Drug Metabolism Prediction -- Contents -- List of Contributors -- Preface -- A Personal Foreword -- Part One: Introduction -- 1 Metabolism in Drug Development -- 1.1 What? An Introduction -- 1.2 Why? Metabolism in Drug Development -- 1.2.1 The Pharmacological Context -- 1.2.2 Consequences of Drug Metabolism on Activity -- 1.2.3 Adverse Consequences of Drug Metabolism -- 1.2.4 Impact of Metabolism on Absorption, Distribution, and Excretion -- 1.3 How? From Experimental Results to Databases to Expert Software Packages -- 1.3.1 The Many Factors Influencing Drug Metabolism -- 1.3.2 Acquiring and Interpreting Experimental Results -- 1.3.3 Expert Software Tools and Their Domains of Applicability -- 1.3.4 Roads to Progress -- 1.4 Who? Human Intelligence as a Conclusion -- References -- Part Two: Software, Web Servers and Data Resources to Study Metabolism -- 2 Software for Metabolism Prediction -- 2.1 Introduction -- 2.2 Ligand-Based and Structure-Based Methods for Predicting Metabolism -- 2.3 Software for Predicting Sites of Metabolism -- 2.3.1 Knowledge-Based Systems -- 2.3.2 Molecular Interaction Fields -- 2.3.3 Docking -- 2.3.4 Reactivity Models -- 2.3.5 Data Mining and Machine Learning Approaches -- 2.3.6 Shape-Focused Approaches -- 2.4 Software for Predicting Metabolites -- 2.4.1 Knowledge-Based Systems -- 2.4.2 Data Mining and Machine Learning Approaches -- 2.4.3 Molecular Interaction Fields -- 2.5 Software for Predicting Interactions of Small Molecules with Metabolizing Enzymes -- 2.6 Conclusions -- References -- 3 Online Databases and Web Servers for Drug Metabolism Research -- 3.1 Introduction -- 3.2 Online Drug Metabolism Databases -- 3.2.1 DrugBank -- 3.2.2 HMDB -- 3.2.3 PharmGKB -- 3.2.4 Wikipedia -- 3.2.5 PubChem -- 3.2.6 Synoptic Databases: ChEBI, ChEMBL, KEGG, and BindingDB.

3.2.7 Specialized Databases: UM-BBD, SuperCYP, PKKB, and PK/DB -- 3.2.8 Online Database Summary -- 3.3 Online Drug Metabolism Prediction Servers -- 3.3.1 Metabolite Predictors -- 3.3.2 SoM Predictors -- 3.3.3 Specialized Predictors -- 3.3.4 ADMET Predictors -- 3.3.5 Web Server Summary -- References -- Part Three: Computational Approaches to Study Cytochrome P450 Enzymes -- 4 Structure and Dynamics of Human Drug-Metabolizing Cytochrome P450 Enzymes -- 4.1 Introduction -- 4.2 Three-Dimensional Structures of Human CYPs -- 4.3 Structural Features of CYPs -- 4.3.1 CYP-Electron Transfer Protein Interactions -- 4.3.2 Substrate Recognition Sites -- 4.3.3 Structural Variability and Substrate Specificity Profiles -- 4.3.3.1 CYP1A2 -- 4.3.3.2 CYP2A6 -- 4.3.3.3 CYP2C9 -- 4.3.3.4 CYP2D6 -- 4.3.3.5 CYP2E1 -- 4.3.3.6 CYP3A4 -- 4.4 Dynamics of CYPs -- 4.4.1 Active Site Flexibility -- 4.4.2 Active Site Solvation -- 4.4.3 Active Site Access and Egress Pathways -- 4.4.4 MD Simulations of CYPs in Lipid Bilayers -- 4.5 Conclusions -- References -- 5 Cytochrome P450 Substrate Recognition and Binding -- 5.1 Introduction -- 5.2 Substrate Recognition in the Catalytic Cycle of CYPs -- 5.3 Substrate Identity in Various Species -- 5.4 Structural Insight into Substrate Recognition by CYPs -- 5.4.1 CYP1A1, CYP1A2, and CYP1B1 -- 5.4.2 CYP2A6 -- 5.4.3 CYP2A13 -- 5.4.4 CYP2C8 -- 5.4.5 CYP2C9 -- 5.4.6 CYP2D6 -- 5.4.7 CYP2E1 -- 5.4.8 CYP2R1 -- 5.4.9 CYP3A4 -- 5.4.10 CYP8A1 -- 5.4.11 CYP11A1 -- 5.4.12 CYP11B2 -- 5.4.13 CYP19A1 -- 5.4.14 CYP46A1 -- 5.4.15 General Insights from Protein-Ligand Crystal Structures -- 5.5 The Challenges of Using Docking for Predicting Kinetic Parameters -- 5.6 Substrate Properties for Various Human Isoforms -- 5.6.1 Kinetic Parameters Km and kcat and Their Relationship with Substrate and Protein Structure -- 5.7 Conclusions -- References.

