 3 groups

Group 1 Starting materialimpurities

Peaks present in unstressed samples and peak area decreases

during dry heat stress eg polymyxins B1 (peak 24) B2 (peak 11) B3

(peak 13) and I-B1 (peak 19) in Figure 46 and 47

Group 2 Impuritiesdegradants

Peaks present in unstressed samples and peak area increases during

dry heat stress eg peaks 1 and 2 in Figure 46 and 47

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 52: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

43

Group 3 Degradants

Typical degradation peaks that are not present in unstressed

samples but are formed during dry heat stress eg peaks 4 and 39

in Figure 46 and 47

The components of group 1 and 2 are the least interesting for this purpose

because they are not the ldquotypicalrdquo degradation products because they are already found

in unstressed samples However still a lot of degradant peaks are formed out of the

components of group 1 and 2 thus the identity of these components is crucial for a

better understanding and clarification of degradation processes

Group 3 is the most interesting group because it contains typical degradation

products that are not found in literature (Orwa et al 2001 Govaerts et al 2002 Van

den Bossche et al 2011) Particularly of interest are the components characterized by a

significant different chromatographic behaviour as the original polymyxin compounds

This is because the degradation products of the central part in the gradient are likely to

be very structurally related to the major polymyxin compounds

Alltogether 39 components above reporting threshold were observed in stressed

samples 21 of the 39 components found in stressed samples were also found in

polymyxin unstressed thus group 1 and 2 together contains 21 components

Consequently group 3 comprises 18 typical degradation products In Figure 46 and

Figure 47 representative chromatograms are given of polymyxin B sulphate of stressed

and unstressed samples respectively

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 53: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

44

Figure 46 Chromatogram of polymyxin B sulphate stressed (180degC 30 min Rep 2) Alltogether 39 peaks

above reporting threshold (1 relative to the polymyxin B1 peak area) were observed

Figure 47 Chromatogram of polymyxin B sulphate unstressed (Rep 3a) Alltogether 21 peaks

corresponding to degradation products above reporting threshold (1 relative to the polymyxin B1 peak

area) observed in stressed samples were also found in polymyxin unstressed

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 54: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

45

444 Mass balance

In Table 410 the mass balance assuring all peaks have identical response factors

is given for the different temperatures and time intervals Generally the mass balance

decreases with increasing dry heat stress conditions

Table 410 Mass balance1 ()

Time point2

Temperature (degC)

160 170 180 190

1 9435 9975 9361 8796

2 9017 9119 8844 7780

3 9415 9744 7911 7381

4 10488 8903 7037 5337

1 Mass balance calculated as sum stressed polymyxin peak areas sum unstressed polymyxin peak areas x 100

2 See Table 36

The loss of mass balance can be explained by the formation of degradation

products with a lower relative response factor (RRF) than the original polymyxin

compounds Indeed absorption at a wavelength of 215 nm reflects the presence of

peptide bonds thus the loss of peptide bonds ie loss of one or more amino acids in the

cyclic ring of polymyxin B as a result of increasing stress conditions is a plausible

explanation for the reduction in mass balance Furthermore next to modifications in RRF

weighing losses as a result of increasing dry heat temperatures could also explain the

decrease in mass balance However in unpublished data the weight loss of a classic small

molecule ie β-artemether at the most extreme conditions was only 84 (SD 44)

thus deficiency of mass balance in polymyxin B sulphate is unlikely to be caused by

weighing losses alone Finally the formation of degradation products which remain on

the UHPLC column could also be an explanation for the decrease in mass balance

Probably a combination of aforementioned hypotheses is most likely to explain the

reduction in mass balance

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 55: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

46

445 Application in HME

Based on research in literature process parameters for hot-melt extrusion were

investigated ie for residence time and temperature (Kumar et al 2008 Almeida et al

2011 Bialleck et al 2011 Maniruzzaman et al 2012 Liu et al 2012) Mean residence

times vary between 40 sec and 5 min and temperatures vary between 100 and 160degC

Therefore the residence time of materials in the extruder for 5 min at a temperature of

160degC could be defined as extreme HME conditions

Hence the amount of degradation was determined for polymyxin B sulphate

Using the first-order rate equation degradation of polymyxin B1 was predicted to be less

than 3 at a temperature of 160degC and residence time of 5 min and even less than 05

at 140degC for 10 min Therefore because of its stability at this process conditions of HME

polymyxin B sulphate is a candidate for HME application

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 56: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

