Enzyme- and Transporter-Based Drug-Drug Interactions (eBook)

K. Sandy Pang Ph.D. is Professor of Pharmacy and Pharmacology, Faculties of Pharmacy and Medicine at the University of Toronto. She received her B.S. (Pharmacy) from the University of Toronto, Ph.D. (Pharmaceutical Chemistry) from UCSF and post-doctoral training with Dr. James R. Gillette as a Fogarty International Fellow at the National Institutes of Health. Dr. Pang’s work spans the fields of pharmacokinetics, drug metabolism and transporters and their regulation. Her research programs are aimed towards a mechanistic-based understanding of the handling of drugs and their metabolites within the liver, the intestine, and kidney via integration of relevant processes into physiologically-based models, encompassing state-of-the-art experimentation and theory. Her work emphasizes the presence of immediate removal of formed metabolites in situ the eliminating organ that reveals differences in the fates of formed vs. preformed metabolites because of transmembrane barriers, enzyme heterogeneity, enzymatic coupling, and kinetics of successive formation of metabolites. Recent studies focused on the continuation of metabolite PBPK modeling, siRNA disposition, and the role of 1a,25-dihydroxyvitamin D3-liganded vitamin D receptor on the regulation of transporters and enzymes. Dr. Pang has published over 200 original articles and chapters. She has served on various committees for NIH ASPET, AAPS, ISSX, and AAAS. She is the editor-in-chief of Biopharmaceutics and Drug Disposition, and is a member of the editorial review boards of the American Journal of Physiology, Journal of Pharmacology and Experimental Therapeutics, Drug Metabolism and Disposition, and AAPS Journal. She was the recipient of the NIH Research Career Development Award, Faculty Development award from the Medical Research Council of Canada, the McNeil Award from the Faculties of Pharmacies in Canada, and the Research Achievement Award in Pharmacokinetics, Pharmacodynamics and Drug Metabolism from the American Association of Pharmaceutical Scientists (AAPS). A. David Rodrigues is Executive Director of the Metabolism & Pharmacokinetics Department, Pharmaceutical Candidate Optimization, at Bristol-Myers Squibb, Princeton, New Jersey. The author and co-author of over ninety peer-reviewed journal articles and book chapters, Dr. Rodrigues sits of the Editorial Board of three journals (Drug Metabolism and Disposition, Current Drug Metabolism, and Drug Metabolism Letters) and is member of the International Society for the Study of Xenobiotics (ISSX) and the American Association of Pharmaceutical Scientists (AAPS). He received the B.Sc. degree (1984) in applied science from Kingston-upon-Thames Polytechnic, Surrey, England, and the Ph.D. degree (1988) in biochemistry from the University of Surrey, Guildford, England. Raimund M. Peter is Associate Director of the Drug Metabolism & Pharmacokinetics Section, Cardiovascular & Gastrointestinal Research Department, at AstraZeneca, Alderley Park, United Kingdom. The author and co-author of twenty peer-reviewed journal articles, Dr. Peter is the current Chairman of the Drug Metabolism Focus Group of AAPS and is member of the International Society for the Study of Xenobiotics (ISSX), the American Association of Pharmaceutical Scientists (AAPS), and the American Chemical Society. He received the Dipl.-Chem. degree (1986) in chemistry, and the Ph.D. degree (1992) in chemistry & biochemical pharmacology from the University of Erlangen-Nuernberg, Germany.

