qsar study of immunotoxicity in antibiotics alison bartlett

444 Pages·2013·24.56 MB·English

QSAR STUDY OF IMMUNOTOXICITY IN ANTIBIOTICS
ALISON BARTLETT
A thesis submitted in partial fulfilment of the requirements of
Liverpool John Moores University for the degree of Doctor of Philosophy
November 1995
THE FOLLOWING PUBLICATIONS,
FIGURES, PLATES AND TABLES HAVE
BEEN EXCLUDED ON INSTRUCTION
FROM THE UNIVERSITY.
APPENDIX 8
FIG 2.12 P1S, TABLE 2.1 P17, FIG 6.3
P72, FIG 6.4 P74. FIG 6.5 P7S, FIG 6.7
P80, FIG 6.9 P85, TABLE 7.1 P89
"1 often say that when you can measure what you are speaking about, and express it
in numbers, you know something about it~ but when you cannot measure it, when
you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory
kind; it may be the beginning of knowledge, but you have scarcely, in your
thoughts, advanced to the stage of science whatever the matter may be. "
Lord Kelvin
Acknowledgements
I would like to express my sincere thanks to my Director of Studies
Professor J.e. Dearden and my Industrial supervisor Dr. P.R. Sibley, for their help
and advice with all aspects of this work.
I would like to thank Glaxo Research and Development for their sponsorship of this
work and gratefully acknowledge receipt of a research studentship from GRD.
I would like to acknowledge and thank several people who helped me during the
course of my research, and to whom I am very grateful;
Dr.M.T.D. Cronin, for his help with all aspects of the computing.
All the staff at Portsmouth University involved in the statistical workshop, which was
a necessity and much valued.
Jane, Su, Joel, Azmina, Loma and Mike of the Immunotoxicology section at GRD,
and everyone at GRD who helped in my work and who made my time there both
enjoyable and productive.
I would like to convey my special thanks to Judy, Janey and Taravat, fellow research
students at LJMU, for their moral support, help and advice, and for making the
laboratory a cheerful and constructive work place.
Last but by no means least, I should like to thank my husband Mike, my parents and
the rest of my family and friends for their continual support, both financial and moral,
throughout my never ending academic career.
\
Abstract
Since their inception the B-Iactam antibiotics have become one of the most
important classes of phannaceutical agents, both therapeutically and economically,
in modern day usage for the treatment of a wide spectrum of bacterial infections.
However, due to the versatility of bacteria many previously treatable species are
developing resistance to the antibiotics currently available and so there is ever a
need to develop more ~-lactam antibiotics, which are effective and yet safe.
A major drawback to the ~-lactams is the degree of immunologically adverse
reactions they induce.
It was the aim of this study to develop both mechanistic and immunological
methods to enable the prediction of a B-lactam's potential to induce an allergic
response and to determine if a relationship between these responses and the
molecular properties of the ~-lactams was present.
In this study a database pertaining to frequency by which 70 p-lactams induce
adverse reactions has been compiled and used to produce 27 QSAR models.
A highly sensitive assay for the quantitation of cross-reactivity between B-lactams
and serum anti-benzylpenicillin antibodies has been developed and used to
determine the cross-reactivity potential of 31 ~-lactams and to develop 18 QSAR
models.
All of the QSARs developed suggest that the shape and electron separation of the
~-lactams are crucial to the development and extent of adverse response or cross
reactivity induced by a specific p-lactam antibiotic, new or old.
The QSARs developed will enable the design and development of new ~-lactam
antibiotics which present a significantly lower risk of inducing immunologically
mediated adverse responses when used therapeutically.
Two sensitive assays for the quantitative detennination of the cytokines IL2 and
IL4 in lymphocyte culture supernatants have been developed, and have been shown
to have a potential use in the prediction of the type of immunological response
initiated following p-Iactam stimulation of a sensitised individual.