47

5 Conclusions

A set of 18 different lipopeptides of which the majority have direct antibacterial

or antifungal clinical applications were classified into 7 major clusters using hierarchical

cluster analysis (HCA) and principal component analysis (PCA) Based on commercial

availability representatives for 4 of the 7 clusters were purchased ie polymyxin B

sulphate caspofungin daptomycin and gramicidin A thus which representing the

structural diversity of the currently commercially available lipopeptides

The chromatographic separation using a formic acid containing water

acetonitrile gradient of these four lipopeptides representatives was examined on four

different (U)HPLC columns using a combination of single (As LoD peak to valley ratio

separation factor) and multiple (time corrected resolution product peak capacity and

chromatographic response factor) response parameters The overall column performance

were compared using a linear desirability function The YMC-Pack Pro C18 column was

characterized with the highest overall D-value thus is the most appropriate column for

the mass spectrometry-compatible chromatographic separation of commercially available

lipopeptides

In the second part of this study the YMC-Triart UHPLC column was used for the

gradient optimization of polymyxin B sulphate because all other columns exceeded the

predefined maximal total analysis time of 30 min The optimization was achieved by

reducing the slope of the original scouting gradient run For the major polymyxin

compounds still no efficient separation was obtained and therefore the slope in the

central compartment consisting of the major compounds was decreased The

chromatographic properties of polymyxin B sulphate were influenced by the time point

where the gradient changes from the steepest to the flattest slope This cut-off reached a

maximum after 37 min of the steepest gradient

Next the final method was evaluated for LoD LoQ linearity carry-over and

precision LoD and LoQ were lower than the specified limit ie 1 relative to unstressed

polymyxin B1 The analytical procedure had a suitable level of linearity over the

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 57: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

48

concentration range between 1 and 120 of the 100 mgml polymyxin B sulphate

solution because the linear regression analysis showed a good correlation coefficient of

099 No carry-over was found for amounts higher than the LoD The variability of the

results varied up to 5 This overall error was mainly caused by weighing errors rather

than other errors

Finally a kinetic profile of polymyxin B sulphate in dry state was established using

different heat stress conditions The results obtained show that degradation of polymyxin

B sulphate during dry heat stress follow first-order kinetics The degradation constants

and Arrhenius parameters of the major polymyxin compounds were comparable

indicating that they have similar degradation mechanisms The activation energy (1193

kJmol) and frequency factor (1450E+12 min-1) of polymyxin B1 ie the major compound

of polymyxin B sulphate were considered to be high enough for application in hot-melt

extrusion

The peaks observed in the degradation profile of stressed samples were classified

into 3 groups starting material impuritiesdegradants and typical degradation products

Group 1 and 2 are crucial for a better understanding and clarification of degradation

processes Group 3 contains the typical degradation products that are not found in

previous work Particularly of interest are the components characterized by a significant

different chromatographic behaviour as the original polymyxin compounds

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 58: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

49

6 References

Almeida A Possemiers S Boone MN et al (2011) Ethylene vinyl acetate as matrix for

oral sustained release dosage forms produced via hot-melt extrusion European Journal of

Pharmaceutics and Biopharmaceutics 77 297-305

Balaji V Jeremiah SS Baliga PR (2011) Polymyxins Antimicrobial susceptibility

concerns and therapeutic options Indian Journal of Medical Microbiology 29 230-242

Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of

antitumor activity of novel benzensulfonamide derivatives based on their

physicochemical properties Letters in Drug Design amp Discovery 9 288-294

BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday

today and tomorrow Lancet Infectious Diseases 2 425-431

Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion

European Journal of Pharmaceutics and Biopharmaceutics 79 440-448

Breitenbach J (2002) Melt extrusion from process to drug delivery technology European

Journal of Pharmaceutics and Biopharmaceutics 54 107-117

Bulet P Stocklin R Menin L (2004) Anti-microbial peptides from invertebrates to

vertebrates Immunological Reviews 198 169-184

Crowley MM Zhang F (2007) Pharmaceutical Applications of Hot-Melt Extrusion Part I

Drug Development and Industrial Pharmacy 33 909-926

Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals

Amino Acids 29 177-205

Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current

Pharmaceutical Design 13 99-117

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 59: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

50

Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl

phthalate produced by Paenibacillus polymyxa Journal of Microbiological Methods 85

175-182

Derringer G Suich R (1980) Simultaneous-optimization of several response variables

Journal of Quality Technology 12 214-219

DrsquoHondt M Demareacute W Van Dorpe S et al (2011) Dry heat stress stability evaluation

of casein peptide mixture Food Chemistry 128 114-122

Dolan JW (2007) The perfect method part 7 the gradient shortcut LCGC Europe 21

Falagas ME Kasiakou SK (2006) Toxicity of polymyxins a systematic review of the

evidence from old and recent studies Critical Care 10

Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains

susceptible only to polymyxin among patients with Pseudomonas aeruginosa bacteremia