Preface 4
Contents 6
Contributors 9
About the Editors 14
Part I Determinants of Drug ADME 16
1 Enzymatic Basis of Phase I and Phase II Drug Metabolism 17
1.1 Introduction to Phase I and Phase II Metabolism 17
1.2 Phase I Enzymatic Reactions 18
1.2.1 Cytochromes P450 18
1.2.1.1 P450 Catalytic Cycle 18
1.2.1.2 Specific P450 Isoforms 20
1.2.2 Flavin-Containing Monooxygenases 24
1.3 Phase II Enzymatic Reactions 25
1.3.1 Glutathione S -Transferases 25
1.3.2 N -Acetyltransferases 27
1.3.3 UDP-Glucuronosyltransferases 28
1.3.3.1 UGT1A1 28
1.3.3.2 UGT2B7 30
1.3.4 Sulfotransferases 30
1.4 Conclusions 31
References 32
2 Transporters: Importance in Drug Absorption, Distribution, and Removal 40
2.1 Introduction 40
2.2 SLC Drug Transporters 47
2.3 ABC Drug Transporters 51
2.4 Transporters for Intestinal Drug Absorption 52
2.5 Transporters for Hepatic Drug Elimination 54
2.6 Transporters for Renal Drug Elimination 56
2.7 Conclusions 58
References 59
3 ADME Pharmacogenetics and Its Impact on DrugDrug Interactions 63
3.1 Introduction 63
3.1.1 CYP450s 65
3.1.2 CYP450 Polymorphism-Related Drug--Drug Interactions 65
3.1.2.1 CYP2B6 65
3.1.2.2 CYP2C9 67
3.1.2.3 CYP2C19 68
3.1.2.4 CYP3A4/5 69
3.1.2.5 CYP2D6 70
3.2 Drug Transporters 71
3.2.1 ABCB1 (P-gp) Polymorphisms and Drug Interactions 72
3.3 Drug Uptake Transporters 75
3.3.1 OATP Polymorphisms and Drug Interactions 75
3.3.2 OCT Polymorphisms and Drug Interactions 76
3.4 Clinical Relevance of Pharmacogenetics for DDI 76
3.4.1 Implications for Drug Treatment 77
3.4.2 Implications for Drug Development 79
3.5 Conclusions 80
3.5.1 WEB-Based Scientific Resources 80
References 81
4 Impact of Nuclear Receptors CAR, PXR, FXR, and VDR, and Their Ligands On Enzymes and Transporters 87
4.1 Introduction 87
4.2 Pharmacology of Induction 89
4.2.1 Nuclear Receptor-Mediated Regulation of Drug Disposition Gene Expression 89
4.2.2 Drug-Metabolizing Enzymes Regulated by Nuclear Receptors 91
4.2.3 Drug Transporters Regulated by Nuclear Receptors 92
4.2.4 Nuclear Receptor Regulatory Networks in Drug Disposition 93
4.2.5 Prediction of Induction-Type Drug Interactions 94
4.3 Drugs and Herbal Medicines Involved in Clinically Relevant Induction-Type DrugDrug Interactions 95
4.4 Therapeutic Aspects of Induction-Type Drug Interactions 98
4.4.1 Time Course of Nuclear Receptor Responses 98
4.4.2 Inhibition of Nuclear Receptors 99
4.4.3 Nuclear Receptors and Drug Side Effects 99
4.4.4 Interindividual Variation in Drug Response -- Pharmacogenetics of Nuclear Receptors 100
4.4.5 Xenobiotic Receptors as Drug Targets 101
4.5 Perspectives 102
References 102
5 Impact of Physiological Determinants: Flow, Binding, Transporters and Enzymes on Organ and TotalBody Clearances 118
5.1 Introduction 118
5.2 Common Determinants of Clearance in Eliminating Organs 119
5.2.1 Protein and Red Blood Cell Binding 120
5.2.2 Blood Flow 123
5.2.3 Enzymatic Activity 124
5.2.4 Excretory Activity 124
5.2.5 Basolateral Transporters 124
5.3 Physiologically-Based Pharmacokinetic (PBPK) Models 125
5.4 Rate-Limiting Step in Clearance 126
5.5 Models for Hepatic Drug Clearance 126
5.5.1 The Well-Stirred Model for Hepatic Drug Clearance 127
5.5.2 Solutions for AUC and CL 129
5.5.3 Examples 131
5.6 PBPK Modeling of Renal Drug Clearance 131
5.6.1 Solutions for AUC and CL r 135
5.6.2 Examples 136
5.7 Models for Intestinal Drug Clearance 137
5.7.1 Solutions for AUC and CL I 139
5.7.2 Examples 140
5.8 Whole Body PBPK Modeling 140
5.9 TransporterEnzyme, TransporterTransporter, and EnzymeEnzyme Interplay 146
5.10 Conclusions and Perspectives 150
References 151
Part II Methods for the Study of DrugDrug Interactions 159
