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

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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.

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