QSAR in Safety Evaluation and Risk Assessment

QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment.
Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Chapter 1. QSAR facilitating safety evaluation and risk assessment
Introduction
Data sources for QSAR
QSAR methods
Evaluation of QSAR models
Machine learning and deep learning accelerate QSAR development
Perspectives
Part I. Methods and advances of QSAR
Chapter 2. Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity
Introduction: growing regulatory pressure to develop alternative computational methods for chemical toxicity assessment
Comparison of computational approaches for chemical toxicity prediction
Contrasting alerts and QSAR-based predictions of acute toxicity
Integration of interpretative QSAR models and chemical alerts
The continuing importance of data quality and curation in the age of big data and AI
Biomedical knowledge mining to identify mechanistic pathways underlying chemical toxicity effects
Conclusions and perspectives
Chapter 3. Neural network-based descriptors as input for QSAR
Introduction
Deep learning–based methods for generating descriptors
Black box approach in deep learning–based descriptors
Summary
Chapter 4. Decision forest—a machine learning algorithm for QSAR modeling
Introduction
Decision forest algorithm
QSAR models developed using decision forest
Conclusion remarks
Chapter 5. Integrated modeling for compound efficacy and safety assessment
Introduction
Compound representation
Molecular representation
MOA representation
Datasets for compound discovery
Virtual screening
Quantitative structure–activity relationship
Generative models
Read-across
Biomarker discovery
Systems pharmacology
Knowledge graph–based approaches for chemical safety and drug design
Conclusions
Chapter 6. Deep learning quantitative structure–activity relationship methods for chemical toxicity prediction and risk assessment
Introduction
Deep learning methods
Key DL techniques for QSAR researches
Recent advances in DL-based QSAR researches in toxicity prediction and risk assessment
Free available DL-based tools for chemical toxicity prediction
Conclusions and future perspectives
Chapter 7. Predictive modeling approaches for the risk assessment of persistent organic pollutants (POPs): from QSAR to machine learning–based models
Introduction
Significant breakthroughs in QSAR modeling of POPs
Current advancements and guidelines for QSAR model development of POPs
Different molecular endpoints for the classification of POPs
Molecular descriptors utilized in the QSAR modeling of POPs
Statistical and ML-based approaches for model development of POPs
Classical approaches for QSAR model development
ML-based QSAR approaches
Contemporary QSAR tools for PBT analysis of POPs
Conclusions
Chapter 8. Machine learning–based QSAR for safety evaluation of environmental chemicals
Introduction
ML-driven QSAR modeling
ML-driven QSAR applications
Challenges in QSAR model construction
Perspectives
Chapter 9. Advances in QSAR through artificial intelligence and machine learning methods
Introduction
Roadmap of QSAR method
QSAR methods based on molecular descriptors
Artificial intelligence in drug screening
Iterative integration of different models in QSAR
Neural network learning
Artificial neural network–quantitative structure–activity relationship
Machine learning models—Quantitative structure–activity relationship
Deep learning—Quantitative structure–activity relationship
Decision tree algorithms
Random forest
Supervised learning
Intrinsic proximity measure
AdaBoost classifier
Partial least squares regression
Software's available for QSAR
Concluding remarks
Chapter 10. Advances of the QSAR approach as an alternative strategy in the environmental risk assessment
Introduction
The principal aspects of ERA
The QSAR approach and its fundaments
General methodologies of the QSAR models
Features, contributions, and advances of QSAR modeling in ERA processes
Future perspective of QSAR modeling within ERA approach
Chapter 11. QSAR modeling based on graph neural networks
Introduction
QSAR models for the management of chemicals
GNN algorithm
GNN for QSAR modeling
Applicability domains for GNN-based QSAR models
Conclusions
Part II. Tools and data sources for QSAR
Chapter 12. Modeling safety and risk assessment with VEGAHUB
The global needs of modern society about risk assessment and safety
The VEGAHUB components
The architecture and the conceptual basis within VEGAHUB
The use of VEGAHUB for safety and risk assessment
The role of VEGAHUB within a larger network
Conclusions
Chapter 13. Recent advancements in QSAR and machine learning approaches for risk assessment of organic chemicals
Introduction
Brief overview of the methodologies used for QSAR modeling in predictive toxicology
QSPR applications in toxicity prediction of organic chemicals
Conclusion
Chapter 14. admetSAR—A valuable tool for assisting safety evaluation
Introduction
Basic architecture of admetSAR
Details of admetSAR
Usage of admetSAR
Applications of admetSAR
Comparison with other tools
Conclusions and outlook
Chapter 15. QSAR tools for toxicity prediction in risk assessment—Comparative analysis
Introduction
The basic information of toxicity prediction software package
Modeling methods of the toxicity prediction software packages
Perspectives
Chapter 16. Fast and efficient implementation of computational toxicology solutions using the FlexFilters platform
Introduction
The “filter” concept
Syntax for the filter calls
Frequently used filters in FlexFilters platform
Building FlexFilters modules
Applying the modules for prediction
Examples of computational toxicology solutions built using the FlexFilters methodology
Conclusions and future directions
Chapter 17. DILIrank dataset for QSAR modeling of drug-induced liver injury
Introduction
Basic concepts for DILI annotations
Drug labeling for DILI annotation
Annotation schema for assessing DILI risk
Develop a DILIrank dataset to support the development of QSAR and other predictive models
Concluding remarks
Chapter 18. Application of QSAR models based on machine learning methods in chemical risk assessment and drug discovery
Introduction
Overview of QSAR models based on machine learning methods
QSAR models for chemical risk assessment
QSAR models for drug discovery
Conclusions and future directions
Chapter 19. EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity
Introduction
EADB schema
EADB applications
Conclusions
Chapter 20. Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction
Introduction
Methods and materials
Results
Discussions
Part III. QSAR models for safety evaluation of drugs and consumer products
Chapter 21. QSAR modeling for predicting drug-induced liver injury
Introduction
How does QSAR apply to DILI prediction?