6 In Silico Approaches to Predict DDIs 160
6.1 Introduction 160
6.2 QSAR Modeling 160
6.3 Pharmacophore Modeling 161
6.4 Docking 162
6.5 In Combo Modeling 163
6.6 Industrial Application of DDI Modeling 163
6.7 The Current State of Enzymatic Predictions for DDI 164
6.7.1 Overview of Statistical Approches 164
6.7.2 Examples of P450 Inhibition 165
6.7.3 P450 Crystallization 166
6.7.4 P450 Docking 167
6.7.5 Mechanism Based Inhibitors 169
6.8 Current State of Transporter-Based DDI Models 170
6.9 Limitations in Current In Silico Methodologies 171
6.10 Future Perspectives, Emerging Science, Conclusions 171
References 172
7 In Vitro Techniques to Study Drug--Drug Interactionsof Drug Metabolism: Cytochrome P450 178
7.1 Introduction 178
7.2 General Approach and Practices 181
7.2.1 Experimental Conduct 184
7.2.2 Probe Substrates 185
7.2.3 Correction for Nonspecific Binding In Vitro 185
7.3 Enzyme and Victim Drug Properties 187
7.3.1 Enzyme Turnover 187
7.3.2 Impact of Parallel Elimination Pathways 189
7.3.3 Prediction of F G from In Vitro Data 191
7.4 Reversible Enzyme Inhibition 193
7.4.1 In Vitro Systems Used for the Assessment of Reversible Inhibition 194
7.4.2 Inhibition at Multiple Sites 195
7.5 Time-Dependent Inhibition 196
7.5.1 In Vitro Systems Used for the Assessment of Irreversible Inhibition 197
7.5.2 Alternative Approaches for the Assessment of Time-Dependent Drug--Drug Interactions 200
7.6 Induction 201
7.6.1 In Vitro Systems Used for the Assessment of Induction 202
7.7 Predictive Utility of In Vitro Inhibition and Induction Parameters 205
7.7.1 Qualitative Zoning and Ranking Approach of Drug--Drug Interactions 205
7.7.2 Quantitative Prediction of Inhibition 206
7.7.3 Importance of Multiple Inhibitors and Multiple Inhibition Mechanisms 209
7.7.4 Quantitative Prediction of Induction 211
7.7.5 Mechanisms for False-Negative and False-Positive DDI Predictions 213
7.8 Perspectives for Future Improvement of Prediction Strategies 213
References 215
8 The In Vitro Characterization of Inhibitory DrugDrug Interactions Involving UDP-Glucuronosyltransferase 225
8.1 Inhibitory DrugDrug Interactions Involving Conjugation Enzymes 225
8.2 The Glucuronidation Reaction and UDP-Glucuronosyltransferase (UGT) 226
8.3 The Characterization of Drug Glucuronidation In Vitro: Experimental Considerations 228
8.3.1 Enzyme Sources 228
8.3.2 Latency of Microsomal UGT 229
8.3.3 Dependence of UGT Activity on pH, Buffer Type, Ionic Strength, and Organic Solvents 230
8.3.4 Glucuronide Stability 230
8.3.5 Cofactor Concentration 231
8.3.6 Nonspecific Binding of Substrate and Inhibitor 231
8.4 Reaction Phenotyping and the Qualitative Prediction of DDIs 231
8.5 Screening for Inhibition of Drug Glucuronidation In Vitro Kinetic and Pharmacogenetic Considerations 233
8.5.1 Atypical Glucuronidation Kinetics 233
8.5.2 Heterotropic Cooperativity 234
8.5.3 Genotype-Dependent Effects 234
8.6 Quantitative IV-IVE for DDI Involving Glucuronidated Drugs 235
8.6.1 Theoretical Considerations 235
8.6.2 Prediction of DDI Involving Glucuronidated Drugs 236
8.6.3 Endogenous Fatty Acids as Inhibitors of Drug Glucuronidation In Vitro 237
8.6.4 The Albumin Effect and DDI Prediction for Glucuronidated Drugs 238
8.7 Inhibitory DDIs Due to Glucuronide Conjugates 238
8.7.1 Mechanism-Based Inactivation 238
8.7.2 Assessment of Mechanism-Based Inactivation by Glucuronides 239
8.7.3 Potential Competitive Inhibition of CYP2C8 by Glucuronides 240
8.8 Prospects 240
References 240
9 In Vitro Techniques to Study Transporter-Based DDI 245
9.1 Introduction 245
9.2 Hepatocytes 248
9.2.1 Hepatocytes in Suspension 248
9.2.2 Sandwich-Cultured Hepatocytes 249
