Prediction of human intestinal passive membrane permeability using physiologically informed machine learning models.

Abstract

Predicting human intestinal passive membrane permeability (Peff) is essential for reliable application of physiologically based biopharmaceutics models (PBBMs). Although in vitro cell-based assays and animal studies are widely used, their predictive performance is often limited by inter-laboratory variability and species differences. Furthermore, accurate prediction of intrinsic permeability requires consideration of physiological factors, particularly correction for the unstirred water layer (UWL) and exclusion of transporter-mediated transport. We developed machine learning models to predict human jejunal Peff directly from chemical structure using a physiologically curated dataset derived from in vivo human jejunal permeability data. The dataset was refined by excluding transporter substrates and correcting for UWL resistance to estimate intrinsic cellular permeability (Pcell). To maximize predictive performance while maintaining robustness with a limited dataset, two feature selection strategies were evaluated: a data-driven approach to identify novel structure-permeability relationships and a hypothesis-driven approach based on established physicochemical principles. The practical applicability of the developed models was assessed by incorporating the predicted Peff values into a PBBM. The data-driven model demonstrated superior generalization performance and accurately reproduced observed human plasma concentration-time profiles. These findings demonstrate that the proposed approach can robustly predict intrinsic human intestinal membrane permeability from chemical structure alone.

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