Animal versus human oral drug bioavailability: machine learning says yes – they correlate

Abstract

Oral bioavailability (%F) is a key determinant of drug efficacy and a critical decision-making parameter in early-stage drug development. Current prediction of human oral %F often relies on preclinical animal studies, which are costly, ethically disputed and do not necessarily correlate with human %F. Machine learning (ML), an emerging in silico technology, has been leveraged to predict human %F. However, previously published ML approaches have often relied on binary classification or achieved limited regression performance, with R2 values ranging from -0.36 to 0.50. In this study, we demonstrate that an ML pipeline can indeed be used to predict accurately human oral %F using preclinical data, potentially reducing the need for animal experiments. A total of 192 FDA-approved compounds were curated, which were discovered to span a diverse chemical space. Model inputs included molecular descriptors, structural fingerprints, published animal %F and cytochrome P450-aware metabolic feature, with the latter designed to account for metabolism-driven interspecies variability. Five-fold cross-validation, repeated 100 times, yielded an R2 of 0.766 (RMSE = 16.13%), substantially outperforming all previously published models and individual animal species univariate correlation, including non-human primates (R2 = 0.691). Notably, high performance was maintained without non-human primate data, with rat and dog %F combined achieving R2 = 0.723 ± 0.016, reducing dependence on ethically and logistically demanding primate studies. Permutation feature importance analysis identified lipophilicity, surface area and CYP features as informative predictors, providing mechanistic interpretability alongside predictive accuracy. These results demonstrate that a combination of feature engineering and a comprehensive ML pipeline, can achieve precision and reproducible prediction of human oral %F from routinely collected preclinical data. The impact of this study includes the potential to reduce late-stage attrition and accelerate the translation of drug candidates to the clinic.