9.3 Uptake into Recombinant Cell Lines 251
9.4 Bidirectional Transport in Recombinant Cell Lines 252
9.5 The Vesicular Transport Assay 254
9.6 Utility of In Vitro Transporter Assays in Assessing Transporter-Based DDIs 256
9.7 Conclusions and Perspective 258
References 259
10 In Vitro Techniques to Study DrugDrug Interactions Involving Transport: Caco-2 Model for Study of P-Glycoprotein and Other Transporters 264
10.1 Introduction 264
10.2 Role of P-gp in Drug Disposition 265
10.2.1 Tissue Distribution of P-gp 265
10.2.2 Substrates and Inhibitors of P-gp 266
10.2.3 P-gp and Drug Disposition 267
10.3 P-gp and DDI 268
10.4 In Vitro Assessment of Transporter-Related DDI 270
10.4.1 Caco-2 Cell Model 270
10.4.2 Cell Culture- and Age-Dependent Changes to Caco-2 Cells 272
10.4.3 Experimental Approaches to Study Transporter-Mediated DDI in Caco-2 Cell Monolayers 273
10.5 In Vitro/In Vivo Relationships 277
10.6 Other Uptake and Efflux Transporters in Caco-2 Cells 279
10.6.1 Apical Uptake Transporters 280
10.6.2 Other Apical Efflux Transporters 280
10.6.3 Basolateral Efflux Transporters 281
10.7 Conclusions 282
References 282
11 Use of In Vivo Animal Models to AssessDrug--Drug Interactions 290
11.1 Introduction 290
11.2 Native Animal Models 292
11.2.1 Absorption Model 292
11.2.2 Metabolism Model 293
11.2.3 Excretion Model 295
11.3 Case Examples Use of Native Animal Models for DDI Studies 296
11.3.1 Metabolic Drug Interactions 296
11.3.1.1 Rhesus Model to Assess CYP3A-Mediated DDI 296
11.3.1.2 Rhesus Model for Evaluating Diclofenac (DF) as a Valid CYP2C9 Probe in Humans 297
11.3.1.3 Rat Model for Assessing CYP3A-Mediated DDI 299
11.3.2 Transporter-Mediated Drug Interaction 300
11.3.2.1 Cynomolgus Monkey Model to Assess Renal DDI 300
11.3.2.2 Rat and Rhesus Models to Assess Potential Renal Transporter-Mediated DDI 300
11.4 Transgenic and Knockout Animal Models 301
11.5 Conclusions 301
References 302
12 Extrapolation of In Vitro Metabolicand P-Glycoprotein-Mediated Transport Datato In Vivo by Modeling and Simulations 305
12.1 Prediction of DrugDrug Interactions in Hepatic Metabolism Using Physiologically Based Pharmacokinetic (PBPK) Modeling 305
12.1.1 Introduction 305
12.1.2 False Negative and False Positive Predictions Using the I/K i Method 307
12.1.3 The PBPK-Based Method 307
12.1.4 Discrepancy Between In Vitro and In Vivo K i 310
12.1.5 Prediction Result 312
12.2 Prediction of DDIs in Intestinal Metabolism and Transport 313
12.2.1 Introduction 313
12.2.2 The Dose/K i Method 314
12.2.3 Results 315
12.2.4 Comparison with FDA Draft Guidance 316
12.3 Development of a Computer Program for Predicting DDIs in the Liver and the Intestine 317
12.3.1 Introduction 317
12.3.2 Importance of PBPK Model-Based Prediction 318
12.3.3 Importance of the Prediction of DDIs in Drug Development 319
References 320
13 Translation of In Vitro Metabolic Data to Predict In Vivo DrugDrug Interactions: IVIVE and Modeling and Simulations 322
13.1 Introduction 322
13.2 Translation of In Vitro ADME Data to Characterize In Vivo Pharmacokinetics 324
13.2.1 Prediction of Exposure 324
13.2.1.1 IVIVE to Determine Oral Bioavailability 327
13.2.1.2 IVIVE to Determine Drug Clearance 327
13.2.2 Prediction of Concentration--Time Profile 329
13.2.2.1 IVIVE to Determine Drug Distribution 329
13.3 Requirements for an M-DDI Modeling and Simulation Platform 330
13.4 In Silico Simulations to Assess In Vivo M-DDIs Using In Vitro Data 331
13.4.1 Static Models for Estimating M-DDI, Ignoring Gut Metabolism 331
13.4.2 Static Model to Estimate M-DDI in Gut Wall 334
13.4.3 Considerations for Operational Concentration of the ''Perpetrator'' 336
