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Computational Toxicology

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

Table of Contents

  1. Cover
  2. Title Page
    1. Copyright
    2. Dedication
  3. List of Contributors
  4. Preface
  5. Acknowledgments
  6. Part I: Computational Methods
    1. Chapter 1: Accessible Machine Learning Approaches for Toxicology
      1. 1.1 Introduction
      2. 1.2 Bayesian Models
      3. 1.3 Deep Learning Models
      4. 1.4 Comparison of Different Machine Learning Methods
      5. 1.5 Future Work
      6. Acknowledgments
      7. References
    2. Chapter 2: Quantum Mechanics Approaches in Computational Toxicology
      1. 2.1 Translating Computational Chemistry to Predictive Toxicology
      2. 2.2 Levels of Theory in Quantum Mechanical Calculations
      3. 2.3 Representing Molecular Orbitals
      4. 2.4 Hybrid Quantum and Molecular Mechanical Calculations
      5. 2.5 Representing System Dynamics
      6. 2.6 Developing QM Descriptors
      7. 2.7 Rational Design of Safer Chemicals
      8. References
  7. Part II: Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical
    1. Chapter 3: Computational Approaches for Predicting hERG Activity
      1. 3.1 Introduction
      2. 3.2 Computational Approaches
      3. 3.3 Ligand-Based Approaches
      4. 3.4 Structure-Based Approaches
      5. 3.5 Applications to Predict hERG Blockage
      6. 3.6 Other Computational Approaches Related to hERG Liability
      7. 3.7 Final Remarks
      8. References
    2. Chapter 4: Computational Toxicology for Traditional Chinese Medicine
      1. 4.1 Background, Current Status, and Challenges
      2. 4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions
      3. 4.3 Conclusion
      4. Acknowledgment
      5. References
    3. Chapter 5: Pharmacophore Models for Toxicology Prediction
      1. 5.1 Introduction
      2. 5.2 Antitarget Screening
      3. 5.3 Prediction of Liver Toxicity
      4. 5.4 Prediction of Cardiovascular Toxicity
      5. 5.5 Prediction of Central Nervous System (CNS) Toxicity
      6. 5.6 Prediction of Endocrine Disruption
      7. 5.7 Prediction of ADME
      8. 5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies
      9. References
    4. Chapter 6: Transporters in Hepatotoxicity
      1. 6.1 Introduction
      2. 6.2 Basolateral Transporters
      3. 6.3 Canalicular Transporters
      4. 6.4 Data Sources for Transporters in Hepatotoxicity
      5. 6.5 In Silico Transporters Models
      6. 6.6 Ligand-Based Approaches
      7. 6.7 OATP1B1 and OATP1B3
      8. 6.8 NTCP
      9. 6.9 OCT1
      10. 6.10 OCT2
      11. 6.11 MRP1, MRP3, and MRP4
      12. 6.12 BSEP
      13. 6.13 MRP2
      14. 6.14 MDR1/P-gp
      15. 6.15 MDR3
      16. 6.16 BCRP
      17. 6.17 MATE1
      18. 6.18 ASBT
      19. 6.19 Structure-Based Approaches
      20. 6.20 Complex Models Incorporating Transporter Information
      21. 6.21 In Vitro Models
      22. 6.22 Multiscale Models
      23. 6.23 Outlook
      24. Acknowledgments
      25. References
    5. Chapter 7: Cheminformatics in a Clinical Setting
      1. 7.1 Introduction
      2. 7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays
      3. 7.3 Similarity Analysis Applied to Therapeutic Drug Monitoring Immunoassays
      4. 7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays
      5. 7.5 Cheminformatics Applied to “Designer Drugs”
      6. 7.6 Relevance to Antibody-Ligand Interactions
      7. 7.7 Conclusions and Future Directions
      8. Acknowledgment
      9. References
  8. Part III: Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives
    1. Chapter 8: Computational Tools for ADMET Profiling
      1. 8.1 Introduction
      2. 8.2 Cheminformatics Approaches for ADMET Profiling
      3. 8.3 Unsolved Challenges in Structure Based Profiling
      4. 8.4 Perspectives
      5. 8.5 Conclusions
      6. Acknowledgments
      7. Disclaimer
      8. References
    2. Chapter 9: Computational Toxicology and Reach
      1. 9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models
      2. 9.2 Reach and the Other Legislations
      3. 9.3 Annex XI of Reach for QSAR Models
      4. 9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA
      5. 9.5 Conclusions
      6. References
    3. Chapter 10: Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures
      1. 10.1 Introduction
      2. 10.2 Principles of Dermal Absorption
      3. 10.3 Dermal Mixtures
      4. 10.4 Model Systems
      5. 10.5 Local Skin Versus Systemic Endpoints
      6. 10.6 QSAR Approaches to Model Dermal Absorption
      7. 10.7 Pharmacokinetic Models
      8. 10.8 Conclusions
      9. References
  9. Part IV: New Technologies for Toxicology, Future Perspectives
    1. Chapter 11: Big Data in Computational Toxicology: Challenges and Opportunities
      1. 11.1 Big Data Scenario of Computational Toxicology
      2. 11.2 Fast-Growing Chemical Toxicity Data
      3. 11.3 The Use of Big Data Approaches in Modern Computational Toxicology
      4. 11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts
      5. References
    2. Chapter 12: HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling
      1. 12.1 Introduction
      2. 12.2 Human Leukocyte Antigens
      3. 12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs
      4. 12.4 Perspectives
      5. References
    3. Chapter 13: Open Science Data Repository for Toxicology
      1. 13.1 Introduction
      2. 13.2 Open Science Data Repository
      3. 13.3 Benefits of OSDR
      4. 13.4 Technical Details
      5. 13.5 Future Work
      6. References
    4. Chapter 14: Developing Next Generation Tools for Computational Toxicology
      1. 14.1 Introduction
      2. 14.2 Developing Apps for Chemistry
      3. 14.3 Green Chemistry
      4. 14.4 Polypharma and Assay Central
      5. 14.5 Conclusion
      6. Acknowledgments
      7. References
    5. Index
  10. End User License Agreement