fi
Contents
Paoe #
:=
Acknowledgements
Abstract
11
Contents
111
List of Figures
IV
List of Tables
IX
1.0 General Introduction
2.0 p-Lactam Antibiotics
2.1 Introduction 3
2.2 Molecular Mode of Action 4
2.3 Penicillins 9
2.4 Cephalosporins It
2.5 Drawbacks of~-Lactams 16
2.6 Future Developments 19
3.0 Computational Chemistry
3. 1 Concepts and Applications 20
3. 11 Molecular Mechanics 21
3.12 Molecular Orbital Theory 22
3.2 Computational Chemistry in QSAR 23
4.0 Quantitative Structure-Activity Relationships (QSAR)
4.1 Definition 24
4.2 Historical Background and Developments 24
4.3 Objectives ofQSAR 27
4.31 Biological Data 27
4.32 Physical and Chemical Descriptors 28
4.33 Structural or Topological Parameters 42
4.34 Statistical Techniques 46
4.4 Principal Steps in the Development of a QSAR 46
4.5 Restrictions 47
4.6 Adavantages and Uses 47
4.61 Examples of some QSAR's 48
5.0 Statistical Techniques
5.1 Introduction 49
5.2 Linear Relationships 49
5.21 Simple Graphical Plots 49
5.22 Regression Analysis 49
5.23 Pattern Recognition or Multivariate Analysis 54
°ril
5.3 Non-Linear Relationships
61
5.4 'Goodness of Fit' of Statistical Models 62
5.5 VaIidation of Statistical Models 65
6.0 Immunology
6. I Introduction 66
6.2 History oflmmunologicaI Ideas 66
6.3 Immunity 68
6.4 Immunohistology 68
6.5 Cellular Components of the Immune System 73
6.6 Cytokines 83
7.0 Immunotoxicology
7.1 Introduction 87
7.2 Immunotoxic Drug Reactions 88
7.3 IrnmunotoxicologicaI Assesment 95
8.0 Project Aim 103
9.0 Prediction of the Frequency of ~-Lactam Induced Adverse Reactions
9. 1 Introduction 104
13-
9.2 Generation of Data-Base Pertaining to the frequency of Lactam
Antibiotic Induced Adverse Reactions 105
9.21 Method 105
9.22 Results and Discussion 107
9.3 Development ofQSAR Models ofl3-Lactam Allergenicity 118
9.31 Method 118
9.32 Results and Discussion 123
9.3221 QSAR Models of All Adverse Reaction Frequencies 131
9.3222 QSAR Models of Cutaneous Rash Frequency 145
9.3223 QSAR Models ofGI Disruption Frequency 159
9.4 Prediction of the Frequencies of ARs of Untested Compounds 173
9.41 Method 173
9.42 Results and Discussion 173
9.5 Conclusion 189
10.0 Prediction of Cross-Reactivity Between I3-Lactam Antibiotics and
Anti-Benzylpenicillin Serum Antibodies
10.1 Introduction 190
10.2 Development of an Assay for the Quantitative Determination of
Cross-Reactivity Between 13-Lactam Antibiotics and
Anti-Benzylpenicillin Serum Antibodies 191
10.3 QSAR Study of the Cross-Reactivity of~-Lactam Antibiotics and
Anti-Benzylpenicillin Serum Antibodies 218
10.21 Method 218
10.22 Results and Discussion 220
10.3231 QSAR Models of Cross-Reactivity at 3mM Concentration 227
10.3231 QSAR Models of Cross-Reactivity at HRTDD 238
10.4 Prediction of the Cross-Reactivity of Untested Compounds 249
10.41 Method 249
10.42 Results and Discussion 249
10.5 Conclusion 252
11.0 Determination of Differential Cytokine Secretion Patterns fonn Sensitised
Lympbocytes Following f3-Lactam Stimulation
11.1 Introduction 254
11.2 Developemnt of Sensitive ELISAs for the Detection of IL2 & IL4 256
11.3 Determination IL2 & IL4 Levels in Lympbocyte Culture Supernatant
Following In Vitro Stimulation 267
11.4 Investiagtion into Use of mLNA in Cytokine Analysis 273
11.5 Conclusion 285
12.0 General Conclusion 286
13.0 References 288
Appendix 1 Structures of 70 ~-Lactam Antibiotics Al
Appendix 2 Smiles Codes of the 70 ~-Lactam Antibiotics AI3
Appendix 3 List ofPhysico-Chemical and Structural Parameters Generated AI7
Appendix 4 Compounds, Descrpitor Variable Values, Actual Predicted and
Residual Values, Correlation Matrices and Validity Plots for QSARs
Generated in 9.3 A20
Appendix 5 Therapeutic Dose Levels and Plasma Half-Lives A83
Appendix 6 mg/ml Quantities off3-Lacatam Antibiotic used to give 3mM
and HRTDD Concentrations for ELISA Analysis A86
Appendix 7 Compounds, Descrpitor Variable Values, Actual Predicted and
Residual Values, Correlation Matrices and Validity Plots for QSARs
Generated in 10.3 A87
Appendix 8 ElLISA Antibody Technical Data Sheets AI15
Bibliography of Publications Arising from this Study
Listing of Poster Presentations PI
