Predicting Drug–Drug Interactions Involving Multiple Mechanisms Using Physiologically Based Pharmacokinetic Modeling: A Case Study With Ruxolitinib

Abstract

Physiologically based pharmacokinetic modeling was applied to characterize the potential drug-drug interactions for ruxolitinib. A ruxolitinib physiologically based pharmacokinetic model was constructed using all baseline PK data in healthy subjects, and verified by retrospective predictions of observed drug-drug interactions with rifampin (a potent CYP3A4 inducer), ketoconazole (a potent CYP3A4 reversible inhibitor) and erythromycin (a moderate time-dependent inhibitor of CYP3A4). The model prospectively predicts that 100-200 mg daily dose of fluconazole, a dual inhibitor of CYP3A4 and 2C9, would increase ruxolitinib plasma concentration area under the curve by ∼two-fold, and that as a perpetrator, ruxolitinib is highly unlikely to have any discernible effect on digoxin, a sensitive P-glycoprotein substrate. The analysis described here illustrates the capability of physiologically based pharmacokinetic modeling to predict drug-drug interactions involving several commonly encountered interaction mechanisms and makes the case for routine use of model-based prediction for clinical drug-drug interactions. A model verification checklist was explored to harmonize the methodology and use of physiologically based pharmacokinetic modeling.