Predictive Analytics for Toxicology

Auteur

Luis G. Valerio, Jr. has been a toxicologist for over 26 years. He has served in the public health arena for 20 years. He earned his Ph.D. in Pharmaceutical Science from the University of Colorado in Denver and is a board-certified toxicologist in the Academy of Toxicological Sciences. He was a National Research Council, National Academies of Sciences, Engineering, and Medicine Fellow in Gastroenterology at the University of Colorado, School of Medicine, Department of Medicine, Division of Gastroenterology and Hepatology. Following his academic research on the molecular regulation of cytochrome P450 1A1 and 1A2, he served as a National Science Foundation International Postdoctoral Research Fellow at the Autonomous University of Barcelona, Spain where he studied the biochemistry and metabolism of alcohol dehydrogenase enzymes. He served as a temporary advisor for the World Health Organization and on international scientific committees assessing the safety of chemicals added to food. He has worked on quality standards-setting committees for human pharmaceuticals. He has several years of industry experience in consumer product safety and chemical toxicology. He has overseen clinical studies to predict dermal hazards of consumer products. He has served as a Scientific Director of a food chemical and manufacturing trade association. He is currently Editor-in-Chief of the journals, Toxicology Mechanisms and Methods and Expert Opinion on Drug Metabolism & Toxicology (Taylor & Francis Group). He enjoys writing and has published 100 articles on a variety of topics in mechanisms of chemical toxicity, xenobiotic metabolism, safety science, and computational science.

Texte du rabat

This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology.

Résumé

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alternative testing initiatives. Predictive Analytics for Toxicology: Applications in Discovery Science provides a comprehensive overview of the application of predictive analytics in the field of toxicology, highlighting its role and applications in discovery science.

This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology. It underscores the importance of predictive toxicology in proposing and explaining adverse outcomes resulting from human exposures to specific toxicants, especially when experimental and observational data on the toxicant are incomplete or unavailable.

Key features:

• Includes a plain language description of predictive analytics in toxicology adding an overview of the wide range of applications
• Examines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine learning
• Opens the hood on AI and its applications in toxicology
• Features coverage on how in silico toxicity predictions are translational science tools The book integrates strategies and practices of predictive toxicology and offers practical information that students and professionals of the toxicology, chemical, and pharmaceutical industries will find essential. It fulfills the expectations of student researchers seeking to learn predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine learning, and inspire students and seasoned scientists interested in automated science to pick up new research using predictive in silico models to evaluate chemical-induced toxicity.

With its focus on practical applications and real-world examples, this book serves as a guide for navigating the complex issues and practices of discovery toxicology. It is an essential resource for those interested in computer-based methods in toxicology, providing valuable insights into the use of predictive analytics.

Contenu

About the Author

Preface

1 Finding No Limit: Discovery Science

1.1 Introduction

1.2 Predictive Analytics

1.3 Data Service

1.4 Examination of Data Quality

1.5 Program Development Intelligence

1.6 Data Visualization

1.7 Decision Analysis

1.8 Conclusion

2 Exploring the Science of Prediction

2.1 Introduction

2.2 The Ubiquitous Application of Prediction Science in Society

2.3 Understanding the Science of Prediction through Machine Learning

2.4 Predictive Modeling of the Human Brain

2.4.1 Human Learning and Logic

2.4.2 Computerized Modeling of Human Learning

2.5 Major Stages in Model Development and Prediction Testing

2.5.1 Model Objectives and Scope

2.5.2 Data Selection

2.5.3 Data Curation

2.5.4 Data Cleaning

2.5.5 Feature Selection

2.5.6 Model Construction

2.5.7 Model Testing

2.6 Current Trends in Machine Learning to Make Predictions

2.7 Conclusions

3 Predictive Analytic Approaches

3.1 Introduction

3.2 Descriptive Analytics

3.3 Diagnostic Analytics

3.4 Predictive Analytics

3.5 Prescriptive Analytics

3.6 Characteristics of a Predictive Model

3.7 Categories of Machine Learning Approaches

3.7.1 Supervised Machine Learning

3.7.2 Unsupervised Machine Learning

3.7.3 Semi- supervised Machine Learning

3.8 Machine Learning Algorithms

3.8.1 Unsupervised Machine Learning Techniques

3.8.2 Supervised Machine Learning

3.9 Conclusions

4 The Role of Predictive Analytics in Translational Science

4.1 Introduction

4.2 What Is Translational Science?

4.3 The Rapid Pace of Translational Science

4.4 Understanding Translational Science Research Tools

4.4.1 Relevant Targets for Translational Science Tools

4.5 Predictive Analytics in Advancing Translational Science

4.6 Development of Clinical Decision- Support Models

4.7 Conclusions

5 Toxicology and Artificial Intelligence

5.1 Introduction

5.2 Fundamental Ideas with AI

5.3 Toxicological Research with AI

5.3.1 Toxicogenomics

5.3.2 Disease and Health-Based Research

5.3.3 Prediction of Toxicity

5.4 Limitations and Considerations of AI

5.4.1 AI and the Human Workforce

5.4.2 AI and Human Behavior

5.4.3 AI and Ethical Considerations

5.4.4 AI and Transparency

5.4.5 Cost of Implementing AI

5.4.6 An Environmental Dichotomy

5.5 Risk Assessment and AI

5.6 Conclusions

6 The Explosion of Computational Approaches for Hazard Identification

6.1 Introduction

6.2 Initiatives and Large- Scale Programs Supporting Computational Approaches for Hazard Identification of Chemicals

6.2.1 Toxicity Testing in the 21st Century: A Vision and Strategy Tox21

6.2.2 Other Initiatives and Major Activities Using Predictive Analytics Approaches for Toxicology

6.3 Widespread Availability of Public Data, Tools, and Resources for Modeling

6.4 Chemical Hazard Identification, Safety, and Risk- Driven Needs

6.5 Data Science: Uses and Definition

6.6 Conclusions

7 Opportunities for Computational Modeling in Toxicology Prediction

7.1 Introduction

7.2 Current Applications of Computational Modeling in Toxicity Prediction

7.3 Opportunistic Areas in Computational Modeling for Toxicology Prediction

7.3.1 Modeling High- Value Organ- Specific Toxicities

7.3.2 In vitro Modeling

7.3.3 Mixtures Toxicity Modeling

7.3.4 Metabolic Reactions Modeling

7.3.5 Nanotoxicology Modeling

7.3.6 Modeling Based on Clinical Toxicology Data

7.3.7 Epidemiology Data- Based Modeling

7.3.8 Advanced Artificial Intelligence (AI) Systems Modeling

7.4 Looking Forward

7.5 Conclusions …