Drug Metabolism Prediction

The first professional reference on this highly relevant topic, for drug developers, pharmacologists and toxicologists. The authors provide more than a systematic overview of computational tools and knowledge bases for drug metabolism research and their underlying principles. They aim to convey their expert knowledge distilled from many years of experience in the field. In addition to the fundamentals, computational approaches and their applications, this volume provides expert accounts of the latest experimental methods for investigating drug metabolism in four dedicated chapters. The authors discuss the most important caveats and common errors to consider when working with experimental data. Collating the knowledge gained over the past decade, this practice–oriented guide presents methods not only used in drug development, but also in the development and toxicological assessment of cosmetics, functional foods, agrochemicals, and additives for consumer goods, making it an invaluable reference in a variety of disciplines.

List of Contributors XVII Preface XXI A Personal Foreword XXIII Part One Introduction 1 1 Metabolism in Drug Development 3 Bernard Testa 1.1 What? An Introduction 3 1.2 Why? Metabolism in Drug Development 4 1.2.1 The Pharmacological Context 4 1.2.2 Consequences of Drug Metabolism on Activity 6 1.2.3 Adverse Consequences of Drug Metabolism 7 1.2.4 Impact of Metabolism on Absorption, Distribution, and Excretion 10 1.3 How? From Experimental Results to Databases to Expert Software Packages 11 1.3.1 The Many Factors Influencing Drug Metabolism 11 1.3.2 Acquiring and Interpreting Experimental Results 13 1.3.3 Expert Software Tools and Their Domains of Applicability 14 1.3.4 Roads to Progress 16 1.4 Who? Human Intelligence as a Conclusion 17 References 19 Part Two Software, Web Servers and Data Resources to Study Metabolism 27 2 Software for Metabolism Prediction 29 Lu Tan and Johannes Kirchmair 2.1 Introduction 29 2.2 Ligand–Based and Structure–Based Methods for Predicting Metabolism 30 2.3 Software for Predicting Sites of Metabolism 38 2.3.1 Knowledge–Based Systems 38 2.3.2 Molecular Interaction Fields 39 2.3.3 Docking 39 2.3.4 Reactivity Models 40 2.3.5 Data Mining and Machine Learning Approaches 41 2.3.6 Shape–Focused Approaches 42 2.4 Software for Predicting Metabolites 43 2.4.1 Knowledge–Based Systems 44 2.4.2 Data Mining and Machine Learning Approaches 46 2.4.3 Molecular Interaction Fields 46 2.5 Software for Predicting Interactions of Small Molecules with Metabolizing Enzymes 46 2.6 Conclusions 48 References 49 3 Online Databases and Web Servers for Drug Metabolism Research 53 David S. Wishart 3.1 Introduction 53 3.2 Online Drug Metabolism Databases 54 3.2.1 DrugBank 57 3.2.2 HMDB 59 3.2.3 PharmGKB 59 3.2.4 Wikipedia 60 3.2.5 PubChem 61 3.2.6 Synoptic Databases: ChEBI, ChEMBL, KEGG, and BindingDB 61 3.2.7 Specialized Databases: UM–BBD, SuperCYP, PKKB, and PK/DB 63 3.2.8 Online Database Summary 64 3.3 Online Drug Metabolism Prediction Servers 65 3.3.1 Metabolite Predictors 66 3.3.2 