Sean Ekins, MSc, PhD, DSc has over 20 years of pharmaceutical and toxicology experience. He is the founder or co-founder of two companies and Adjunct Professor at three universities. He has been awarded 16 NIH grants as Principal Investigator. He has authored or co authored over 285 peer-reviewed papers and book chapters and edited five books with Wiley. His research is focused on collaborations to facilitate rare and neglected disease drug discovery.

• List of Contributors xviiPreface xxiAcknowledgments xxiiiPart I Computational Methods 11 AccessibleMachine Learning Approaches for Toxicology 3Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery Tkachenko1.1 Introduction 31.2 Bayesian Models 51.2.1 CDD Models 71.3 Deep LearningModels 131.4 Comparison of Different Machine LearningMethods 161.4.1 Classic Machine LearningMethods 171.4.1.1 Bernoulli Naive Bayes 171.4.1.2 Linear Logistic Regression with Regularization 181.4.1.3 AdaBoost Decision Tree 181.4.1.4 Random Forest 181.4.1.5 Support Vector Machine 191.4.2 Deep Neural Networks 191.4.3 Comparing Models 201.5 FutureWork 21Acknowledgments 21References 212 Quantum Mechanics Approaches in Computational Toxicology 31Jakub Kostal2.1 Translating Computational Chemistry to Predictive Toxicology 312.2 Levels of Theory in Quantum Mechanical Calculations 332.3 Representing Molecular Orbitals 382.4 Hybrid Quantum and Molecular Mechanical Calculations 392.5 Representing System Dynamics 402.6 Developing QM Descriptors 422.6.1 Global Electronic Parameters 422.6.1.1 Electrostatic Potential, Dipole, and Polarizability 432.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 452.6.2 Local (Atom-Based) Electronic Parameters 472.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 482.6.2.2 Partial Atomic Charges 512.6.2.3 Hydrogen-Bonding Interactions 512.6.2.4 Bond Enthalpies 532.6.3 Modeling Chemical Reactions 532.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 562.6.5 Medium Effects and Hydration Models 592.7 Rational Design of Safer Chemicals 61References 64Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 693 Computational Approaches for Predicting hERG Activity 71Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade3.1 Introduction 713.2 Computational Approaches 733.3 Ligand-Based Approaches 733.4 Structure-Based Approaches 773.5 Applications to Predict hERG Blockage 773.5.1 Pred-hERGWeb App 793.6 Other Computational Approaches Related to hERG Liability 823.7 Final Remarks 83References 834 Computational Toxicology for Traditional Chinese Medicine 93Ni Ai and Xiaohui Fan4.1 Background, Current Status, and Challenges 934.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 994.2.1 Introduction to OAT1 and TCM 994.2.2 Construction of TCM Compound Database 1014.2.3 OAT1 Inhibitor Pharmacophore Development 1014.2.4 External Test Set Evaluation 1024.2.5 Database Searching 1024.2.6 Results: OAT1 Inhibitor Pharmacophore 1034.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 1044.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 1044.2.9 Discussion 1104.3 Conclusion 114Acknowledgment 114References 1145 PharmacophoreModels for Toxicology Prediction 121Daniela Schuster5.1 Introduction 1215.2 Antitarget Screening 1255.3 Prediction of Liver Toxicity 1255.4 Prediction of Cardiovascular Toxicity 1275.5 Prediction of Central Nervous System (CNS) Toxicity 1285.6 Prediction of Endocrine Disruption 1305.7 Prediction of ADME 1355.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136References 1376 Transporters in Hepatotoxicity 145Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker6.1 Introduction 1456.2 Basolateral Transporters 1466.3 Canalicular Transporters 1486.4 Data Sources for Transporters in Hepatotoxicity 1486.5 In Silico Transporters Models 1506.6 Ligand-Based Approaches 1506.7 OATP1B1 and OATP1B3 1506.8 NTCP 1546.9 OCT1 1546.10 OCT2 1546.11 MRP1, MRP3, and MRP4 1556.12 BSEP 1556.13 MRP2 1566.14 MDR1/P-gp 1566.15 MDR3 1576.16 BCRP 1576.17 MATE1 1586.18 ASBT 1596.19 Structure-Based Approaches 1596.20 Complex Models Incorporating Transporter Information 1606.21 In Vitro Models 1606.22 Multiscale Models 1616.23 Outlook 162Acknowledgments 164References 1647 Cheminformatics in a Clinical Setting 175Matthew D. Krasowski and Sean Ekins7.1 Introduction 1757.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 1777.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 1877.