Abstracts P2
Papers P7
v
List of Figures
Page #
2.1 The ~-Lactam Ring
2.2 Diagrammatic Representation of the Bacterial Cell Wall 5
2.3 Mechanism of Normal Bacterial Cell Wall Synthesis 5
2.4 Diagrammatic Representation of the Similarity Between p-Lactams
and the Natural Substrate of the Transpeptidase Enzymes 7
2.5 Diagrammatic Representation of Possible Enzymic ~-Lactam Binding Sites 8
2.6 lliustration of the Molecular Mode of Action of the J3-Lactam Antibiotics 8
2.7 Common Penicillin Structure 10
2.8 Fonnation of a Penicillin via Hydrolysis 10
2.9 Cephalosporin Common Structure and Structure-Activity Relationships 13
2. 10 Outline of Cephalosporin Production 13
2.11 Cephalosporin Classification 14
2.12 Time Scale of p-Lactam Antibiotic Development 15
4.1 The Biphasic Relationship Between Biological Activity and LogP 29
4.2 Diagrammatic Representation ofVerloop's Sterimol Parameters 42
4.3 Simplified Representation of a Molecular Connectivity Calculation 45
5.1 2D Representation of a Linear Regression Relationship 51
5.2 Dendogram presentation of Cluster Analysis Results 60
6.1 Illustration of the Differential Derivation of the Two Systems
of Adaptive Immunity 69
6.2 Diagram of the Internal Structure of the Thymus 71
6.3 Diagram of the Internal Structure of the Spleen 72
6.4 Diagram of the Internal Structure of a Lymph Node 74
6.5 The Major Cellular Components of the Immune System 75
6.6 Diagram ofLymphocyte Production, Activation and
Immune Response Initiation 78
6.7 lliustration of the T-Lymphocye Receptor 80
6.8 Diagram of the Differential Cytokine Secretions by Thl and Th2 Clones 82
6.9 lllustration of the Molecular Structure ofIL2 85
7.1 Diagrammatic Representation of the celluar Events in
Immediate Hypersensitivity Reactions 92
7.2 Diagrammatic Representation of the celIuar Events in
Delayed Hypersensitivity Reactions 94
7.3 Outline of a Tiered Panel oflmmunotoxicological Tests 96
7.4 Diagrammatic Representation of a Non-Competitive ELISA 100
9.l Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 28 Penicillin Antibiotics 110
9.2 Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 42 Cephalosporin Antibiotics 113
9.3 Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 15 Oral Penicillin Antibiotics 114
9.4 Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 13 Parenteral Penicillin Antibiotics 115
9.5 Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 17 Oral Cephalosporin Antibiotics 116
9.6 Relative Frequencies of Adverse Reactions Exhibited in Response to
Therapy with 25 Parenteral Cephalosporin Antibiotics 117
9.7 Representation of the Relationship Between the Frequency of Cutaneous Rash
Induced by Oral Penicillins and Their Shape Similarity to Benzylpenicillin 152
10.1 Reproducibility of Benzylpenicillin Cytochrome-C ELISA for the Detection
of Anti-Benzylpenicillin Antibodies in Rabbit Serum 193
10.2 Adsorbance of Three Concentrations ofBenzylpeniciIlin as Detected
by BPCC ELISA 196
10.3 Diagram of the Checkerboard ELISA Plate 198
10.4 Checkerboard Optimisation of BP CC ELISA Reagent Concentrations 199
10.5 Reproducibility ofOptimised BPCC ELISA 200
10.6 Adsorption of Benzylpenicillin as Detected by Optimised ELISA 202
10.7 Diagrammatic representation of the Competitive ELLS A for the
Quantitation Determination of Cross-Reactivity 204
10.8 Determination of Cross-Reactivity Between Native Benzylpenicillin and
Anti-Benzylpenicillin Serum Antibodies, Using Optimised ELISA 205
10.9 Determination of the Concentrations of Native Benzylpenicillin Giving
Maximal and Minimal Cross-Reactivity 207
10. 10 Graphical Representation of Different Extents of Cross-Reactivity of
Different Concentrations of Native Benzylpenicillin 209
10.11 Comparrison of the Percentage Cross-Reactivities of Various PenicilIins
with Rabbit Anti-Benzylpenicillin Serum Antibodies at Two Concentration
Levels, as Determined by Optimsed Competitve ELISA 216
10.12 Comparrison of the Percentage Cross-Reactivities of Various Cephalosporins
with Rabbit Anti-Benzylpenicillin Serum Antibodies at Two Concentration
Levels, as Determined by Optimsed Competitve ELISA 217