How does deep learning assist QSAR for DILI prediction?
Prediction performance of current DILI QSAR models
Perspectives
Chapter 22. The need of QSAR methods to assess safety of chemicals in food contact materials
Introduction
Non-testing approaches for hazard identification and characterization
Safety assessment protocol for FCM chemicals
Conclusion and perspectives
Chapter 23. QSAR models for predicting in vivo reproductive toxicity
Introduction
QSAR models based on ECHA-C&L inventory
QSAR models based on ToxRefDB
QSAR models based on P&G and leadscope
QSAR model application
Conclusions
Chapter 24. Aryl hydrocarbon receptors and their ligands in human health management
Introduction
Studies on AHR-generated hepatotoxicity
Studies of AHR antagonist
Studies on AHR activation
Studies that determine the AHR inhibitors in food
Conclusions
Chapter 25. Use of in silico protocols to evaluate drug safety
Introduction
In silico toxicology protocol concepts
Applying in silico toxicology concepts and protocols to assess genotoxic impurities
Interactive visual hazard assessment framework
Discussion and conclusions
Chapter 26. QSAR models for predicting cardiac toxicity of drugs
Introduction
In vivo and in vitro approaches for the evaluation of hERG safety
Computational approaches
Applications to predict hERG blockage
Conclusions and future directions
Part IV. QSAR models for risk assessment of chemicals
Chapter 27. Curation of more than 10,000 Ames test data used in the Ames/QSAR International Challenge Projects
Introduction
Data curation procedure of 12,140 ANEI-HOU chemicals
Strain information
Solvent and purity of the ames tests
Relationship with mutagenicity and strain
Summary
Acknowledgments and fundings
Chapter 28. QSAR model of photolysis kinetic parameters in aquatic environment
Introduction
Direct photolysis
Indirect photolysis
Perspectives
Chapter 29. (Q)SAR models on transthyretin disrupting effects of chemicals
Introduction
Profile of transthyretin disrupting effects
(Q)SARs models of transthyretin disrupting effects
Software could be used to screen potential transthyretin disruptors
Conclusions and future directions
Chapter 30. QSAR models for toxicity assessment of multicomponent systems
Introduction
Multicomponent systems or mixtures
Conclusions or perspectives or future directions
Chapter 31. Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms
Introduction
Conclusions and perspectives
Chapter 32. Theoretical prediction for carrying capacity of microplastic toward organic pollutants
Introduction
Adsorption between microplastics and organic pollutants
Influencing factors on the adsorption capacity
Theoretical prediction models on Kd value
Other prediction methods on adsorption mechanism
Perspectives
Chapter 33. QSAR models on degradation rate constants of atmospheric pollutants
Introduction
QSAR models for predicting reaction rate constants of pollutants with ·OH
QSAR models for predicting reaction rate constants of pollutants with O3
QSAR models for predicting reaction rate constants of pollutants with NO3·
QSAR models for predicting reaction rate constants of pollutants with ·Cl
Perspectives
Part V. QSAR models in material science and other areas
Chapter 34. Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs)
Introduction
QSAR methodology
Cancer risk assessment
Conclusions and future directions
Chapter 35. QSAR in risk assessment of nanomaterials
Introduction
Critical aspects in nano-QSAR/QSPR modeling
Nano-QSAR/QSPR development
The adaptation of OECD principles for nano-QSAR/QSPR modeling
Conclusions
Chapter 36. In silico and in vivo ecotoxicity—QSAR-based predictions and experimental assays for the aquatic environment
Introduction
QSAR modeling for ecotoxicity assessment in the aquatic environment
Databases for experimentally determined aquatic ecotoxicity values
In silico tools
Comparison of experimental versus predicted data
Conclusions, perspectives, and future directions
Chapter 37. In vitro to in vivo extrapolation methods in chemical hazard identification and risk assessment
Introduction
Concept and workflow of IVIVE
Application status of IVIVE
Challenges and perspectives
Chapter 38. QSAR models in marine ecotoxicology and risk assessment
Introduction
Development characteristics of QSAR model in marine ecotoxicology
Framework of marine ecological risk assessment
Application of QSAR model in development of marine quality benchmarks
Perspectives
Index

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Huixiao Hong