Machine Learning and Artificial Intelligence in Toxicology and Environmental Health

Dr. Zhoumeng Lin is an Assistant Professor of Pharmacology and Toxicology in the Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University. He is a Diplomate of American Board of Toxicology (DABT), the Coordinator of the Certara Center of Excellence for Model-informed Drug Development at Kansas State University, and the Principal Investigator at the Midwest Regional Center of the Food Animal Residue Avoidance Databank (FARAD) program. Dr. Lin has more than 8 years of research experience in PBPK modeling for environmental chemicals, drugs, and nanoparticles in laboratory rodents, food-producing animals, companion animals and humans. He received graduate training in Toxicology and PBPK modeling from Dr. Nikolay M. Filipov and Dr. Jeffrey W. Fisher at The University of Georgia. He received postdoc training in Pharmacology, Toxicology, and PBPK modeling from Dr. Jim E. Riviere, Dr. Nancy A. Monteiro-Riviere and Dr. Ronette Gehring at Kansas State University. He learned how to teach PBPK modeling from the PBPK Modeling Workshop for Beginners offered by Dr. Raymond S. H. Yang at Colorado State University. His current research focuses on developing PBPK models and other computational methods to address issues in food safety assessment, toxicology, and risk assessment. He teaches an online course entitled “Physiologically Based Pharmacokinetic Modeling” every Spring semester and another online course entitled “Basic and Applied Pharmacokinetics” in the Fall semester through K-State Global Campus. Dr. Wei-Chun Chou is a Research Assistant Professor of the Department of Environmental and Global Health and a member of the Center for Environmental and Human Toxicology (CEHT) at the University of Florida. He received his PhD in Biomedical Engineering and Environmental Sciences from the National Tsing Hua University, Taiwan in 2013. He completed his postdoctoral training in the Institute of Computational Comparative Medicine at Kansas State University in 2021. His research focused on the development of computational models for prediction of chemical toxicity and its application on human health risk assessments without resorting to animal testing. The goals are accomplished by integrating in vitro high-throughput toxicity screening data, physiologically based pharmacokinetic (PBPK) modeling, machine learning and artificial intelligence to quantitatively describe the relationships between environmental exposure and mechanisms that cause adverse effects in human populations. He has received several awards and honors from the Society of Toxicology (SOT), including the Andersen-Clewell Trainee Award of the Biological Modeling Specialty Section and Best Paper Award of Risk Assessment Specialty Section.

1. Applications of machine learning and artificial intelligence in toxicology and environmental health
2. Basics of machine learning and artificial intelligence methods in toxicology and environmental health
3. Application of machine learning and AI methods in predictions of absorption, distribution, metabolism, excretion (ADME) properties
4. Application of machine learning and AI methods in developing physiologically based pharmacokinetic (PBPK) models
5. Application of machine learning and AI methods in predictions of different toxicity endpoints
6. Application of machine learning and AI methods in developing quantitative structure-activity relationship (QSAR) models
7. Application of machine learning and AI methods in quantitative adverse outcome pathway (qAOP) analysis
8. Application of machine learning and AI methods in toxicogenomics analysis
9. Application of machine learning and AI methods in analyzing high[1]throughput in vitro assays
10. Application of machine learning and AI methods in high-throughput cell imaging and analysis
11. Application of machine learning and AI methods in exposure and toxicity assessment of nanoparticles
12. Application of machine learning and AI methods in ecotoxicity assessment
13. Application of machine learning and AI methods in air pollution assessment and health outcome analysis
14. Application of machine learning and AI methods in climate changes and health outcome analysis
15. Application of machine learning and AI methods in predicting health outcomes based on human biomonitoring data
16. Databases for applications of machine learning and AI methods in toxicology and environmental health
17. Application of machine learning and AI methods in food safety assessment
18. Application of machine learning and AI methods in human health risk assessment of environmental chemicals
19. Application of machine learning and AI methods in toxicity and risk assessment of chemical mixtures
20. Data sharing, collaboration, challenges, and future direction of machine learning and AI methods in toxicology and environmental health
21. Regulatory and Ethical Consideration of machine learning and AI methods in toxicology and environmental health