Towards closing gaps in livestock physiologically-based kinetic modeling: Machine learning prediction of total plasma clearance and tissue-plasma partition coefficients, and integration of growth in a physiologically-based kinetic model with a case...

This thesis explores physiologically-based kinetic (PBK) modeling to evaluate toxicological effects and chemical disposition in farm animals. Because necessary input values for livestock are frequently unavailable, the research first developed machine learning models to predict clearance and tissue partitioning. Utilizing molecular features together with anatomical and physiological attributes from livestock and pre-clinical species, these computational tools surpassed traditional mechanistic models and allometric scaling. Additionally, to account for long-term exposure and persistent compounds during rapid juvenile maturation, growth curves across various livestock species were established to integrate developmental changes in anatomy and physiology directly into PBK models.