Antimicrobial Agents and Chemotherapy 50 2541-2543

Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms

frequency and treatment options Drug Resistance Updates 13 132-138

Fischer E Fourneau E (1901) Ueber einige Derivate des Glykocolls Ber Dtsch Chem

Ges 34 2868-2879

Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids

Chemistry amp Biodiversity 7 1491-1530

Gikas E Bazoti FN Fanourgiakis P et al (2009) Development and validation of a

UPLC-UV method for the determination of daptomycin in rabbit plasma Biomedical

Chromatography 24 522-527

Giuliani A Pirri G Nicoletto SF (2007) Antimicrobial peptides an overview of a

promising class of therapeutics Central European Journal of Biology 2 1-33

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 60: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

51

Govaerts C Orwa JA Van Schepdael A et al (2002) Characterization of polypeptide

antibiotics of the polymyxin series by liquid chromatography electrospray ionization ion

trap tandem mass spectrometry Journal of Peptide Science 7 45-55

Govaerts C Orwa JA Van Schepdael A et al (2002) Liquid chromatography-ion trap

tandem mass spectrometry for the characterization of polypeptide antibiotics of the

colistin series in commercial samples Journal of Chromatography A 976 65-78

Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance

of antibiotic resistance Drug Resistance Updates 14 79-87

Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in

liquid chromatography ndash electrospray ionization mass spectrometry using volatile ion-

pairing reagents Journal of Chromatography A 937 41-47

Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic

Electronic Journal of Biotechnology 10 279-314

Hancock R Sahl HG (2006) Antimicrobial and host-defense peptides as new anti-

infective therapeutic strategies Nature Biotechnology 24 1551-1557

Honda S Akiba T Kato YS et al (2008) Crystal Structure of a Ten-Amino Acid Protein

Journal of the American Chemical Society 130 15327-15331

Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building

blocks for proteins comparative theoretical and spectroscopic studies Journal of

Molecular Structure (Theochem) 675 61-77

Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on

Investigational Drugs 16 1159-1169

Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for

peptide and protein synthesis with applications to β-peptide assemblies Journal of

peptide Research 65 229-260

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 61: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

52

Koba M Baczek T (2011) Physicochemical interaction of antitumor acridinone

derivatives with DNA in view of QSAR studies Medicinal Chemistry Research 20 1385-

1393

Kumar A Ganjyal GM Jones DD et al (2008) Modeling residence time distribution in

a twin screw extruder as a series of ideal steady-state flow reactors Journal of Food

Engineering 84 441-448

Kwa A Kosiakou SK Tam VH et al (2007) Polymyxin B similarities to and differences

from colistin (polymyxin E) Expert Review of anti-infective Therapy 5 811-821

Kwa A Tam VH Falagas ME (2008) Polymyxins A Review of the Current Status

Including Recent Developments Annals Acadamy of Medicine Singapore 37 870-883

Landman D Georgescu C Martin DA et al (2008) Polymyxins revisited Clinical

Microbiology Reviews 21 449-465

Liu X Lu M Guo Z et al (2012) Improving the chemical stability of amorphous solid

dispersion with cocrystal technique by hot melt extrusion Pharmaceutical Research 29

806-817

Mageshwaran V Walia S Annapurna K (2012) Isolation and partial characterization of

antibacterial lipopeptide produced by Paenibacillus polymyxa HKA-15 against

phytopathogen Xanthomonas campestris pv Phaseoli M-5 World Journal of Microbiology

and Biotechnology 28 909-917

Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central

nervous system disorders Neuropeptides 45 309-316

Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking of

paracetamol by hot-melt extrusion an in vitro and in vivo evaluation European Journal of

Pharmaceutics and Biopharmaceutics 80 443-442

Mathews CK Van Holde KE Ahern KG (2005) Biochemistry Third Edition

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 62: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

53

Merrifield RB (1963) Solid phase peptide synthesis I The synthesis of a tetrapeptide J

Am Chem Soc 85 2149-2154

Molina J Cordero E Pachon J (2009) New information about the polymyxincolistin

class of antibiotics Expert Opinion on Pharmacotherapy 10 2811-2828

Orwa JA Busson R Roets E et al (2001) Isolation and structural characterization of

polymyxin B components Journal of Chromatography A 912 369-373

Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1

E-1 and E-2 in aqueous solution using liquid chromatography and mass spectrometry

