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Published on: May 9, 2025
Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory
Nassim Djebli1, Vincent Buchheit2, Neil Parrott2
1Roche Pharmaceutical Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland. nassim.djebli@roche.com.
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.
Insights
This study developed a physiologically-based pharmacokinetic (PBPK) model for entrectinib and its metabolite M5. The model accurately predicts how CYP3A4 inhibitors and inducers affect entrectinib exposure, supporting safe dosing recommendations.
Area of Science:
- Pharmacology
- Pharmacokinetics
- Oncology
Background:
- Entrectinib, a kinase inhibitor for oncology, is primarily metabolized by CYP3A4.
- CYP3A4 activity significantly impacts entrectinib and its active metabolite M5 pharmacokinetics.
- Drug-drug interactions with CYP3A4 modulators require careful consideration for entrectinib dosing.
Purpose of the Study:
- To develop a combined physiologically-based pharmacokinetic (PBPK) model for entrectinib and its active metabolite M5.
- To predict the pharmacokinetic changes of entrectinib and M5 when co-administered with CYP3A4 inhibitors or inducers.
- To support the development of dosing recommendations for entrectinib in combination therapy.
Main Methods:
- A PBPK model was constructed using the Simcyp Simulator.
- The model was refined through sensitivity analysis and non-linear mixed effects modeling.
- Model qualification involved comparison with clinical data from a drug-drug interaction study with itraconazole.
Main Results:
- The validated PBPK model demonstrated good predictive performance for entrectinib and M5.
- Simulations predicted that moderate CYP3A4 inhibitors could increase entrectinib exposure by 2.2- to 3.1-fold.
- Moderate CYP3A4 inducers were predicted to decrease entrectinib exposure by 45% to 79%.
Conclusions:
- PBPK modeling successfully predicted entrectinib and M5 pharmacokinetics under various CYP3A4 interaction scenarios.
- The model facilitated the derivation of dosing recommendations for entrectinib with CYP3A4 inhibitors/inducers.
- PBPK modeling served as a valuable tool, potentially reducing the need for extensive clinical drug-drug interaction studies.
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