13.4.4 Using Full Time Course of ''Perpetrator'' to Assess M-DDI 337
13.4.5 Assessing Variability in M-DDI 340
13.5 Conclusion 343
References 343
14 Absorption Models to Examine Bioavailabilityand Drug--Drug Interactions in Humans 347
14.1 Introduction 347
14.2 Remotely Activated Capsules 350
14.2.1 HF-Capsule 350
14.2.1.1 Design 350
14.2.1.2 Application 350
14.2.2 InteliSite ® 352
14.2.2.1 Design 352
14.2.2.2 Application 353
14.2.3 EnterionTM 354
14.2.3.1 Design 354
14.2.3.2 Application 355
14.3 Intestinal Aspiration/Perfusion Catheters 356
14.3.1 Loc-I-Gut ® 358
14.3.1.1 Design 358
14.3.1.2 Applications 359
14.3.1.3 Determination of Intestinal Presystemic Metabolism 360
14.3.1.4 Evaluation of Biliary Excretion 360
14.3.1.5 Impact of Modulators of Hepatic Transport 361
14.3.1.6 Directly Detecting Drug--Food Interactions 362
14.3.2 CHOL-ect 362
14.3.2.1 Design 362
14.3.2.2 Applications 363
14.4 Microdosing 367
14.5 Future Challenges 368
14.5.1 Need for Innovation 368
14.5.2 Identification of Probe Substrates 369
14.5.3 Better Detection and Quantification Methods 370
14.5.4 Understanding the Physiology of Drug Disposition Within Organs 370
14.6 Conclusions 370
References 371
15 Management of Drug Interactions of New Drugs in Multicenter Trials Using the Metabolism and Transport Drug Interaction Database 375
15.1 Introduction 375
15.2 Database Design and Content 376
15.3 Examples of Use 377
15.3.1 Example: Finding CYP3A Inhibitors in the Context of Clinical Trials 377
15.3.1.1 Background 377
15.3.1.2 Question 378
15.3.1.3 Search Strategy 378
15.3.1.4 Result Outputs 379
15.3.1.5 Interpretation 380
15.3.2 Example: Analysis of Drug Interactions in the Context of a Disease and Its Comorbidities 382
15.3.2.1 Background 382
15.3.2.2 Question 382
15.3.2.3 Search Strategy 383
15.3.2.4 Interpretation 387
15.4 Ongoing Developments 388
15.5 Appendix 389
15.4.0 Qualitative 389
15.4.0 Quantitative 389
References 390
16 Web-Based Database as a Tool to Examine DrugDrug Interactions Involving Transporters 391
16.1 Drug Transporters as a Target of DrugDrug Interaction 391
16.1.1 General Features of Transporter-Mediated Drug--Drug Interaction 391
16.1.2 Examples of Clinically Relevant Drug--Drug Interaction Mediated by Transporters 393
16.1.2.1 Multidrug Resistance (MDR) 1 (P-glycoprotein) 393
16.1.2.2 OATP Family Transporters 394
16.1.2.3 Organic Anion Transporter (OAT) 1 and OAT3 395
16.1.2.4 Multidrug and Toxin Extrusion (MATE) 396
16.2 What Should Be Considered When Predicting a Transporter-Mediated DrugDrug Interaction? 396
16.2.1 The Effect of Inhibitors on the Decrease in the Intrinsic Clearance of Substrate Drugs 397
16.2.2 The Influence of the Pharmacokinetic Properties of Substrate Drugs on the Extent of Drug--Drug Interaction 399
16.2.2.1 Contribution of Each Transporter to the Overall Membrane Transport of Substrates 399
16.2.2.2 Importance of the Transport Process in Overall Intrinsic Clearance 401
16.2.2.3 Effect of the Absolute Value of Organ Clearance and the Fraction of Substrates Excreted from the Liver and Kidney on the Extent of Drug--Drug Interaction 403
16.2.3 The Impact of the Inhibition of Uptake and Efflux Processes on Change in the Pharmacokinetics of Substrate Drugs 405
16.3 Examples of the Prediction of DrugDrug Interaction from In Vitro Data 406
16.4 Web-Based Transporter-Mediated DDI Database 408
16.5 In Silico Prediction of the Time Profiles of Substrate Drugs in the Presence of Inhibitors Using Physiologically Based Pharmacokinetic Modeling 410
References 414
Part III Impact of DrugDrug Interactions 417
17 Drug Disposition and DrugDrug Interactions: Importance of First-Pass Metabolism in Gut and Liver 418
