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February 2, 2026

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

Key Points

The study aims to evaluate the efficiency of a high-throughput physiologically based pharmacokinetic model for predicting drug properties using machine learning inputs.
Utilized a corporate high-throughput pharmacokinetic application called SwiftPK.
Leveraged machine learning to replace in vitro data with in silico predictions.
Evaluated predictions for over 9000 rodent pharmacokinetic data points across ten primary and secondary endpoints.
Filtered compounds based on structural predictions of hepatic metabolism.
Most pharmacokinetic endpoints were predicted within a three- to four-fold error.
Performance improved for compounds cleared by hepatic metabolism.
High confidence machine learning inputs correlated with better prediction quality.

Abstract

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 in vitro 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 in vitro data by high-quality in silico 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 in vitro parameter inputs were replaced with in silico 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.

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Cite This Study

Bassani et al. (Fri,) studied this question.

synapsesocial.com/papers/6980feabc1c9540dea810f33 https://doi.org/https://doi.org/10.1021/acs.molpharmaceut.5c01317

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