Predictive Toxicology in Drug Safety

Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
Contributors......Page 9
Prologue: Predictive toxicology: a new chapter in drug safety evaluation......Page 13
Predictive Toxicology in Drug Safety......Page 15
1.1 Introduction......Page 17
1.1.1 Candidate selection and attrition – the inevitability of failure......Page 18
1.1.2 In silico, in vitro, and in vivo – what approaches to use, and when?......Page 20
1.2 Meaning and value of predicting human toxicity in pharmaceutical development......Page 22
1.3.1 Predictive value of animal testing......Page 23
1.4 Limitations of in vivo testing in drug development – example of carcinogenicity studies......Page 27
1.5 Remaining gaps and additional perspectives on predicting human toxicity......Page 30
references......Page 32
2.1 Introduction......Page 34
2.2 Screening approaches for mutagenicity......Page 36
2.3 Screening approaches for clastogenicity......Page 38
2.4 Stress (SOS) response-based screening assays......Page 42
References......Page 46
3.2 The regulatory situation......Page 50
3.3 Ion channels involved in cardiac action potential and pacemaker activity......Page 52
3.4 Heterogeneity of repolarization and dispersion......Page 53
3.5.1 In silico predictions......Page 54
3.5.2 Considerations on the hERG assay......Page 56
3.5.3 Repolarization assays......Page 58
3.5.4 Arterially perfused wedge left ventricular preparations......Page 59
3.5.6 Measurement of the concentration of test article in in vitro systems......Page 60
3.5.7 Nonrodent in vivo telemetry......Page 61
3.6 Integrated cardiac risk assessment......Page 62
3.7 Outlook......Page 65
References......Page 66
4.1 Introduction: The problem of drug-induced liver injury......Page 70
4.2.1 Risk factors from a toxicokinetic perspective......Page 71
4.2.2 Risk factors from a toxicodynamic perspective......Page 72
4.3 Identify safer drugs: Risk factors of a problematic drug......Page 77
4.3.1 Multi-hit and multistep mechanisms of DILI: A contemporary understanding......Page 78
4.3.2 Integrated approaches to predict DILI......Page 80
4.3.3 The need for more predictive human hepatotoxicity models......Page 83
4.4 Concluding remarks and outlook......Page 85
References......Page 86
5.1 Introduction......Page 92
5.1.1 Mechanisms of adverse drug–drug interactions......Page 93
Phase II conjugation......Page 94
5.1.3 CYP isoforms......Page 95
Hepatocytes......Page 96
Liver microsomes......Page 97
5.2 Mechanisms of Metabolic Drug–Drug Interactions......Page 98
Induction potential for drug-metabolizing enzymes......Page 99
Study 1: Metabolic phenotyping 1 – metabolite identification......Page 100
Study 3: Metabolic phenotyping 3 – identification of P450 isoform pathways (P450 phenotyping)......Page 101
Liver microsome/inhibitor study design......Page 103
Study 4: CYP inhibitory potential......Page 104
IC50, Ki, Kinact and [I]/Ki determinations......Page 105
Study 5: Enzyme Induction potential......Page 108
Study 6: In vitro empirical drug–drug interactions......Page 109
Pathway evaluation......Page 110
P450 inhibition......Page 111
5.4 Nuclear Receptors and Drug–Drug Interactions......Page 112
References......Page 114
6.2 Linking metabolism with toxicity......Page 118
6.3 Reactive metabolites and idiosyncratic drug toxicity – key challenges in drug discovery......Page 119
6.4.1 Experimental methodology to evaluate reactive metabolite formation......Page 120
6.4.2 In silico and experimental tools for assessment of bioactivation potential of new compounds......Page 122
6.5 Structural Alert Predictions......Page 125
6.6 Structural alerts and drug design......Page 126
6.7 Are reactive metabolite trapping and covalent binding studies reliable predictors of toxicity potential of drug candidates?......Page 130
6.9 Concluding remarks......Page 133
