Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

Enhancing the precision of drug–drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (<i>f</i>_<i>m</i>) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. The <i>f</i>_<i>m</i> values were determined for 33 approved drugs using a human liver microsome for <i>in vitro</i> measurements and the ADMET Predictor software for in silico predictions. Subsequently, these <i>f</i>_<i>m</i> values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUC_{combo with ketoconazole}/AUC_{dosing alone}), and the accuracy of these predictions was evaluated by comparison with observed AUCR. The integration of <i>in vitro f</i>_<i>m</i> method demonstrates superior performance compared to the in silico <i>f</i>_<i>m</i> method and <i>f</i>_<i>m</i> of 100% method. Under the Guest-limits criteria, the integration of <i>in vitro f</i>_<i>m</i> achieves an accuracy of 76%, while the in silico <i>f</i>_<i>m</i> and <i>f</i>_<i>m</i> of 100% methods achieve accuracies of 67% and 58%, respectively. Our study highlights the importance of <i>in vitro f</i>_<i>m</i> data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico <i>f</i>_<i>m</i> in predicting DDIs.