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 1917.5 Cheminformatics Applied to "Designer Drugs" 1957.6 Relevance to Antibody-Ligand Interactions 2027.7 Conclusions and Future Directions 203Acknowledgment 204References 204Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 2118 Computational Tools for ADMET Profiling 213Denis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann Richard, and Alexander Tropsha8.1 Introduction 2138.2 Cheminformatics Approaches for ADMET Profiling 2148.2.1 Chemical Data Curation Prior to ADMET Modeling 2158.2.2 QSAR Modelability Index 2178.2.3 Predictive QSAR Model DevelopmentWorkflow 2188.2.4 Hybrid QSAR Modeling 2208.2.4.1 Simple Consensus 2238.2.4.2 Mixed Chemical and Biological Features 2238.2.4.3 Two-Step HierarchicalWorkflow 2248.2.5 Chemical Biological Read-Across 2268.2.6 Public Chemotype Approach to Data-Mining 2298.3 Unsolved Challenges in Structure Based Profiling 2308.3.1 Biological Data Curation 2318.3.2 Identification and Treatment of Activity and Toxicity Cliffs 2338.3.3 In Vitro to In Vivo Continuum in the Context of AOP 2338.4 Perspectives 2348.4.1 Profilers on the Go with Mobile Devices 2358.4.2 Structure–Exposure–Activity Relationships 2368.5 Conclusions 237Acknowledgments 237Disclaimer 237References 2389 Computational Toxicology and Reach 245Emilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 2459.2 Reach and the Other Legislations 2479.3 Annex XI of Reach for QSARModels 2489.3.1 The First Condition of Annex XI and QMRF 2499.3.2 The Second Condition and the Applicability Domain 2519.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 2529.3.4 Adequate and Reliable Documentation of the Applied Method 2549.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 2559.4.1 Example of Bioconcentration Factor (BCF) 2559.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 2609.5 Conclusions 266References 26610 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269Jim E. Riviere and Jason Chittenden10.1 Introduction 26910.2 Principles of Dermal Absorption 27010.3 Dermal Mixtures 27410.4 Model Systems 27510.5 Local Skin Versus Systemic Endpoints 27710.6 QSAR Approaches to Model Dermal Absorption 27810.7 PharmacokineticModels 28110.8 Conclusions 284References 285Part IV New Technologies for Toxicology, Future Perspectives 29111 Big Data in Computational Toxicology: Challenges and Opportunities 293Linlin Zhao and Hao Zhu11.1 Big Data Scenario of Computational Toxicology 29311.2 Fast-Growing Chemical Toxicity Data 29511.3 The Use of Big Data Approaches in Modern Computational Toxicology 29911.3.1 Profiling the Toxicants with Massive Biological Data 29911.3.2 Read-Across Study to Fill Data Gap 30111.3.3 Unstructured Data Curation 30211.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303References 30412 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 313George van Den Driessche and Denis Fourches12.1 Introduction 31312.2 Human Leukocyte Antigens 31412.2.1 HLA Proteins 31412.2.2 ADR–HLA Associations 31612.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 32112.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 32212.3.1 Structure-Based Docking 32412.3.2 Case Study: Abacavir with B*57:01 32612.3.3 Limitations 33212.4 Perspectives 334References 33513 Open Science Data Repository for Toxicology 341Valery Tkachenko, Richard Zakharov, and Sean Ekins13.1 Introduction 34113.2 Open Science Data Repository 34213.3 Benefits of OSDR 34413.3.1 Chemically and Semantically Enabled Scientific Data Repository 34413.3.2 Chemical Validation and Standardization Platform 34613.3.3 Format Adapters 34713.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 35013.3.5 Dataledger 35013.4 Technical Details 35113.5 FutureWork 35313.5.1 Implementation of Ontology-Based Properties 35613.5.2 Implementation of an Advanced Search System 35713.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357References 35814 Developing Next Generation Tools for Computational Toxicology 363Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins14.1 Introduction 36314.2 Developing Apps for Chemistry 36414.3 Green Chemistry 36414.3.1 Green Solvents and Lab Solvents 36714.3.2 Green Lab Notebook 37014.4 Polypharma and Assay Central 37414.4.1 Future Efforts with Assay Central 38014.5 Conclusion 382Acknowledgments 383References 383Index 389

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Sean Ekins, MSc, PhD, DSc has over 20 years of pharmaceutical and toxicology experience. He is the founder or co-founder of two companies and Adjunct Professor at three universities. He has been awarded 16 NIH grants as Principal Investigator. He has authored or co authored over 285 peer-reviewed papers and book chapters and edited five books with Wiley. His research is focused on collaborations to facilitate rare and neglected disease drug discovery.

