ArticleCPT: pharmacometrics & systems pharmacology2026
Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.
Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
pH-dependent drug-drug interactions (DDIs) commonly occur when acid-reducing agents (ARAs) are co-administered with weakly basic drugs. Although physiologically based pharmacokinetic (PBPK) modeling effectively evaluates such DDIs, its use is limited by reliance on costly commercial software. This study developed a PBPK-informed machine learning model to support early assessment of pH-dependent DDI risk in drug development. PBPK models were built for 14 representative weakly basic drugs using literature-derived parameters to identify eight key determinants (e.g., solubility and pKa). Based on these distributions, virtual drugs were generated and simulated under varying gastric pH conditions; compounds with DDI AUC ratios < 0.1 were excluded, yielding 4339 virtual drugs. An extreme gradient boosting (XGBoost) algorithm was used to develop the machine learning model, and an external validation set comprising clinically observed data from an additional eight drugs was employed. The XGBoost model showed excellent internal performance (training: R
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