Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling

Abstract

Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool for quantitating and understanding the fate of drug and drug carriers in complex living systems. It is particularly valuable in situations where data are difficult to obtain due to cost, time, or ethical constraints. Recent advances in PBPK modeling have greatly improved their accuracy in modeling in vivo and clinical data, especially in special populations (e.g., pediatric and geriatric subjects), which consequently enhanced their utility in drug development. Nevertheless, current PBPK models remain limited by our ability to ascertain complex biological mechanisms and/or physiological processes, often resulting in many critical but unknown parameters or parameters with large uncertainty. Machine learning (ML) and applications of broader artificial intelligence (AI) tools that facilitate parameter estimation, model learning, database mining, and uncertainty quantification not only offer the potential to address the shortcomings of PBPK modeling, but also introduce opportunities for enabling earlier use of PBPK modeling in the drug development process. Here, we summarize ML-influenced advances in PBPK modeling and discuss our expectations of the likely avenues for future ML/AI contributions to PBPK modeling.