SoM Predictors 66 3.3.3 Specialized Predictors 68 3.3.4 ADMET Predictors 70 3.3.5 Web Server Summary 71 References 71 Part Three Computational Approaches to Study Cytochrome P450 Enzymes 75 4 Structure and Dynamics of Human Drug–Metabolizing Cytochrome P450 Enzymes 77 Ghulam Mustafa, Xiaofeng Yu, and Rebecca C. Wade 4.1 Introduction 77 4.2 Three–Dimensional Structures of Human CYPs 78 4.3 Structural Features of CYPs 78 4.3.1 CYP–Electron Transfer Protein Interactions 81 4.3.2 Substrate Recognition Sites 82 4.3.3 Structural Variability and Substrate Specificity Profiles 83 4.3.3.1 CYP1A2 83 4.3.3.2 CYP2A6 85 4.3.3.3 CYP2C9 85 4.3.3.4 CYP2D6 86 4.3.3.5 CYP2E1 87 4.3.3.6 CYP3A4 87 4.4 Dynamics of CYPs 88 4.4.1 Active Site Flexibility 88 4.4.2 Active Site Solvation 93 4.4.3 Active Site Access and Egress Pathways 93 4.4.4 MD Simulations of CYPs in Lipid Bilayers 96 4.5 Conclusions 96 References 97 5 Cytochrome P450 Substrate Recognition and Binding 103 Andrew G. Leach and Nathan J. Kidley 5.1 Introduction 103 5.2 Substrate Recognition in the Catalytic Cycle of CYPs 103 5.3 Substrate Identity in Various Species 104 5.4 Structural Insight into Substrate Recognition by CYPs 107 5.4.1 CYP1A1, CYP1A2, and CYP1B1 108 5.4.2 CYP2A6 108 5.4.3 CYP2A13 109 5.4.4 CYP2C8 110 5.4.5 CYP2C9 112 5.4.6 CYP2D6 112 5.4.7 CYP2E1 113 5.4.8 CYP2R1 113 5.4.9 CYP3A4 115 5.4.10 CYP8A1 115 5.4.11 CYP11A1 116 5.4.12 CYP11B2 118 5.4.13 CYP19A1 118 5.4.14 CYP46A1 119 5.4.15 General Insights from Protein–Ligand Crystal Structures 119 5.5 The Challenges of Using Docking for Predicting Kinetic Parameters 120 5.6 Substrate Properties for Various Human Isoforms 120 5.6.1 Kinetic Parameters Km and kcat and Their Relationship with Substrate and Protein Structure 124 5.7 Conclusions 128 References 128 6 QM/MM Studies of Structure and Reactivity of Cytochrome P450 Enzymes: Methodology and Selected Applications 133 Sason Shaik, Hui Chen, Dandamudi Usharani, and Walter Thiel 6.1 Introduction 133 6.2 QM/MM Methods 135 6.2.1 Methodological Issues in QM/MM Studies 136 6.2.1.1 QM/MM Partitioning 136 6.2.1.2 QM Methods 137 6.2.1.3 MM Methods 138 6.2.1.4 Subtractive versus Additive QM/MM Schemes 139 6.2.1.5 Electrostatic QM/MM Interactions 139 6.2.1.6 QM/MM Boundary Treatments 139 6.2.1.7 QM/MM Geometry Optimization 140 6.2.1.8 QM/MM Molecular Dynamics and Free Energy Calculations 140 6.2.1.9 QM/MM Energy versus Free Energy Calculations 141 6.2.2 Practical Issues in QM/MM Studies 141 6.2.2.1 QM/MM Software 141 6.2.2.2 QM/MM Setup 142 6.2.2.3 Accuracy of QM/MM Results 143 6.2.2.4 QM/MM Geometry Optimization 143 6.2.2.5 Extracting Insights from QM/MM Calculations 144 6.3 Selected QM/MM Applications to Cytochrome P450 Enzymes 144 6.3.1 Formation of Cpd I from Cpd 0 146 6.3.1.1 Conversion of Cpd 0 into Cpd I in the T252X Mutants 148 6.3.2 Properties of Cpd I 151 6.3.2.1 Cpd I Species of Different Cytochrome P450s 154 6.3.3 The Mechanism of Cytochrome P450 StaP 155 6.3.4 The Mechanism of Dopamine Formation 160 6.3.4.1 The Electrostatic Effect is Not Due to Simple Bulk Polarity 163 6.4 