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Part II: Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical
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Part II: Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical
by Sean Ekins
Computational Toxicology
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
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Chapter 2: Quantum Mechanics Approaches in Computational Toxicology
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Chapter 3: Computational Approaches for Predicting hERG Activity
Part II
Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical
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