Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.

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: R2 = 1.00, MAPE = 0.99; test: R2 = 0.98, MAPE = 2.64). When evaluated using an external validation set comprising clinically observed data from eight drugs, 100% of the predicted values fell within the 0.5-2.0-fold range of the observed clinical values. For DDI AUC risk classification, the XGBoost model achieved an accuracy of 87.5% (7/8). This PBPK-informed ML framework enables efficient screening of pH-dependent DDI risk for weakly basic drugs co-administered with ARAs. The freely accessible web tool (https://ddi-antacid.xy3yx.com/), integrating structure-based ADMETlab3.0 estimation, offers a practical complement to conventional PBPK modeling for early drug development.