An Overview of Cytochrome P450 Function Requires Reliable MD Calculations 163 6.5 Conclusions 164 References 165 7 Computational Free Energy Methods for Ascertaining Ligand Interaction with Metabolizing Enzymes 179 Mark J. Williamson 7.1 Introduction 179 7.2 Linking Experiment and Simulation: Statistical Mechanics 180 7.2.1 A Note on Chemical Transformations 182 7.3 Taxonomy of Free Energy Methods 183 7.3.1 Pathway Methods 183 7.3.1.1 Pathway Planning: Using the State Nature of the Free Energy Cycle 184 7.3.1.2 Free Energy Perturbation 185 7.3.1.3 Bennett Acceptance Ratio 185 7.3.1.4 Thermodynamic Integration 186 7.3.2 Endpoint Methods 186 7.3.2.1 Molecular Mechanics–Generalized Born Surface Area (MM–GBSA) 186 7.3.2.2 Linear Interaction Energy 187 7.3.2.3 QM Endpoint Methods 187 7.3.3 Summary of Free Energy Methods 187 7.4 Ligand Parameterization 188 7.5 Specific Examples 189 7.5.1 Cytochrome P450 (CYP) 189 7.5.2 Chorismate Mutase 192 7.6 Conclusions 192 References 193 8 Experimental Approaches to Analysis of Reactions of Cytochrome P450 Enzymes 199 Frederick Peter Guengerich 8.1 Introduction 199 8.2 Structural Data and Substrate Binding 199 8.3 Systems for Production of Reaction Products and Analysis of Systems 200 8.3.1 In Vivo Systems 201 8.3.2 Tissue Microsomal Systems 201 8.3.3 Purified CYPs in Reconstituted Systems 201 8.3.4 Membranes from Heterologous Expression Systems 202 8.3.4.1 Mammalian Cells 202 8.3.4.2 Insect Cell Systems (Using Baculovirus Infection for Expression) 202 8.3.4.3 Microbial Membrane Systems 202 8.4 Methods for Analysis of Products of Drugs 203 8.4.1 Separation Methods 203 8.4.1.1 High–Performance Liquid Chromatography 203 8.4.1.2 Other Separation Methods 204 8.4.2 Analysis Methods 204 8.4.2.1 HPLC–UV 204 8.4.2.2 LC–MS 205 8.4.2.3 LC–MS/MS 205 8.4.2.4 LC–HRMS 205 8.4.2.5 NMR 205 8.4.2.6 Other Spectroscopy of Metabolites 206 8.5 Untargeted Searches for CYP Reactions 208 8.6 Complex CYP Products 208 8.7 Structure–Activity Relationships Based on Products 210 8.7.1 SARs Based on Chemical Bond Energy 211 8.7.2 SARs Based on Docking 211 8.7.3 Knowledge–Based SAR 212 8.8 SAR of Reaction Rates 213 8.9 Other Issues in Predictions 213 8.10 Conclusions 214 References 214 Part Four Computational Approaches to Study Sites and Products of Metabolism 221 9 Molecular Interaction Fields for Predicting the Sites and Products of Metabolism 223 Fabio Broccatelli and Nathan Brown 9.1 Introduction 223 9.2 CYP from a GRID Perspective 224 9.3 From Lead Optimization to Preclinical Phases: the Challenge of SoM Prediction 226 9.3.1 MetaSite: Accessibility Function 227 9.3.2 MetaSite: Reactivity Function 229 9.3.3 MetaSite: Site of Metabolism Prediction 230 9.3.4 MetaSite: Validation and Case Studies 231 9.3.5 MetaSite: Prediction of CYP Inhibition 234 9.3.6 MassMetaSite: Automated Metabolite Identification 236 9.4 Conclusions 239 References 241 10 Structure–Based Methods for Predicting the Sites and Products of Metabolism 243 Chris Oostenbrink 10.1 Introduction 243 10.2 6 Å Rule 243 10.3 Methodological Approaches 245 10.4 Prediction