Book Description A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals. • Provides a perspective of what is currently achievable with computational toxicology and a view to future developments • Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment • Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference • Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling • Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data Show and hide more
Table of Contents
Cover Title Page Copyright Dedication List of Contributors Preface Acknowledgments Part I: Computational Methods Chapter 1: Accessible Machine Learning Approaches for Toxicology 1.1 Introduction 1.2 Bayesian Models 1.3 Deep Learning Models 1.4 Comparison of Different Machine Learning Methods 1.5 Future Work Acknowledgments References Chapter 2: Quantum Mechanics Approaches in Computational Toxicology 2.1 Translating Computational Chemistry to Predictive Toxicology 2.2 Levels of Theory in Quantum Mechanical Calculations 2.3 Representing Molecular Orbitals 2.4 Hybrid Quantum and Molecular Mechanical Calculations 2.5 Representing System Dynamics 2.6 Developing QM Descriptors 2.7 Rational Design of Safer Chemicals References Part II: Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical Chapter 3: Computational Approaches for Predicting hERG Activity 3.1 Introduction 3.2 Computational Approaches 3.3 Ligand-Based Approaches 3.4 Structure-Based Approaches 3.5 Applications to Predict hERG Blockage 3.6 Other Computational Approaches Related to hERG Liability 3.7 Final Remarks References Chapter 4: Computational Toxicology for Traditional Chinese Medicine 4.1 Background, Current Status, and Challenges 4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 4.3 Conclusion Acknowledgment References Chapter 5: Pharmacophore Models for Toxicology Prediction 5.1 Introduction 5.2 Antitarget Screening 5.3 Prediction of Liver Toxicity 5.4 Prediction of Cardiovascular Toxicity 5.5 Prediction of Central Nervous System (CNS) Toxicity 5.6 Prediction of Endocrine Disruption 5.7 Prediction of ADME 5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies References Chapter 6: Transporters in Hepatotoxicity 6.1 Introduction 6.2 Basolateral Transporters 6.3 Canalicular Transporters 6.4 Data Sources for Transporters in Hepatotoxicity 6.5 In Silico Transporters Models 6.6 Ligand-Based Approaches 6.7 OATP1B1 and OATP1B3 6.8 NTCP 6.9 OCT1 6.10 OCT2 6.11 MRP1, MRP3, and MRP4 6.12 BSEP 6.13 MRP2 6.14 MDR1/P-gp 6.15 MDR3 6.16 BCRP 6.17 MATE1 6.18 ASBT 6.19 Structure-Based Approaches 6.20 Complex Models Incorporating Transporter Information 6.21 In Vitro Models 6.22 Multiscale Models 6.23 Outlook Acknowledgments References Chapter 7: Cheminformatics in a Clinical Setting 7.1 Introduction 7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 7.3 Similarity Analysis Applied to Therapeutic Drug Monitoring Immunoassays 7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 7.5 Cheminformatics Applied to “Designer Drugs” 7.6 Relevance to Antibody-Ligand Interactions 7.7 Conclusions and Future Directions Acknowledgment References Part III: Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives Chapter 8: Computational Tools for ADMET Profiling 8.1 Introduction 8.2 Cheminformatics Approaches for ADMET Profiling 8.3 Unsolved Challenges in Structure Based Profiling 8.4 Perspectives 8.5 Conclusions Acknowledgments Disclaimer References Chapter 9: Computational Toxicology and Reach 9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 9.2 Reach and the Other Legislations 9.3 Annex XI of Reach for QSAR Models 9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 9.5 Conclusions References Chapter 10: Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 10.1 Introduction 10.2 Principles of Dermal Absorption 10.3 Dermal Mixtures 10.4 Model Systems 10.5 Local Skin Versus Systemic Endpoints 10.6 QSAR Approaches to Model Dermal Absorption 10.7 Pharmacokinetic Models 10.8 Conclusions References Part IV: New Technologies for Toxicology, Future Perspectives Chapter 11: Big Data in Computational Toxicology: Challenges and Opportunities 11.1 Big Data Scenario of Computational Toxicology 11.2 Fast-Growing Chemical Toxicity Data 11.3 The Use of Big Data Approaches in Modern Computational Toxicology 11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts References Chapter 12: HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 12.1 Introduction 12.2 Human Leukocyte Antigens 12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 12.4 Perspectives References Chapter 13: Open Science Data Repository for Toxicology 13.1 Introduction 13.2 Open Science Data Repository 13.3 Benefits of OSDR 13.4 Technical Details 13.5 Future Work References Chapter 14: Developing Next Generation Tools for Computational Toxicology 14.1 Introduction 14.2 Developing Apps for Chemistry 14.3 Green Chemistry 14.4 Polypharma and Assay Central 14.5 Conclusion Acknowledgments References Index End User License Agreement