• List of Contributors xviiPreface xxiAcknowledgments xxiiiPart I Computational Methods 11 AccessibleMachine Learning Approaches for Toxicology 3Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery Tkachenko1.1 Introduction 31.2 Bayesian Models 51.2.1 CDD Models 71.3 Deep LearningModels 131.4 Comparison of Different Machine LearningMethods 161.4.1 Classic Machine LearningMethods 171.4.1.1 Bernoulli Naive Bayes 171.4.1.2 Linear Logistic Regression with Regularization 181.4.1.3 AdaBoost Decision Tree 181.4.1.4 Random Forest 181.4.1.5 Support Vector Machine 191.4.2 Deep Neural Networks 191.4.3 Comparing Models 201.5 FutureWork 21Acknowledgments 21References 212 Quantum Mechanics Approaches in Computational Toxicology 31Jakub Kostal2.1 Translating Computational Chemistry to Predictive Toxicology 312.2 Levels of Theory in Quantum Mechanical Calculations 332.3 Representing Molecular Orbitals 382.4 Hybrid Quantum and Molecular Mechanical Calculations 392.5 Representing System Dynamics 402.6 Developing QM Descriptors 422.6.1 Global Electronic Parameters 422.6.1.1 Electrostatic Potential, Dipole, and Polarizability 432.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 452.6.2 Local (Atom-Based) Electronic Parameters 472.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 482.6.2.2 Partial Atomic Charges 512.6.2.3 Hydrogen-Bonding Interactions 512.6.2.4 Bond Enthalpies 532.6.3 Modeling Chemical Reactions 532.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 562.6.5 Medium Effects and Hydration Models 592.7 Rational Design of Safer Chemicals 61References 64Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 693 Computational Approaches for Predicting hERG Activity 71Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade3.1 Introduction 713.2 Computational Approaches 733.3 Ligand-Based Approaches 733.4 Structure-Based Approaches 773.5 Applications to Predict hERG Blockage 773.5.1 Pred-hERGWeb App 793.6 Other Computational Approaches Related to hERG Liability 823.7 Final Remarks 83References 834 Computational Toxicology for Traditional Chinese Medicine 93Ni Ai and Xiaohui Fan4.1 Background, Current Status, and Challenges 934.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 994.2.1 Introduction to OAT1 and TCM 994.2.2 Construction of TCM Compound Database 1014.2.3 OAT1 Inhibitor Pharmacophore Development 1014.2.4 External Test Set Evaluation 1024.2.5 Database Searching 1024.2.6 Results: OAT1 Inhibitor Pharmacophore 1034.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 1044.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 1044.2.9 Discussion 1104.3 Conclusion 114Acknowledgment 114References 1145 PharmacophoreModels for Toxicology Prediction 121Daniela Schuster5.1 Introduction 1215.2 Antitarget Screening 1255.3 Prediction of Liver Toxicity 1255.4 Prediction of Cardiovascular Toxicity 1275.5 Prediction of Central Nervous System (CNS) Toxicity 1285.6 Prediction of Endocrine Disruption 1305.7 Prediction of ADME 1355.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136References 1376 Transporters in Hepatotoxicity 145Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker6.1 Introduction 1456.2 Basolateral Transporters 1466.3 Canalicular Transporters 1486.4 Data Sources for Transporters in Hepatotoxicity 1486.5 In Silico Transporters Models 1506.6 Ligand-Based Approaches 1506.7 OATP1B1 and OATP1B3 1506.8 NTCP 1546.9 OCT1 1546.10 OCT2 1546.11 MRP1, MRP3, and MRP4 1556.12 BSEP 1556.13 MRP2 1566.14 MDR1/P-gp 1566.15 MDR3 1576.16 BCRP 1576.17 MATE1 1586.18 ASBT 1596.19 Structure-Based Approaches 1596.20 Complex Models Incorporating Transporter Information 1606.21 In Vitro Models 1606.22 Multiscale Models 1616.23 Outlook 162Acknowledgments 164References 1647 Cheminformatics in a Clinical Setting 175Matthew D. Krasowski and Sean Ekins7.1 Introduction 1757.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 1777.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 1877.