Nassim Djebli1, Vincent Buchheit2, Neil Parrott2, Elena Guerini2, Yumi Cleary2, Stephen Fowler2, Nicolas Frey2, Li Yu3, François Mercier2, Alex Phipps4, Georgina Meneses-Lorente4. 1. Roche Pharmaceutical Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland. nassim.djebli@roche.com. 2. Roche Pharmaceutical Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 3. Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Jersey City, NJ, USA. 4. Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Roche Products Ltd, Welwyn, UK.
Abstract
BACKGROUND AND OBJECTIVE: Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. METHODS: A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. RESULTS: The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. CONCLUSIONS: The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.
BACKGROUND AND OBJECTIVE: Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. METHODS: A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. RESULTS: The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. CONCLUSIONS: The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.
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