References......Page 134
7.1 Adverse drug reactions mediated by the adaptive immune system......Page 140
7.2 Nickel-mediated contact hypersensitivity......Page 141
7.3 Technologies to predict contact sensitization......Page 142
7.4.1 Nucleic acids stimulating Toll-like receptor 9......Page 143
7.4.2 Nucleic acids stimulating Toll-like receptors 7 and 8......Page 145
7.6 Summary......Page 146
References......Page 147
8.1 Introduction......Page 151
8.2 In vivo testing for neurotoxicity and developmental neurotoxicity......Page 152
8.3 In vitro neurotoxicity testing in mammalian cells......Page 155
8.3.1 In vitro systems for mechanistic studies......Page 157
8.3.2 In vitro systems for neurotoxicity screening......Page 158
8.4 Nonmammalian models for neurotoxicity testing......Page 162
8.5 Conclusions......Page 163
References......Page 165
9.1 Introduction: The business need for in vitro tests......Page 169
9.2.3 What targets need to be assessed?......Page 170
9.2.4 Genetically modified animal models......Page 171
9.3 Off-target Effects......Page 173
9.3.1 In silico approaches......Page 174
9.4 In vitro tests......Page 175
9.4.2 Embryonic stem cells......Page 176
9.4.3 Whole embryo culture......Page 177
9.4.4 Zebrafish......Page 178
9.5.1 In vivo......Page 180
9.5.3 Interpretation of in vitro developmental toxicity data......Page 181
9.5.4 Comparison of all four tests: advantages/disadvantages......Page 183
9.5.5 Performance......Page 185
9.6 Industrial Application of In Vitro Screens......Page 186
9.7 Putting it all together......Page 189
9.8.1 Target-specific effects......Page 191
9.8.3 In vitro screening......Page 192
References......Page 194
10.1 Introduction: Risk awareness, a major element of modern drug discovery......Page 199
10.2 Outline of the need of integrated assessment of ADMET during lead optimization......Page 200
Target selection......Page 201
Hit expansion – increase awareness......Page 202
Lead nomination – decision making and hazard identification......Page 203
Clinical candidate selection – characterize candidates and assess risk (including regulatory requirements prior to clinical trials) of identified molecules......Page 204
10.3.1 In vitro toxicology: The emergence of multiple readout-based approaches......Page 205
Confidence in data......Page 207
Relevance of data......Page 209
10.4 Learning from past mistakes......Page 210
10.5 In silico approaches, decision support tools, and modeling......Page 211
10.5.2 How to drive lead optimization toward an acceptable therapeutic index......Page 212
10.5.3 Moving away from in vitro affinity toward effective plasma and tissue concentrations......Page 214
10.6 Critical evaluation and conclusions......Page 216
Acknowledgments......Page 217
References......Page 218
11.1 Cancer as a Worldwide Disease......Page 220
11.2 Oncology Therapy for the Late-Stage Cancer Patient......Page 221
11.3 Toxicology/Pathology Challenges in the Discovery of New Oncology Drugs......Page 222
11.4.1 Classifications of adverse effects......Page 223
11.4.2 Target validation......Page 225
11.4.3 Lead optimization......Page 226
Early lead optimization — in vitro considerations......Page 227
Early lead optimization – in vivo considerations......Page 229
11.4.4 Definitions of benchmark doses in oncology testing......Page 230
11.4.5 Building a candidate database......Page 231
11.5.1 Criteria for progression of a candidate......Page 233
11.5.2 IND-enabling (GLP-compliant) toxicology studies......Page 235
11.5.3 Safety pharmacology......Page 238
11.5.4 Investigational new drug application......Page 239
11.5.5 Longer-term toxicity studies for cancer chemotherapeutics......Page 240
Pediatric testing and combination therapy considerations......Page 241
Acknowledgments......Page 242
References......Page 243
12.1 Introduction......Page 246