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 1917.5 Cheminformatics Applied to "Designer Drugs" 1957.6 Relevance to Antibody-Ligand Interactions 2027.7 Conclusions and Future Directions 203Acknowledgment 204References 204Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 2118 Computational Tools for ADMET Profiling 213Denis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann Richard, and Alexander Tropsha8.1 Introduction 2138.2 Cheminformatics Approaches for ADMET Profiling 2148.2.1 Chemical Data Curation Prior to ADMET Modeling 2158.2.2 QSAR Modelability Index 2178.2.3 Predictive QSAR Model DevelopmentWorkflow 2188.2.4 Hybrid QSAR Modeling 2208.2.4.1 Simple Consensus 2238.2.4.2 Mixed Chemical and Biological Features 2238.2.4.3 Two-Step HierarchicalWorkflow 2248.2.5 Chemical Biological Read-Across 2268.2.6 Public Chemotype Approach to Data-Mining 2298.3 Unsolved Challenges in Structure Based Profiling 2308.3.1 Biological Data Curation 2318.3.2 Identification and Treatment of Activity and Toxicity Cliffs 2338.3.3 In Vitro to In Vivo Continuum in the Context of AOP 2338.4 Perspectives 2348.4.1 Profilers on the Go with Mobile Devices 2358.4.2 Structure–Exposure–Activity Relationships 2368.5 Conclusions 237Acknowledgments 237Disclaimer 237References 2389 Computational Toxicology and Reach 245Emilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 2459.2 Reach and the Other Legislations 2479.3 Annex XI of Reach for QSARModels 2489.3.1 The First Condition of Annex XI and QMRF 2499.3.2 The Second Condition and the Applicability Domain 2519.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 2529.3.4 Adequate and Reliable Documentation of the Applied Method 2549.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 2559.4.1 Example of Bioconcentration Factor (BCF) 2559.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 2609.5 Conclusions 266References 26610 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269Jim E. Riviere and Jason Chittenden10.1 Introduction 26910.2 Principles of Dermal Absorption 27010.3 Dermal Mixtures 27410.4 Model Systems 27510.5 Local Skin Versus Systemic Endpoints 27710.6 QSAR Approaches to Model Dermal Absorption 27810.7 PharmacokineticModels 28110.8 Conclusions 284References 285Part IV New Technologies for Toxicology, Future Perspectives 29111 Big Data in Computational Toxicology: Challenges and Opportunities 293Linlin Zhao and Hao Zhu11.1 Big Data Scenario of Computational Toxicology 29311.2 Fast-Growing Chemical Toxicity Data 29511.3 The Use of Big Data Approaches in Modern Computational Toxicology 29911.3.1 Profiling the Toxicants with Massive Biological Data 29911.3.2 Read-Across Study to Fill Data Gap 30111.3.3 Unstructured Data Curation 30211.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303References 30412 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 313George van Den Driessche and Denis Fourches12.1 Introduction 31312.2 Human Leukocyte Antigens 31412.2.1 HLA Proteins 31412.2.2 ADR–HLA Associations 31612.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 32112.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 32212.3.1 Structure-Based Docking 32412.3.2 Case Study: Abacavir with B*57:01 32612.3.3 Limitations 33212.4 Perspectives 334References 33513 Open Science Data Repository for Toxicology 341Valery Tkachenko, Richard Zakharov, and Sean Ekins13.1 Introduction 34113.2 Open Science Data Repository 34213.3 Benefits of OSDR 34413.3.1 Chemically and Semantically Enabled Scientific Data Repository 34413.3.2 Chemical Validation and Standardization Platform 34613.3.3 Format Adapters 34713.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 35013.3.5 Dataledger 35013.4 Technical Details 35113.5 FutureWork 35313.5.1 Implementation of Ontology-Based Properties 35613.5.2 Implementation of an Advanced Search System 35713.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357References 35814 Developing Next Generation Tools for Computational Toxicology 363Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins14.1 Introduction 36314.2 Developing Apps for Chemistry 36414.3 Green Chemistry 36414.3.1 Green Solvents and Lab Solvents 36714.3.2 Green Lab Notebook 37014.4 Polypharma and Assay Central 37414.4.1 Future Efforts with Assay Central 38014.5 Conclusion 382Acknowledgments 383References 383Index 389

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Sean Ekins, MSc, PhD, DSc has over 20 years of pharmaceutical and toxicology experience. He is the founder or co-founder of two companies and Adjunct Professor at three universities. He has been awarded 16 NIH grants as Principal Investigator. He has authored or co authored over 285 peer-reviewed papers and book chapters and edited five books with Wiley. His research is focused on collaborations to facilitate rare and neglected disease drug discovery.

