FACULTEIT FARMACEUTISCHE...
Transcript of FACULTEIT FARMACEUTISCHE...
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
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
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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
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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
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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
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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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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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Grundmann H Klugman KP Walsh T et al (2011) A framework for global surveillance
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Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in
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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
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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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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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and Biotechnology 28 909-917
Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central
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Maniruzzaman M Boateng JS Bonnefille M et al (2012) Taste masking 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
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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
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European Food Research and Technology 231 189-196
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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
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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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
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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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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 Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms
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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
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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
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
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
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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of
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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
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
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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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
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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
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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 Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms
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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-
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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
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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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
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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
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Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals
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Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current
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Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl
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Friedman M (2010) Origin Microbiology Nutrition and Pharmacology of D-Amino Acids
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51
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Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in
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Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic
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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building
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Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on
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Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for
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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
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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
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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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Picker-Freyer KM Schmidt AG (2004) Does temperature increase induced by tableting
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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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Toth I Simerska P Fujita Y (2008) Recent Advances in Design and Synthesis of Self-
Adjuvanting Lipopeptide Vaccines International Journal of Peptide Research and
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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
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
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
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Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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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
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
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
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
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
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
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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
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
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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Bialleck S Rein H (2011) Preparation of starch-based pellets by hot-melt-extrusion
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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
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Curis E Nicolis I Moinard C et al (2005) Almost all about citrulline in mammals
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Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current
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Deng Y Lu Z Lu F et al (2010) Identification of LI-F type antibiotics and di-n-butyl
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51
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Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in
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Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic
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Jalkanen KJ Elstner M Suhai S (2004) Amino acids and small peptides as building
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Jerala R (2007) Synthetic lipopeptides a novel class of anti-infectives Expert Opinion on
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Kimmerlin T Seebach D (2005) lsquo100 years of peptide synthesisrsquo ligation methods for
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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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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
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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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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
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
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
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
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
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
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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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
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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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175-182
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 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
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
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
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
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
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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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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 Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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Falagas ME Rafailidis PI Matthaou DK (2010) Resistance to polymyxins Mechanisms
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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
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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
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
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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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
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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
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
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
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
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
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
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
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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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
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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
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
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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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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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Orwa JA Govaerts C Gevers K et al (2002) Study of the stability of polymyxins B-1
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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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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
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
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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
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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BenMohamed L Wechsler SL Nesburn AB (2002) Lipopeptide vaccines ndash yesterday
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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
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Degim IT Ccedilelebi N (2007) Controlled Delivery of Peptides and Proteins Current
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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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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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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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Gustavsson SA Samskog J Markides K et al (2001) Studies of signal suppression in
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pairing reagents Journal of Chromatography A 937 41-47
Guzman F Barberis S Illanes A (2007) Peptide synthesis chemical or enzymatic
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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
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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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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
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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
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
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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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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
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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
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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
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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
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
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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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
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
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Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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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
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1393
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
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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
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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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and Biotechnology 28 909-917
Malavolta L Cabral FR (2011) Peptides Important tools for the treatment of central
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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
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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
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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
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
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
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European Food Research and Technology 231 189-196
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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
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
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
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
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Falagas ME Koletski PK Kopterides P et al (2006) Risk factors for isolation of strains
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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
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
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
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
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
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
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Belka M Konieczna L Kawczak P et al (2012) The chemometric evaluation of
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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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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
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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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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
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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
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 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
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
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
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
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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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
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Breitenbach J (2002) Melt extrusion from process to drug delivery technology European
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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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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
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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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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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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
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
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
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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
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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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
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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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Snyder LR Dolan JW Gant JR (2001) Gradient elution in high performance liquid
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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
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
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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
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
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
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Breitenbach J (2002) Melt extrusion from process to drug delivery technology European
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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 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
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
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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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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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