Advances in Computational Toxicology

1. Front Matter

   Pages i-xvii

   PDF

2. Computational Toxicology Promotes Regulatory Science

   • Rebecca Kusko, Huixiao Hong

   Pages 1-11

3. Methods in Computational Toxicology

   1. Front Matter

      Pages 13-13

      PDF

   2. Background, Tasks, Modeling Methods, and Challenges for Computational Toxicology

      • Zhongyu Wang, Jingwen Chen

      Pages 15-36

   3. Modelling Simple Toxicity Endpoints: Alerts, (Q)SARs and Beyond

      • Richard Williams, Martyn Chilton, Donna Macmillan, Alex Cayley, Lilia Fisk, Mukesh Patel

      Pages 37-55

   4. Matrix and Tensor Factorization Methods for Toxicogenomic Modeling and Prediction

      • Suleiman A. Khan, Tero Aittokallio, Andreas Scherer, Roland Grafström, Pekka Kohonen

      Pages 57-74

   5. Cardio-oncology: Network-Based Prediction of Cancer Therapy-Induced Cardiotoxicity

      • Feixiong Cheng

      Pages 75-97

   6. Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for In Silico Predictive Toxicology

      • Ping Gong, Sundar Thangapandian, Yan Li, Gabriel Idakwo, Joseph Luttrell IV, Minjun Chen et al.

      Pages 99-118

   7. A Review of Feature Reduction Methods for QSAR-Based Toxicity Prediction

      • Gabriel Idakwo, Joseph Luttrell IV, Minjun Chen, Huixiao Hong, Ping Gong, Chaoyang Zhang

      Pages 119-139

   8. An Overview of National Toxicology Program’s Toxicogenomic Applications: DrugMatrix and ToxFX

      • Daniel L. Svoboda, Trey Saddler, Scott S. Auerbach

      Pages 141-157

   9. A Pair Ranking (PRank) Method for Assessing Assay Transferability Among the Toxicogenomics Testing Systems

      • Zhichao Liu, Brian Delavan, Liyuan Zhu, Ruth Robert, Weida Tong

      Pages 159-180

   10. Applications of Molecular Dynamics Simulations in Computational Toxicology

       • Sugunadevi Sakkiah, Rebecca Kusko, Weida Tong, Huixiao Hong

       Pages 181-212

4. Applications in Regulatory Science

   1. Front Matter

      Pages 213-213

      PDF

   2. Applicability Domain: Towards a More Formal Framework to Express the Applicability of a Model and the Confidence in Individual Predictions

      • Thierry Hanser, Chris Barber, Sébastien Guesné, Jean François Marchaland, Stéphane Werner

      Pages 215-232

   3. Application of Computational Methods for the Safety Assessment of Food Ingredients

      • Patra Volarath, Yu (Janet) Zang, Shruti V. Kabadi

      Pages 233-257

   4. Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling

      • Minjun Chen, Jieqiang Zhu, Kristin Ashby, Leihong Wu, Zhichao Liu, Ping Gong et al.

      Pages 259-278

   5. Predictive Modeling of Tox21 Data

      • Ruili Huang

      Pages 279-297

   6. In Silico Prediction of the Point of Departure (POD) with High-Throughput Data

      • Dong Wang

      Pages 299-313

   7. Molecular Modeling Method Applications: Probing the Mechanism of Endocrine Disruptor Action

      • Xianhai Yang, Huihui Liu, Rebecca Kusko

      Pages 315-335

   8. Xenobiotic Metabolism by Cytochrome P450 Enzymes: Insights Gained from Molecular Simulations

      • Zhiqiang Fu, Jingwen Chen

      Pages 337-364

This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making. The authors from various government agencies such as FDA, NCATS and NIEHS industry, and academic institutes share their real-world experience and discuss most current practices in computational toxicology and potential applications in regulatory science. Among the topics covered are molecular modeling and molecular dynamics simulations, machine learning methods for toxicity analysis, network-based approaches for the assessment of drug toxicity and toxicogenomic analyses. Offering a valuable reference guide to computational toxicology and potential applications in regulatory science, this book will appeal to chemists, toxicologists, drug discovery and development researchers as well as to regulatory scientists, government reviewers and graduate students interested in thisfield.

• Computational Toxicology
• Regulatory Science Toxicology
• Machine Learning Toxicology
• In Silico Toxicology
• Deep Learning for Toxicity Prediction
• Deep Learning Toxicology
• Toxicity Prediction
• Toxicity Modeling
• Toxicity Analysis
• Toxicology Simulations
• Database Toxicology
• Toxicogenomics
• Endocrine Disruptors
• Mixture Toxicity
• Regulatory Science

• National Center for Toxicological Research, US Food and Drug Administration, Jefferson, USA

  Huixiao Hong

Dr. Huixiao Hong is the Chief of Bioinformatics Branch at the Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), FDA, Arkansas, USA and an Adjunct Professor at the University of Arkansas at Little Rock. He received his Ph.D. in computational chemistry from Nanjing University. Before he joined NCTR in 2007, Dr. Hong worked as the Manager of Bioinformatics Division at ICF International, as a senior computational scientist in ROW Science and held a research scientist position at Sumitomo Chemical Company, Japan. He was also a visiting scientist at National Cancer Institute at National Institutes of Health (NIH), USA and at Maxwell Institute in Leeds University, UK. Dr. Hong was an Associated Professor at Nanjing University in China. His research interests span the areas of chemoinformatics, computational chemistry, next-generation sequencing data analysis, genome-wide association studies, proteomics, and systems biology. Dr. Huixiao Hong has published more than 180 scientific manuscripts and received many awards during his career.

Policies and ethics