of Binding Poses 247 10.5 Protein Flexibility 249 10.6 Role of Water Molecules 254 10.7 Effect of Mutations 256 10.8 Conclusions 258 References 259 11 Reactivity–Based Approaches and Machine Learning Methods for Predicting the Sites of Cytochrome P450–Mediated Metabolism 265 Patrik Rydberg 11.1 Introduction 265 11.2 Reactivity Models for CYP Reactions 268 11.2.1 Hydroxylation of Aliphatic Carbon Atoms 268 11.2.2 Hydroxylation and Epoxidation of Aromatic and Double Bonded Carbon Atoms 271 11.2.3 Combined Carbon Atom Models 273 11.2.4 Comprehensive Models 273 11.3 Reactivity–Based Methods Applied to CYP–Mediated Site of Metabolism Prediction 274 11.3.1 Methods Only Applicable to Carbon Atoms 274 11.3.2 Comprehensive Methods 276 11.4 Machine Learning Methods Applied to CYP–Mediated Site of Metabolism Prediction 278 11.4.1 Atomic Descriptors 278 11.4.2 Machine Learning Methods and Optimization Criteria 279 11.5 Applications to SoM Prediction 280 11.5.1 Isoform–Specific Models 281 11.5.2 Isoform–Unspecific Models 283 11.6 Combinations of Structure–Based Models and Reactivity 284 11.7 Conclusions 285 References 286 12 Knowledge–Based Approaches for Predicting the Sites and Products of Metabolism 293 Philip Neville Judson 12.1 Introduction 293 12.2 Building and Maintaining a Knowledge Base 295 12.3 Encoding Rules in a Knowledge Base 299 12.4 Ways of Working with Rules 301 12.5 Using the Logic of Argumentation 303 12.6 Combining Absolute and Relative Reasoning 307 12.7 Combining Predictions from Multiple Sources 310 12.8 Validation and Assessment of Performance 312 12.9 Conclusions 314 References 314 Part Five Computational Approaches to Study Enzyme Inhibition and Induction 319 13 Quantitative Structure–Activity Relationship (QSAR) Methods for the Prediction of Substrates, Inhibitors, and Inducers of Metabolic Enzymes 321 Oraphan Phuangsawai, Supa Hannongbua, and Mathew Paul Gleeson 13.1 Introduction 321 13.2 In Silico QSAR Methods 322 13.2.1 Experimental Variability 323 13.2.2 Data Curation and Manipulation 324 13.2.3 Molecular Descriptors 324 13.2.4 Training SAR, QSAR, and Machine Learning Models 325 13.2.5 Local versus Global QSAR Models 325 13.2.6 SAR and Classical QSAR Methods 326 13.2.7 Machine Learning QSAR Methods 327 13.2.8 Model Assessment and Validation 327 13.2.8.1 Assessing the Predictive Ability of QSAR Models 327 13.2.8.2 Applicability Domains of QSAR Models 328 13.3 QSAR Models for Cytochrome P450 328 13.3.1 Inhibition QSAR 328 13.3.1.1 SAR 328 13.3.1.2 Classical QSAR Models 329 13.3.1.3 Machine Learning QSAR Models 333 13.3.1.4 Classification Models 334 13.3.1.5 3D QSAR Models 335 13.3.2 Enzyme Induction QSAR 336 13.4 Conjugative Metabolizing Enzymes 337 13.4.1 Uridine Diphosphate Glucosyltransferase (UGT) QSAR 338 13.4.2 Sulfotransferases QSAR 338 13.5 In Vitro Clearance QSAR 339 13.6 Conclusions 340 References 341 14 Pharmacophore–Based Methods for Predicting the Inhibition and Induction of Metabolic Enzymes 351 Teresa Kaserer, Veronika Temml, and Daniela Schuster 14.1 Introduction 351 14.2 Substrate and Inhibitor