17.1 Clinical Aspects of First-Pass Drug Interactions 418
17.1.1 Background 418
17.1.2 CYP3A-Based Interactions 419
17.1.2.1 Enzymology 419
17.1.2.2 Examples of Intestinal CYP3A Inhibition 420
17.1.2.3 Examples of Intestinal CYP3A Induction 421
17.1.2.4 Clinical Utility of CYP3A Inhibition 422
17.1.3 UGT-Based Interactions 422
17.2 Prediction of First-Pass Drug Interactions 424
17.2.1 Overview 424
17.2.2 Pharmacokinetic Principles of First-Pass Metabolism 424
17.2.3 Organ Intrinsic Clearance and Bioavailability 425
17.2.4 Organ Bioavailability and Drug Exposure 426
17.2.5 Added Complexity of Intestinal Drug Interactions 430
17.2.5.1 Diffusion Barrier Effects 430
17.2.5.2 Inhibitor Dose and Temporal Factors 432
17.3 Conclusions 433
References 434
18 Transporter-Based DrugDrug Interactions and Their Effect on Distribution Volumes 439
18.1 Introduction 439
18.1.1 Volume of Distribution: V 1 , V area , and V ss 440
18.1.2 Drug Transporters and Drug--Drug Interactions 441
18.2 Trends in Transporter Effects on Distribution Volume 441
18.2.1 Measures of Volume 442
18.2.2 Inter-relationship Between Volume, Clearance, and Half-Life 451
18.2.3 Interactions Involving Hepatic and Renal Uptake 452
18.2.4 Interactions Involving Hepatic and Renal Efflux 459
18.2.5 Secondary Interactions at the Blood--Brain Barrier 464
18.3 Pharmacodynamic Considerations 466
18.4 Experimental Considerations 467
18.5 Conclusions 469
References 469
19 Inactivation of Human Cytochrome P450 Enzymesand Drug--Drug Interactions 474
19.1 Introduction 474
19.2 Biochemical Aspects of Inactivation of Cytochrome P450 475
19.2.1 Quasi-irreversible Complex Formation 477
19.2.2 Covalent Modification of Heme and Protein 477
19.2.3 Examples of Functional Groups That Inactivate Cytochrome P450 477
19.2.3.1 Alkylamines 478
19.2.3.2 Methylenedioxyphenyl 478
19.2.3.3 Alkynes 479
19.2.3.4 Progenitors of Michael Acceptors 479
19.2.3.5 Five-Membered Aromatic Heterocyclic Compounds 480
19.2.3.6 Alkyl-Substituted 5-Member Aromatic Azaheterocyclics 481
19.3 Experimental Approaches to Determination of Time-Dependent Inhibition 482
19.3.1 Abbreviated Approaches to Identify Time-Dependent Inhibition 484
19.3.2 Determination of K I and k inact 485
19.4 Correlation of In Vitro P450 Inactivation Data to In Vivo DrugDrug Interactions 487
19.4.1 Prediction of Drug Interactions for Time-Dependent Inhibitors 487
19.4.2 Combined Mathematical Equation 488
19.4.3 Computer Simulations 490
19.4.4 Factors That Impact on Prediction Accuracy 490
19.4.4.1 Factors Specifically Required for Predicting DDI for Inactivators 490
19.4.4.2 Factors Which Are Important for Predicting DDI for Inactivators but Also Important for Other Mechanisms of DDI 491
19.5 Conclusions 492
References 493
20 Allosteric Enzyme- and Transporter-Based Interactions 497
20.1 Atypical Kinetic Profiles Resulting from Allosterism 498
20.2 Kinetic Models to Describe Atypical Kinetics 498
20.3 Cytochrome P450-Based Allosteric Interactions 500
20.3.1 CYP3A4 500
20.3.2 CYP2C9 501
20.3.3 CYP1A2 502
20.3.4 Other CYPs 502
20.4 Conjugating Enzyme-Based Allosteric Interactions 503
20.4.1 Glucuronosyltransferases (UGTs) 503
20.4.2 Sulfotransferases (SULTs) 506
20.5 Drug Transporter-Based Allosteric Interactions 506
20.5.1 P-glycoprotein (ABCB1) 506
20.5.2 Breast Cancer Resistance Protein (ABCG2) 508
20.6 In Vivo Examples of Allosteric Interactions 508
References 509
21 The Impact and In Vitro to In Vivo Prediction of Transporter-Based DrugDrug Interactions in Humans 516
21.1 Introduction 516
21.2 Key Concepts of Transporter-Based DDIs 518
21.2.1 Effect on PK Parameters 518
