An Integrated AI-PBPK Platform for Predicting Drug In Vivo Fate and Tissue Distribution in Human and Inter-Species Extrapolation.

Abstract

Optimal pharmacokinetic (PK) profile, including tissue distribution, is pivotal for a drug achieving success in clinical trials. Traditionally, PK estimation in early drug development has relied on extensive in vitro and in vivo testing to assess drug-like properties, a process that is not only costly and time-consuming but also limited in its ability to evaluate the synergistic effects of multiple properties. This study aims to develop an integrated artificial intelligence (AI) and physiologically based pharmacokinetic (PBPK) platform to rapidly estimate drug in vivo fate based solely on molecular structures. The AI models were trained to predict eight types of key properties (solubility, pKa values, crystal density, intrinsic dissolution rate, apparent permeability, protein unbound fraction, plasma clearance, and tissue partition coefficients for 15 organs), from which the PBPK model forecasted PK curves without further training. The AI-PBPK approach was validated against human PK data of 71 intravenous and 606 oral administrations collected from the PK-DB database. The results were robust, with most of the AUC predictions falling within two and threefold error ranges. The AI-PBPK model also accurately predicted drug organ selectivity, and for drugs exhibiting high plasma clearance, predictions were optimized through an inter-species extrapolation approach. This study illustrates that the developed modeling strategy adeptly addresses pivotal PK challenges in drug discovery and aligns with contemporary drug development processes. The modeling system can guide candidate selection, advancing more drugs with favorable PK profiles into clinical trials, thereby significantly enhancing the efficiency of drug development.