Pharmacophore Models 354 14.2.1 Cytochrome P450 enzymes 354 14.2.1.1 CYP1A2 354 14.2.1.2 CYP2B6 355 14.2.1.3 CYP2C9 356 14.2.1.4 CYP2C19 357 14.2.1.5 CYP2D6 358 14.2.1.6 CYP3A4 359 14.2.1.7 CYP3A5 and CYP3A7 360 14.2.2 UDP–Glucuronosyltransferases (UGTs) 361 14.2.2.1 UGT1A1 361 14.2.2.2 UGT1A4 361 14.2.2.3 UGT1A9 361 14.2.2.4 UGT2B7 362 14.2.3 Interference with Recently Identified Phase I Metabolic Enzymes 362 14.3 Inducer Models 363 14.3.1 Hetero– and Autoactivation 363 14.3.1.1 CYP2C9 363 14.3.1.2 CYP3A4 364 14.3.2 Nuclear Receptors 364 14.3.2.1 Pregnane X Receptor 364 14.3.2.2 CAR 366 14.4 Conclusions 366 References 368 15 Prediction of Phosphoglycoprotein (P–gp)–Mediated Disposition in Early Drug Discovery 373 Simon Thomas and Richard J. Dimelow 15.1 Introduction 373 15.2 QSAR Modeling of Compounds Interacting with Transporters 376 15.2.1 Experimental Data and Assays 376 15.2.2 Descriptors Used in P–gp Substrate Identification 378 15.2.3 QSAR Methods Used in P–gp Substrate Identification 380 15.3 Influence of Compound Structure on P–gp Substrate Identity 380 15.4 QSAR Models for P–gp Substrates 385 15.5 Application to Drug Discovery 388 15.6 Conclusions 391 References 392 16 Predicting Toxic Effects of Metabolites 397 Andreas Bender 16.1 Introduction 397 16.2 Methods for Predicting Toxic Effects 401 16.2.1 Predicting Metabolites 401 16.2.2 Predicting Relative and Absolute Metabolism Likelihoods and Rates 401 16.2.3 Utilizing Pharmacogenetic Data to Anticipate Dose, Rate, and Time Information in an Individual Patient 402 16.2.4 Predicting the Effect of the Resulting Metabolites 402 16.2.4.1 Bioactivity–Based Mechanistic Models 403 16.2.4.2 Incorporating Pathway Information into Toxicity Models 404 16.2.4.3 Toxicogenetic and Pharmacogenomic Approaches 406 16.2.4.4 Knowledge–Based Systems 407 16.2.4.5 Reactive Metabolites 407 16.2.5 Current Scientific and Political Developments Regarding Metabolism and Toxicity Prediction 408 16.3 Conclusions 408 References 409 Part Six Experimental Approaches to Study Metabolism 413 17 In Vitro Models for Metabolism: Applicability for Research on Food Bioactives 415 Natalie D. Glube and Guus Duchateau 17.1 Introduction 415 17.1.1 Bioavailability 416 17.1.2 Intestinal Absorption 416 17.1.3 First–Pass Metabolism 418 17.2 Classification of In Vitro Models for Metabolism 418 17.3 Modifications via Gut (Colon) Microflora 419 17.3.1 Background Information 419 17.3.2 In Vitro Models 420 17.3.2.1 Fecal Slurry 421 17.3.2.2 Isolated Pure Bacterial Cultures 421 17.3.2.3 Complex Intestinal Models (TIM–2) 421 17.4 Intestinal (Gut Wall) Metabolism 421 17.4.1 Background Information 421 17.4.2 In Vitro Models 422 17.4.2.1 Tissue Intact Models 423 17.4.2.2 Subcellular and Cellular Models 423 17.5 Hepatic Metabolism 423 17.5.1 Background Information 423 17.5.2 In Vitro Models 424 17.5.2.1 Supersomes: Recombinant Phase I and Phase II Enzymes 424 17.5.2.2 Microsomes 424 17.5.2.3 S9 Fractions 426 17.5.2.4 Hepatocyte Cell Lines 426 17.5.2.5 Primary Cultures: Cryopreserved Hepatocytes 427 17.5.2.6 Cryopreserved