6 QM/MM Studies of Structure and Reactivity of Cytochrome P450 Enzymes: Methodology and Selected Applications -- 6.1 Introduction -- 6.2 QM/MM Methods -- 6.2.1 Methodological Issues in QM/MM Studies -- 6.2.1.1 QM/MM Partitioning -- 6.2.1.2 QM Methods -- 6.2.1.3 MM Methods -- 6.2.1.4 Subtractive versus Additive QM/MM Schemes -- 6.2.1.5 Electrostatic QM/MM Interactions -- 6.2.1.6 QM/MM Boundary Treatments -- 6.2.1.7 QM/MM Geometry Optimization -- 6.2.1.8 QM/MM Molecular Dynamics and Free Energy Calculations -- 6.2.1.9 QM/MM Energy versus Free Energy Calculations -- 6.2.2 Practical Issues in QM/MM Studies -- 6.2.2.1 QM/MM Software -- 6.2.2.2 QM/MM Setup -- 6.2.2.3 Accuracy of QM/MM Results -- 6.2.2.4 QM/MM Geometry Optimization -- 6.2.2.5 Extracting Insights from QM/MM Calculations -- 6.3 Selected QM/MM Applications to Cytochrome P450 Enzymes -- 6.3.1 Formation of Cpd I from Cpd 0 -- 6.3.1.1 Conversion of Cpd 0 into Cpd I in the T252X Mutants -- 6.3.2 Properties of Cpd I -- 6.3.2.1 Cpd I Species of Different Cytochrome P450s -- 6.3.3 The Mechanism of Cytochrome P450 StaP -- 6.3.4 The Mechanism of Dopamine Formation -- 6.3.4.1 The Electrostatic Effect is Not Due to Simple Bulk Polarity -- 6.4 An Overview of Cytochrome P450 Function Requires Reliable MD Calculations -- 6.5 Conclusions -- References -- 7 Computational Free Energy Methods for Ascertaining Ligand Interaction with Metabolizing Enzymes -- 7.1 Introduction -- 7.2 Linking Experiment and Simulation: Statistical Mechanics -- 7.2.1 A Note on Chemical Transformations -- 7.3 Taxonomy of Free Energy Methods -- 7.3.1 Pathway Methods -- 7.3.1.1 Pathway Planning: Using the State Nature of the Free Energy Cycle -- 7.3.1.2 Free Energy Perturbation -- 7.3.1.3 Bennett Acceptance Ratio -- 7.3.1.4 Thermodynamic Integration -- 7.3.2 Endpoint Methods.