21.2.2 Synergistic Effect of two Transporters 519
21.2.3 Transporter DDI ''Masked'' as Metabolism DDI 520
21.2.4 Vectorial Transport 522
21.2.5 Equilibrative Transporter DDI 524
21.3 ATP-Binding Cassette (ABC) Super Family Transporter Interactions 525
21.3.1 P-gp (MDR1, ABCB1) Interactions 525
21.3.2 BCRP (ABCG2) Interactions 528
21.3.3 MRPs (ABCC Family) Interactions 529
21.3.4 Bile Salt Export Pump (BSEP, ABCB11) Interactions 529
21.4 Solute Carrier (SLC) Super Family Transporter Interactions 532
21.4.1 OATPs (SLC21 Family) Interactions 532
21.4.2 OAT (SLC22 Family) Interactions 535
21.4.3 OCT (SLC22 Family) Interactions 536
21.4.4 Peptide Transporters (PEPT1-2, SLC15A1-2) Interactions 537
21.4.5 Multidrug and Toxin Extrusion (MATE, SLC47A) Interactions 537
21.5 In Vitro to In Vivo DDI Prediction 538
21.5.1 P-glycoprotein (P-gp, MDR1) Drug--Drug Interaction Prediction 541
21.5.2 Distributional Drug--Drug Interaction Prediction 543
21.5.3 Absorption/Elimination Drug--Drug Interaction Prediction 543
21.5.4 OATP Drug--Drug Interaction Prediction 543
21.5.5 In Vitro to In Vivo Prediction Summary 544
21.6 Conclusions 544
References 545
22 Herbal Supplement-Based Interactions 553
22.1 Introduction 554
22.2 Metabolic EnzymeTransporter Interactions 555
22.2.1 St. John's Wort 555
22.2.2 Garlic 558
22.2.3 Ginseng 559
22.2.4 Milk Thistle 561
22.2.5 Ginkgo 562
22.2.6 Other Herbal Products 563
22.3 Interactions of Flavonoids with Metabolizing Enzymes/Transporters 563
22.3.1 Interactions of Flavonoids with Metabolizing Enzymes 565
22.3.2 Interactions of Flavonoids with Transporters 567
22.4 Interactions of Organic Isothiocyanates with Metabolizing Enzymes/Transporters 569
22.5 Conclusions and Future Directions 572
References 573
23 Anticipating and Minimizing Drug Interactions in a Drug Discovery and Development Setting: An Industrial Perspective 583
23.1 Introduction 584
23.2 Pharmacokinetic Framework 586
23.2.1 Existing Strategies for Prospective Prediction of DDIs and the Framework for Support of Discovery 586
23.2.2 The Impact of Inhibitor Concentration [I], In Vitro Parameters (k i , k inact , K I , E max , EC 50 ), and f m 0 f m,CYP on Predictions 590
23.2.3 Assessment of Drug Interaction Potential in Early Drug Discovery 592
23.3 DDI Suites at Bristol-Myers Squibb 592
23.3.1 P450 Inhibition 592
23.3.2 P450 Induction 594
23.3.2.1 Receptor Transactivation and Ligand-Binding Assays 594
23.3.2.2 Immortalized Cell Lines 596
23.3.2.3 Primary Human Hepatocytes 597
23.3.3 P-gp Substrate and Inhibition Assays 598
23.3.3.1 P-gp Assay Methodology 599
23.3.3.2 P-gp Substrate Assay 599
23.3.3.3 P-gp Inhibition Assay 600
23.3.4 P450 Substrate 601
23.3.5 OATP Substrate and Inhibition Assays 602
23.3.5.1 OATP Substrate Study 602
23.3.5.2 OATP Inhibition Study 603
23.3.6 Animal Models of CYP Induction and Inhibition 604
23.4 Lead Optimization 606
23.5 Lead Characterization and Beyond 608
23.5.1 Contexting of In Vitro and In Vivo DDI Data 608
23.5.2 Prioritization of Clinical Studies 609
23.5.3 Final Integration of Drug--Drug Interaction Data Set 610
23.6 Challenges of Predicting DDIs 610
23.6.1 Prospective vs. Retrospective In Vitro Studies 610
23.6.2 What [I] to Use Prior to First in Man? 611
23.6.3 Going Beyond P450s to Other Drug-Metabolizing Enzymes and Transporters 612
23.7 Conclusions 613
References 614
24 Clinical Studies of Drug--Drug Interactions: Designand Interpretation 623
24.1 Introduction 623
24.2 Epidemiology of DrugDrug Interactions 625
24.3 Drug Interaction Mechanisms and Terminology 627
24.4 The Design of Clinical Drug Interaction Studies 628
24.4.1 Study Rationale 629
24.4.2 Protocol Construction 630