Hepatocytes versus Microsomes 428 17.5.2.7 Hepatocytes in Culture 429 17.6 Pharmacokinetic Data Obtainable from In Vitro Metabolism Models 431 17.6.1 Pharmacokinetic Analysis 431 17.6.1.1 Measurement Methodology: Substrate Depletion versus Metabolite Formation 432 17.6.1.2 Mathematical Models for Metabolism: Well–Stirred, Parallel Tube, and Dispersion Models 432 17.7 Assay Validation 433 17.7.1 Selection and Preparation of Reference Compounds 433 17.7.2 Analytics 434 17.7.3 Theoretical Steps to Establish an In Vitro Model 434 17.8 Conclusions 435 17.8.1 What Can We Summarize from the Literature? 435 17.8.2 What Questions We Wish to Have Answered Will Determine Which Model We Select 436 References 438 18 In Vitro Approaches to Study Drug–Drug Interactions 441 Stephen S. Ferguson and Jessica A. Bonzo 18.1 Introduction 441 18.1.1 Additional Factors Influencing Drug Metabolism 442 18.2 Inhibition of Drug Metabolism 444 18.2.1 In Vitro Models for Predicting Inhibition of Drug Metabolism 444 18.2.1.1 Human Liver Microsomes 445 18.2.1.2 S9 and Cytosol 456 18.2.1.3 Recombinant Enzymes 457 18.2.1.4 Primary Hepatocytes 458 18.3 Transcriptional Regulation of Metabolism 460 18.3.1 Gene Induction Pathways 460 18.3.2 Gene Repression/Suppression 462 18.3.3 In Vitro Models for Predicting Induction of Drug Metabolism Enzymes 463 18.3.3.1 Ligand Binding Assays 463 18.3.3.2 Gene Reporter Assays 465 18.3.3.3 Cellular Models for Induction Studies 466 18.3.3.4 Induction Assays in Cellular Models 468 18.3.3.5 Treatment with Control and Test Compounds 470 18.3.3.6 Gene Expression in Cellular Models for Induction 471 18.3.3.7 Enzymatic Activity in Metabolically Competent Cellular Models of Induction 474 18.4 Next–Generation Models and Concluding Remarks 474 References 477 19 Metabolite Detection and Profiling 485 Ian D. Wilson 19.1 Introduction 485 19.2 Chromatography 486 19.3 Mass Spectrometry 487 19.4 Sample Preparation for LC–MS–Based Metabolite Profiling 490 19.5 Metabolic Profiling by LC–MS 491 19.5.1 Metabolic Stability and Cytochrome P450 Inhibition Assays 491 19.5.2 Metabolite Profiling, Detection, and Identification from In Vivo and In Vitro Studies 492 19.5.3 Reactive Metabolite Detection 496 19.6 Conclusions 496 References 497 Index 499

Dr. Johannes Kirchmair is currently a lead researcher at the Institute of Pharmaceutical Sciences at ETH Zurich, Switzerland. He received his PhD in medicinal chemistry from the University of Innsbruck, Austria, and subsequently worked as an application scientist for Inte:Ligand in Vienna, Austria, before returning to his Alma Mater as an Assistant Professor. In 2009 he joined BASF SE Ludwigshafen, Germany, where he was responsible for the computational optimization of fungicide leads. From 2010 to 2013 he worked as a senior research associate at the Unilever Centre for Molecular Sciences Informatics, University of Cambridge (UK), where he developed computational methods for drug metabolism prediction. His main research interests include molecular informatics, bioinformatics, medicinal chemistry, and drug design.