7.3.2.1 Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) -- 7.3.2.2 Linear Interaction Energy -- 7.3.2.3 QM Endpoint Methods -- 7.3.3 Summary of Free Energy Methods -- 7.4 Ligand Parameterization -- 7.5 Specific Examples -- 7.5.1 Cytochrome P450 (CYP) -- 7.5.2 Chorismate Mutase -- 7.6 Conclusions -- References -- 8 Experimental Approaches to Analysis of Reactions of Cytochrome P450 Enzymes -- 8.1 Introduction -- 8.2 Structural Data and Substrate Binding -- 8.3 Systems for Production of Reaction Products and Analysis of Systems -- 8.3.1 In Vivo Systems -- 8.3.2 Tissue Microsomal Systems -- 8.3.3 Purified CYPs in Reconstituted Systems -- 8.3.4 Membranes from Heterologous Expression Systems -- 8.3.4.1 Mammalian Cells -- 8.3.4.2 Insect Cell Systems (Using Baculovirus Infection for Expression) -- 8.3.4.3 Microbial Membrane Systems -- 8.4 Methods for Analysis of Products of Drugs -- 8.4.1 Separation Methods -- 8.4.1.1 High-Performance Liquid Chromatography -- 8.4.1.2 Other Separation Methods -- 8.4.2 Analysis Methods -- 8.4.2.1 HPLC-UV -- 8.4.2.2 LC-MS -- 8.4.2.3 LC-MS/MS -- 8.4.2.4 LC-HRMS -- 8.4.2.5 NMR -- 8.4.2.6 Other Spectroscopy of Metabolites -- 8.5 Untargeted Searches for CYP Reactions -- 8.6 Complex CYP Products -- 8.7 Structure-Activity Relationships Based on Products -- 8.7.1 SARs Based on Chemical Bond Energy -- 8.7.2 SARs Based on Docking -- 8.7.3 Knowledge-Based SAR -- 8.8 SAR of Reaction Rates -- 8.9 Other Issues in Predictions -- 8.10 Conclusions -- References -- Part Four: Computational Approaches to Study Sites and Products of Metabolism -- 9 Molecular Interaction Fields for Predicting the Sites and Products of Metabolism -- 9.1 Introduction -- 9.2 CYP from a GRID Perspective -- 9.3 From Lead Optimization to Preclinical Phases: the Challenge of SoM Prediction -- 9.3.1 MetaSite: Accessibility Function.

9.3.2 MetaSite: Reactivity Function -- 9.3.3 MetaSite: Site of Metabolism Prediction -- 9.3.4 MetaSite: Validation and Case Studies -- 9.3.5 MetaSite: Prediction of CYP Inhibition -- 9.3.6 MassMetaSite: Automated Metabolite Identification -- 9.4 Conclusions -- References -- 10 Structure-Based Methods for Predicting the Sites and Products of Metabolism -- 10.1 Introduction -- 10.2 6 Å Rule -- 10.3 Methodological Approaches -- 10.4 Prediction of Binding Poses -- 10.5 Protein Flexibility -- 10.6 Role of Water Molecules -- 10.7 Effect of Mutations -- 10.8 Conclusions -- References -- 11 Reactivity-Based Approaches and Machine Learning Methods for Predicting the Sites of Cytochrome P450-Mediated Metabolism -- 11.1 Introduction -- 11.2 Reactivity Models for CYP Reactions -- 11.2.1 Hydroxylation of Aliphatic Carbon Atoms -- 11.2.2 Hydroxylation and Epoxidation of Aromatic and Double Bonded Carbon Atoms -- 11.2.3 Combined Carbon Atom Models -- 11.2.4 Comprehensive Models -- 11.3 Reactivity-Based Methods Applied to CYP-Mediated Site of Metabolism Prediction -- 11.3.1 Methods Only Applicable to Carbon Atoms -- 11.3.2 Comprehensive Methods -- 11.4 Machine Learning Methods Applied to CYP-Mediated Site of Metabolism Prediction -- 11.4.1 Atomic Descriptors -- 11.4.2 Machine Learning Methods and Optimization Criteria -- 11.5 Applications to SoM Prediction -- 11.5.1 Isoform-Specific Models -- 11.5.2 Isoform-Unspecific Models -- 11.6 Combinations of Structure-Based Models and Reactivity -- 11.7 Conclusions -- References -- 12 Knowledge-Based Approaches for Predicting the Sites and Products of Metabolism -- 12.1 Introduction -- 12.2 Building and Maintaining a Knowledge Base -- 12.3 Encoding Rules in a Knowledge Base -- 12.4 Ways of Working with Rules -- 12.5 Using the Logic of Argumentation -- 12.6 Combining Absolute and Relative Reasoning.

12.7 Combining Predictions from Multiple Sources.