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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 ( fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. The  fmvalues were determined for 33 approved drugs using a human liver microsome for  in vitro  measurements and the ADMET Predictor software for in silico predictions. Subsequently, these  fmvalues 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  in vitro fmmethod demonstrates superior performance compared to the in silico  fmmethod and  fmof 100% method. Under the Guest-limits criteria, the integration of  in vitro fmachieves an accuracy of 76%, while the in silico  fmand  fmof 100% methods achieve accuracies of 67% and 58%, respectively. Our study highlights the importance of  in vitro fmdata to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico  fmin predicting DDIs.

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