24.4.3 Studies of Specific Drug Pairs 631
24.4.4 Candidate Drug as Victim 631
24.4.5 Candidate Drug as Perpetrator 632
24.4.6 Approach to Analysis of Data 635
24.5 Is a Drug Interaction of Clinical Importance? 637
24.6 Are Clinical Drug Interactions Predictable from In Vitro Models? 638
24.7 Boosting or Augmentation 641
References 641
25 Toxicological Consequences of DrugDrug Interactions 648
25.1 Introduction 648
25.1.1 Adverse Drug Reactions 648
25.1.2 Off-Target Toxicity -- Drug-Induced Liver Injury 649
25.1.3 The Role of Metabolism in DILI 649
25.1.4 The Role of Drug--Drug Interactions 651
25.2 Toxicological Consequences of DDI On-Target Toxicity 651
25.2.1 Examples of On-Target Toxicity 651
25.2.2 Anticoagulants 652
25.2.2.1 Enzyme Inhibition 652
25.2.2.2 Protein Binding 652
25.2.2.3 Effects of Anti-platelet Drugs 653
25.3 Toxicological Consequences of DDI Off-Target Toxicity 653
25.3.1 Examples of Off-Target Toxicity 653
25.3.1.1 Carbamazepine 653
25.3.1.2 Acetaminophen 654
25.3.1.3 Nevirapine 655
25.3.1.4 Anti-tuberculosis Drugs 656
25.3.2 Other Interactions 657
25.4 Conclusion 657
References 658
Part IV Regulatory Aspects and Future Developments Involving DDI 662
26 Complex Drug Interactions: Significance and Evaluation 663
26.1 Introduction 663
26.2 Intrinsic Clearance and Pharmacokinetic Outcome 665
26.2.1 Non-proportionality Between Organ Intrinsic Clearance and Organ Clearance 667
26.2.2 Fractional Metabolic Clearance ( f m ) 667
26.2.3 Drug Interaction Affecting First Pass Metabolism 670
26.2.4 Dynamic Nature of Drug Interaction 670
26.3 Examples of Different Types of Complex Drug Interactions 672
26.3.1 Two Interacting Drugs Affecting One Substrate 673
26.3.2 Drug Interaction in Patients with Organ Impairment 673
26.3.3 Inhibition of an Enzyme or Transporter in Poor Metabolizers of Another Pathway 674
26.3.4 Concurrent Inhibition and Induction 674
26.3.5 Inhibitory Metabolite(s) 675
26.4 Assessing Complex Drug Interactions Using Modeling and Simulation 676
26.4.1 Modeling and Simulation: Static Versus Dynamic Approaches 676
26.4.2 Assessing Complex Drug Interactions Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation 677
26.4.3 Applications of PBPK Approach in Studying Drug Interactions 678
26.5 Challenges in Evaluating Complex Drug Interactions 680
26.6 Scientific Perspectives on Studying Complex Drug Interactions 681
26.7 Conclusion 683
References 683
26.8 Useful Links 688
27 Drug--Drug Interactions: Communicating Post--market Drug Safety Information in the USA 689
27.1 Introduction 689
27.1.1 FDA Approved Drug Product Information 690
27.2 Patient Medication History Databases 692
27.3 DDI Databases 692
References 694
28 Drug--Drug Interactions: What Have We Learnedand Where Are We Going? 696
28.1 Drug Interactions Involving Metabolic Enzymes 697
28.1.1 P450s Versus Phase II Enzymes 698
28.1.2 Species Differences 700
28.1.3 Transgenic Animal Models 700
28.2 Drug Interactions Involving Transporters 701
28.2.1 Proteomics-Based Approach to Define Transporter Abundance in Tissues 701
28.2.2 Species Difference in Transport 701
28.2.3 Improved Tools to Examine Transporter Function 702
28.2.4 Improved Probes and Inhibitors to Examine Transporter Function 703
28.3 Improved Methods for the Interpretation of Drug Interaction Data 704
28.3.1 PBPK Modeling 704
28.3.2 In Vitro Estimates For In Vivo Extrapolation (IVIVE) 707
28.3.3 Software for Modeling 707
28.4 Difficulties Remaining 708
28.4.1 Multiple Interactions 708
28.4.2 Drug Interactions Involving Biologic Agents 709
28.4.3 Future Improvement of Prediction Strategies 709
References 710
Subject Index 718