Journal of Pharmaceutical and Biomedical Analysis 29 203-212

Orwa JA Van Gerven A Roets E et al (2000) Liquid chromatography of polymyxin B

sulphate Journal of Chromatography A 870 237-243

Pauling L Corey RB (1951) Atomic Coordinates and Structure Factors for Two Helical

Configurations of Polypeptide Chains PNAS 37 235-240

Perron GG Zasloff M Bell G (2006) Experimental evolution of resistance to an

antimicrobial peptide Proceedings of the Royal Society B 273 251-256

Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting

contribute to tablet quality Journal of Thermal Analysis and Calorimetry 77 531-539

Pirri G Giuliani A Nicoletto SF et al (2009) Lipopeptides as anti-infectives a practical

perspective Central European Journal of Biology 3 258-273

Repka MA Battu SK Upadhye SB et al (2007) Pharmaceutical Applications of Hot-

Melt Extrusion Part II Drug Development and Industrial Pharmacy 33 1043-1057

Repka MA Gerding TG Repka SL et al (1999) Influence of plasticizers and drugs on

the physical-mechanical properties of hydroxypropylcellulose films prepared by hot melt

extrusion Drug Development and Industrial Pharmacy 25 625-633

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 63: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

54

Sato AK Viswanathan M Kent RB et al (2006) Therapeutic peptides technological

advances driving peptides into development Current Opinion in Biotechnology 17 638-

642

Sivapathasekaran C Mukherje S Samanta R et al (2009) High-performance liquid

chromatography purification of biosurfactant isoforms produced by a marine bacterium

Analytical and Bioanalytical Chemistry 395 845-854

Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid

chromatography I Theoretical basis for reversed-phase systems Journal of

Chromatography A 165 3-30

Tietjen L Cronin W Mcintosh N (1992) Infection Prevention for Family Planning

Service Programs A Problem-Solving Reference Manual Dallas Essential Medical

Information Systems

Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-

Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and

Therapeutics 14 333-340

Van den Bossche L Van Schepdael A Chopra S et al (2011) Identification of

impurities in polymyxin B and colistin bulk sample using liquid chromatography coupled

to mass spectrometry Talanta 83 1521-1529

Van Dorpe S Verbeken M Wynendaele E (2011) Purity profiling of peptide drugs

Journal of Bioanalysis and Biomedecine 86

Van Dorpe S Vergote V Pezeshki A et al (2010) Hydrophilic interaction LC of

peptides Columns comparison and clustering Journal of Separation Science 33 728-739

Vermeer C (1990) γ-Carboxylate-containing proteins and the vitamin K-dependent

carboxylase Biochemical Journal 266 625-636

55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

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55

Verreck G Six K Van den Mooter G et al (2003) Characterization of solid dispersions

of itraconazole and hydroxypropylmethylcellulose prepared by melt extrusion ndash part I

International Journal of Pharmaceutics 251 165-174

Vervaet C Verhoeven E Quinten T et al (2008) Hot-melt extrusion and injection

moulding as manufacturing tools for controlled release formulations Dosis 24 119-123

Vlieghe P Lisowski V Martinez J et al (2010) Synthetic therapeutic peptides science

and market Drug Discovery Today 15 40-56

Wang Y Lu Z Bie X et al (2010) Separation and extraction of antimicrobial

lipopeptides produced by bacillus amyloliquefaciens ES-2 with macroporous resin

European Food Research and Technology 231 189-196

Wu G Bazer FW Davis TA et al (2009) Arginine metabolism and nutrition in growth

health and disease Amino acids 37 153-168

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Chromatographic separation techniques

04200920246 p 70 ndash 77

European Pharmacopoeia 70 European Directorate for the quality of Medicines amp

Healthcare Strassbourg France 2011 Polymyxin B sulphate 0120080203 p 2753 ndash

2754

httppubchemncbinlmnihgov

httpwwwedqmeumediasfichiersUPDATED_Annex_1_Qualification_of_HPLC_Equip

mentpdf

httpwwwmz-atdepdface_comparison_guidepdf

httpwwwymcdeymceuropeproductsanalyticalLCanalyticalColumnsYMC-Triart-

C18_19htm

httpwwwfreepatentsonlinecomWO2010058427html

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 65: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

