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Evaluation of Cytochrome P450 3A4-Mediated Drug–Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation

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Abstract

Background and Objectives

Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug–drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions.

Methods

A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole–cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole–cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics.

Results

The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole–cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78 % of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration–time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72 %, respectively.

Conclusion

This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

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Author contributions

NRB, TJ, and YC participated in model design; NRB, TJ, SE, LM, and YC collected data and ran simulations; NRB, TJ, YC, and JYJ performed data analysis and wrote the manuscript; NRB, TJ, LM, SE, MD, YC, and JYJ reviewed the manuscript and approved it for submission.

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Authors and Affiliations

  1. Clinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA

    Nageshwar R. Budha, Tao Ji, Luna Musib, Steve Eppler, Mark Dresser & Jin Y. Jin

  2. Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA

    Yuan Chen

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  1. Nageshwar R. Budha
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  2. Tao Ji
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  3. Luna Musib
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  7. Jin Y. Jin
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Corresponding author

Correspondence to Yuan Chen.

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Compliance with Ethical Standards

This study was funded by Genentech (a member of the Roche group). All authors (NRB, TJ, LM, SE, MD, YC, and JYJ) were employees of Genentech when this work was carried out. They have no other conflicts of interest to declare.

Electronic supplementary material

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40262_2016_412_MOESM1_ESM.tif

Supplementary Figure 1: Observed and simulated plasma concentration-time profiles of itraconazole following administration of 200 mg as capsule formulation daily for 4 days

40262_2016_412_MOESM2_ESM.tif

Supplementary Figure 2: Observed and simulated plasma concentration-time profiles of midazolam with and without co-administration of 200 mg of itraconazole capsule QD for 4 days

40262_2016_412_MOESM3_ESM.tif

Supplementary Figure 3: The effect of Qgut on the predicted DDI between cobimetinib and itraconazole in healthy subjects (a) Cmax ratio, (b) AUC ratio

Supplementary material 4 (DOCX 15 kb)

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Budha, N.R., Ji, T., Musib, L. et al. Evaluation of Cytochrome P450 3A4-Mediated Drug–Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation. Clin Pharmacokinet 55, 1435–1445 (2016). https://doi.org/10.1007/s40262-016-0412-5

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  • DOI: https://doi.org/10.1007/s40262-016-0412-5

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