High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a Large <i>In Vivo</i> Pharmacokinetics Data Set

Accurate prediction of the pharmacokinetic (PK) properties of small-molecule drug candidates is a critical aspect of pharmaceutical research. Fast and reliable PK predictions can accelerate compound optimization cycles, reduce animal testing, and enhance the quality of molecules advancing to human studies. Although physiologically based PK (PBPK) models are well-established for compound selection, their application in early discovery faces limitations due to low throughput and the requirement for substantial <i>in vitro</i> data. Recently, high-throughput PBPK (HT-PBPK) methods have become possible, offering scalable, parallel PBPK simulations that can be executed on thousands of compounds within minutes. Additionally, advancements in machine learning (ML) have enabled the substitution of <i>in vitro</i> data by high-quality <i>in silico</i> predictions that are based solely on chemical structures. In this study, the performance of a corporate HT-PBPK application, called SwiftPK, that leverages the HTPK simulation module included in a commercial software package was evaluated for predicting ten primary and secondary PK endpoints for a large (>9000 compounds) set of rodent PK data. Utilizing a corporate ML pipeline, all <i>in vitro</i> parameter inputs were replaced with <i>in silico</i> predictions. This approach is particularly relevant for early stage project phases, such as lead identification, as well as for external collaborations where experimental data are unavailable. The findings demonstrate the highly predictive performance of the HT-PBPK approach, with most endpoints predicted within a three- to four-fold error. Performance improves after filtering for compounds that are predicted, based on structure alone, to be cleared by hepatic metabolism (Extended Clearance Classification System class 2) and when using ML inputs that demonstrate high confidence. The results highlight the key prerequisites for successful application in early phase projects: predicted primary elimination pathway accuracy and prediction quality. This study is expected to inspire more organizations to incorporate HT-PBPK into their discovery pipelines, expediting the development of safe and effective novel medicines for patients.