56

7 Attachments

71 Dry heat stress kinetics of polymyxin B sulphate

711 Calculation of degradation constants

The experimental data assuming second-order kinetics of the quantitative dry

heat experiments for polymyxin B1 is given in Table 71

Table 71 Experimental data of the quantitative dry heat stress experiments for

polymyxin B1 (second-order regression)

k (M-1 min-1) 95 CI Rsup2 F Sig

Temperature (degC) 160

Rep1 -6438E-09 -1069E-08 to -2189E-09 08857 233 00170

Rep2 -1123E-08 -1761E-08 to -4858E-09 09664 575 00170

pooled -7612E-09 -1103E-08 to -4193E-09 07984 277 00012

Temperature (degC) 170

Rep1 -1694E-08 -2139E-08 to -1249E-08 09800 1467 00012

Rep2 -1566E-08 -2453E-08 to 6786E-09 09132 316 00112

pooled -1630E-08 -1957E-08 to -1302E-08 09428 1319 00000

Temperature (degC) 180

Rep1 -4517E-08 -5682E-08 to -3352E-08 09807 1523 00011

Rep2 -5339E-08 -7066E-08 to -3612E-08 09699 968 00022

pooled -4928E-08 -5685E-08 to -4172E-08 09658 2257 00000

Temperature (degC) 190

Rep1 -1544E-07 -2389E-07 to -6991E-08 09185 338 00101

Rep2 -1375E-07 -1768E-07 to -9810E-08 09763 1235 00016

pooled -1460E-07 -1761E-07 to -1158E-07 09398 1249 00000

The experimental data of the quantitative dry heat experiments for polymyxin B2

B3 and I-B1 is given in Table 72 The degradation constants (k) at different temperatures

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 66: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

57

are comparable between all major polymyxin components ie polymyxin B1 B2 B3 and I-

B1 which indicates that the major components have a similar degradation mechanism

Table 72 Experimental pooled data of the quantitative dry heat stress experiments for

polymyxin B2 B3 and I-B1 (second-order regression)

Temperature (degC) 160 170 180 190

PMX B2

k (M-1 min-1) -3327E-08 -8269E-08 -2459E-07 -7371E-07

R2 06278 09381 09660 09322

PMX B3

k (M-1 min-1) -1699E-07 -3546E-07 -9464E-07 -2339E-06

R2 04223 07886 08891 09323

PMX I-B1

k (M-1 min-1) -3205E-08 -7942E-08 -2279E-07 -6395E-07

R2 04996 08960 09404 09381

712 Calculation of Arrhenius parameters

The calculated degradation constants (k) together with the corresponding

temperatures are inputted in the Arrhenius equation to determine activation energy (Ea)

and frequency factor (A) In Figure 71 the Arrhenius plot is presented for polymyxin B1

assuming second-order degradation

Figure 71 Arrhenius plot for polymyxin B1 (second-order regression) The error bars indicate the 95

confidence interval of the natural logarithm of the degradation constants (k)

y = -19960x + 27269 Rsup2 = 09901

-20000

-19000

-18000

-17000

-16000

-15000

00022 00022 00023 00023 00024

ln k

(M

-1 m

in-1

)

1T (K-1)

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15

Page 67: FACULTEIT FARMACEUTISCHE WETENSCHAPPENlib.ugent.be/fulltxt/RUG01/001/894/730/RUG01-001894730_2012_0001... · Master na Master in de Industriële Farmacie Promotor Prof Dr. Apr. B.

58

In Table 73 the calculated activation energy and frequency factor are given for

the major polymyxins ie polymyxin B1 B2 B3 and I-B1 The calculated Arrhenius

parameters for polymyxin B2 B3 and I-B1 do not significantly differ from the parameters

calculated for polymyxin B1 because the 95 confidence intervals for activation energy

(CI 1155 ndash 2165) and frequency factor (CI 8927E+05 ndash 5432E+17) of polymyxin B1

includes the average values of the calculated Arrhenius parameters for polymyxin B2 B3

and I-B1 Supplementary to the comparable degradation constants at different

temperatures this indicates that the major components have a similar degradation

mechanism

Table 73 Calculation of the Arrhenius parameters Ea and A for polymyxin B1 B2 B3 and

I-B1 (second-order regression)

Compound Parameter Value 95 CI R2

PMX B1

Ea (kJmol) 1660 1155 - 2165

09901 A (min-1) 6963E+11 8927E+05 - 5432E+17

PMX B2

Ea (kJmol) 1729 1399 - 2060

09961

A (min-1) 2210E+13 3099E+09 - 1576E+17

PMX B3

Ea (kJmol) 1474 1148 - 1801

09947

A (min-1) 9436E+10 1456E+07 - 6114E+14

PMX I-B1

Ea (kJmol) 1672 1414 - 1930

09974

A (min-1) 4414E+12 4316E+09 - 4516E+15