• List of Contributors xviiPreface xxiAcknowledgments xxiiiPart I Computational Methods 11 AccessibleMachine Learning Approaches for Toxicology 3Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery Tkachenko1.1 Introduction 31.2 Bayesian Models 51.2.1 CDD Models 71.3 Deep LearningModels 131.4 Comparison of Different Machine LearningMethods 161.4.1 Classic Machine LearningMethods 171.4.1.1 Bernoulli Naive Bayes 171.4.1.2 Linear Logistic Regression with Regularization 181.4.1.3 AdaBoost Decision Tree 181.4.1.4 Random Forest 181.4.1.5 Support Vector Machine 191.4.2 Deep Neural Networks 191.4.3 Comparing Models 201.5 FutureWork 21Acknowledgments 21References 212 Quantum Mechanics Approaches in Computational Toxicology 31Jakub Kostal2.1 Translating Computational Chemistry to Predictive Toxicology 312.2 Levels of Theory in Quantum Mechanical Calculations 332.3 Representing Molecular Orbitals 382.4 Hybrid Quantum and Molecular Mechanical Calculations 392.5 Representing System Dynamics 402.6 Developing QM Descriptors 422.6.1 Global Electronic Parameters 422.6.1.1 Electrostatic Potential, Dipole, and Polarizability 432.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 452.6.2 Local (Atom-Based) Electronic Parameters 472.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 482.6.2.2 Partial Atomic Charges 512.6.2.3 Hydrogen-Bonding Interactions 512.6.2.4 Bond Enthalpies 532.6.3 Modeling Chemical Reactions 532.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 562.6.5 Medium Effects and Hydration Models 592.7 Rational Design of Safer Chemicals 61References 64Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 693 Computational Approaches for Predicting hERG Activity 71Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade3.1 Introduction 713.2 Computational Approaches 733.3 Ligand-Based Approaches 733.4 Structure-Based Approaches 773.5 Applications to Predict hERG Blockage 773.5.1 Pred-hERGWeb App 793.6 Other Computational Approaches Related to hERG Liability 823.7 Final Remarks 83References 834 Computational Toxicology for Traditional Chinese Medicine 93Ni Ai and Xiaohui Fan4.1 Background, Current Status, and Challenges 934.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 994.2.1 Introduction to OAT1 and TCM 994.2.2 Construction of TCM Compound Database 1014.2.3 OAT1 Inhibitor Pharmacophore Development 1014.2.4 External Test Set Evaluation 1024.2.5 Database Searching 1024.2.6 Results: OAT1 Inhibitor Pharmacophore 1034.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 1044.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 1044.2.9 Discussion 1104.3 Conclusion 114Acknowledgment 114References 1145 PharmacophoreModels for Toxicology Prediction 121Daniela Schuster5.1 Introduction 1215.2 Antitarget Screening 1255.3 Prediction of Liver Toxicity 1255.4 Prediction of Cardiovascular Toxicity 1275.5 Prediction of Central Nervous System (CNS) Toxicity 1285.6 Prediction of Endocrine Disruption 1305.7 Prediction of ADME 1355.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136References 1376 Transporters in Hepatotoxicity 145Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker6.1 Introduction 1456.2 Basolateral Transporters 1466.3 Canalicular Transporters 1486.4 Data Sources for Transporters in Hepatotoxicity 1486.5 In Silico Transporters Models 1506.6 Ligand-Based Approaches 1506.7 OATP1B1 and OATP1B3 1506.8 NTCP 1546.9 OCT1 1546.10 OCT2 1546.11 MRP1, MRP3, and MRP4 1556.12 BSEP 1556.13 MRP2 1566.14 MDR1/P-gp 1566.15 MDR3 1576.16 BCRP 1576.17 MATE1 1586.18 ASBT 1596.19 Structure-Based Approaches 1596.20 Complex Models Incorporating Transporter Information 1606.21 In Vitro Models 1606.22 Multiscale Models 1616.23 Outlook 162Acknowledgments 164References 1647 Cheminformatics in a Clinical Setting 175Matthew D. Krasowski and Sean Ekins7.1 Introduction 1757.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 1777.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 1877.