| Autor: |
Pumkathin S; Department of Sustainable Energy and Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand., Hanlumyuang Y; Department of Materials Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand., Wattanathana W; Department of Materials Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand., Laomettachit T; Theoretical and Computational Physics Group, Center of Excellence in Theoretical and Computational Science (TaCS-CoE), King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, Thailand.; Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand., Liangruksa M; National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand. |
| Abstrakt: |
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 ( P eff ). The publicly available P eff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R -squared ( R 2 , coefficient of determination) of 0.68. The model is then applied to compute the P eff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica . 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 in vivo studies in rats. This in silico 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. |
