Investigating pharmacokinetic profiles of using machine learning and PBPK modelling.

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (). The publicly available dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of -squared (, coefficient of determination) of 0.68. The model is then applied to compute the of asiaticoside and madecassoside, the parent compounds found in . Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing studies in rats. This pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.