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 1917.5 Cheminformatics Applied to "Designer Drugs" 1957.6 Relevance to Antibody-Ligand Interactions 2027.7 Conclusions and Future Directions 203Acknowledgment 204References 204Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 2118 Computational Tools for ADMET Profiling 213Denis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann Richard, and Alexander Tropsha8.1 Introduction 2138.2 Cheminformatics Approaches for ADMET Profiling 2148.2.1 Chemical Data Curation Prior to ADMET Modeling 2158.2.2 QSAR Modelability Index 2178.2.3 Predictive QSAR Model DevelopmentWorkflow 2188.2.4 Hybrid QSAR Modeling 2208.2.4.1 Simple Consensus 2238.2.4.2 Mixed Chemical and Biological Features 2238.2.4.3 Two-Step HierarchicalWorkflow 2248.2.5 Chemical Biological Read-Across 2268.2.6 Public Chemotype Approach to Data-Mining 2298.3 Unsolved Challenges in Structure Based Profiling 2308.3.1 Biological Data Curation 2318.3.2 Identification and Treatment of Activity and Toxicity Cliffs 2338.3.3 In Vitro to In Vivo Continuum in the Context of AOP 2338.4 Perspectives 2348.4.1 Profilers on the Go with Mobile Devices 2358.4.2 Structure–Exposure–Activity Relationships 2368.5 Conclusions 237Acknowledgments 237Disclaimer 237References 2389 Computational Toxicology and Reach 245Emilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 2459.2 Reach and the Other Legislations 2479.3 Annex XI of Reach for QSARModels 2489.3.1 The First Condition of Annex XI and QMRF 2499.3.2 The Second Condition and the Applicability Domain 2519.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 2529.3.4 Adequate and Reliable Documentation of the Applied Method 2549.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 2559.4.1 Example of Bioconcentration Factor (BCF) 2559.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 2609.5 Conclusions 266References 26610 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269Jim E. Riviere and Jason Chittenden10.1 Introduction 26910.2 Principles of Dermal Absorption 27010.3 Dermal Mixtures 27410.4 Model Systems 27510.5 Local Skin Versus Systemic Endpoints 27710.6 QSAR Approaches to Model Dermal Absorption 27810.7 PharmacokineticModels 28110.8 Conclusions 284References 285Part IV New Technologies for Toxicology, Future Perspectives 29111 Big Data in Computational Toxicology: Challenges and Opportunities 293Linlin Zhao and Hao Zhu11.1 Big Data Scenario of Computational Toxicology 29311.2 Fast-Growing Chemical Toxicity Data 29511.3 The Use of Big Data Approaches in Modern Computational Toxicology 29911.3.1 Profiling the Toxicants with Massive Biological Data 29911.3.2 Read-Across Study to Fill Data Gap 30111.3.3 Unstructured Data Curation 30211.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303References 30412 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 313George van Den Driessche and Denis Fourches12.1 Introduction 31312.2 Human Leukocyte Antigens 31412.2.1 HLA Proteins 31412.2.2 ADR–HLA Associations 31612.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 32112.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 32212.3.1 Structure-Based Docking 32412.3.2 Case Study: Abacavir with B*57:01 32612.3.3 Limitations 33212.4 Perspectives 334References 33513 Open Science Data Repository for Toxicology 341Valery Tkachenko, Richard Zakharov, and Sean Ekins13.1 Introduction 34113.2 Open Science Data Repository 34213.3 Benefits of OSDR 34413.3.1 Chemically and Semantically Enabled Scientific Data Repository 34413.3.2 Chemical Validation and Standardization Platform 34613.3.3 Format Adapters 34713.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 35013.3.5 Dataledger 35013.4 Technical Details 35113.5 FutureWork 35313.5.1 Implementation of Ontology-Based Properties 35613.5.2 Implementation of an Advanced Search System 35713.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357References 35814 Developing Next Generation Tools for Computational Toxicology 363Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins14.1 Introduction 36314.2 Developing Apps for Chemistry 36414.3 Green Chemistry 36414.3.1 Green Solvents and Lab Solvents 36714.3.2 Green Lab Notebook 37014.4 Polypharma and Assay Central 37414.4.1 Future Efforts with Assay Central 38014.5 Conclusion 382Acknowledgments 383References 383Index 389

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