12.2.1 Use of tissue distribution to evaluate target-related toxicity......Page 247
12.2.2 Use of knockout animals to confirm target-related toxicity......Page 248
12.2.4 Use of inactive enantiomers to evaluate pharmacologic target-related toxicity......Page 250
12.3.1 Background......Page 251
12.3.2 Compound metabolism as the determinant of toxicity......Page 252
12.3.3 In vivo and/or in vitro studies investigating chemistry-related toxicities.......Page 253
12.4 Impact of mechanistic studies on integrated risk assessment for a development molecule......Page 254
References......Page 256
13.1 Introduction......Page 260
13.2 The fish embryo model......Page 262
13.3 Acute and chronic toxicity......Page 267
13.5 Developmental toxicity......Page 269
13.6.2 Hepatotoxicity......Page 272
13.6.4 Gastrointestinal toxicity......Page 273
13.7 Environmental risk assessment of medicinal products......Page 276
13.8 Limitations and research perspectives......Page 277
13.9 Conclusion......Page 278
References......Page 279
14.1.2 Importance of mouse strains and background genetics......Page 285
Genomic versus cDNA transgenics......Page 286
14.1.5 Mouse phenotyping......Page 287
14.1.7 Knockdowns (siRNA, shRNA, antisense)......Page 288
14.2 Use of GEMs in target safety validation......Page 289
14.3 Use of GEMs in on- or off-target liability assessment......Page 291
14.5.1 Mouse versus human targets......Page 292
14.5.2 Humanized metabolism models......Page 293
14.6 Genetically engineered rats......Page 294
14.7 GEM pitfalls and caveats......Page 295
References......Page 296
15.1 INTRODUCTION TO TOXICOGENOMICS......Page 300
15.2 TOXICOGENOMICS APPLICATIONS AND CURRENT CHALLENGES......Page 302
15.3 TOXICOGENOMIC STUDY DESIGN......Page 306
15.4 PATHWAYS AND NETWORKS......Page 308
15.5 INTEGRATIVE PATHWAY AND NETWORK ANALYSIS FOR ‘OMICS DATA......Page 310
15.6 PATHWAY ANALYSIS SCENARIO I: KNOWLEDGE-DRIVEN INTEGRATED ‘OMICS......Page 311
15.7 PATHWAY ANALYSIS SCENARIO II: RANDOM FORESTS CLASSIFICATION......Page 312
15.8 A COMPREHENSIVE VIEW MORE THAN SNAPSHOTS......Page 313
References......Page 315
16.1 Scope......Page 318
16.2 Current status......Page 319
16.3 Needs for improved safety biomarkers......Page 324
16.4 Qualifying new safety biomarkers to foster regulatory acceptance......Page 326
References......Page 327
17.1 Introduction......Page 330
17.3 General Concepts and Considerations......Page 331
17.4 Logistic Regression Models......Page 333
17.5 Case Study 1: Modeling TK and Moribundity from Dog Studies......Page 334
17.6 Case Study 2: Modeling TK and Severity of Lesions in Tissues from a Rat Study......Page 338
17.7 Limitation of the Modeling Approach......Page 340
17.8 Bridging Preclinical Data to Humans......Page 341
17.9 Conclusions......Page 342
References......Page 343
18.1 Mathematical modeling in drug discovery......Page 346
18.2 Mechanistic mathematical modeling of mab therapeutic index......Page 347
18.3 Mathematical modeling for predicting efficacy......Page 348
18.4.1 Antibody–antigen complex......Page 353
18.4.2 Uptake of the mAb by nontarget cells and tissues......Page 354
18.4.3 Immunogenicity......Page 355
18.4.4 Infusion reactions......Page 356
References......Page 357
19.1 Introduction......Page 360
19.2 Predictive Strategies......Page 361
19.2.2 General toxicology......Page 362
19.2.4 Species selection......Page 363
19.2.7 Reproductive and developmental studies......Page 365
19.2.10 Integration studies......Page 368
19.3.1 Autoimmunity risk and vaccination......Page 369
19.3.2 Predicting autoimmunity......Page 370
19.3.3 Autoimmunity and bioinformatics......Page 371
19.4 Allergy/hypersensitivity......Page 373
19.5.1 DNA vaccines......Page 374
19.5.3 Adjuvants......Page 375
19.6 Predictive in vitro systems......Page 376
References......Page 380
Epilogue......Page 389
Index......Page 393