Abstract
Purpose
To characterize cobimetinib pharmacokinetics and evaluate impact of clinically relevant covariates on cobimetinib pharmacokinetics.
Methods
Plasma samples (N = 4886) were collected from 487 patients with various solid tumors (mainly melanoma) in three clinical studies (MEK4592g, NO25395, GO28141). Cobimetinib was administered orally, once daily on either a 21-day-on/7-day-off, 14-day-on/14-day-off or 28-day-on schedule in a 28-day dosing cycle as single agent or in combination with vemurafenib. Cobimetinib doses ranged from 2.1 to 125 mg. NONMEM was used for pharmacokinetic analysis.
Results
A linear two-compartment model with first-order absorption, lag time and first-order elimination described cobimetinib pharmacokinetics. The typical estimates (inter-individual variability) of apparent clearance (CL/F), central volume of distribution (V2/F) and terminal half-life were 322 L/day (58 %), 511 L (49 %) and 2.2 days, respectively. Inter-occasion variability on relative bioavailability was estimated at 46 %. CL/F decreased with age. V2/F increased with body weight (BWT). However, the impact of age and BWT on cobimetinib steady-state exposure (peak and trough concentrations and AUC following the recommended daily dose of 60 mg 21-day-on/7-day-off) was limited (<25 % changes across the distribution of age and BWT). No significant difference in cobimetinib pharmacokinetics or steady-state exposure was observed between patient subgroups based on sex, renal function, ECOG score, hepatic function tests, race, region, cancer type, and co-administration of moderate and weak CYP3A inducers or inhibitors and vemurafenib.
Conclusion
A population pharmacokinetic model was developed for cobimetinib in cancer patients. Covariates had minimal impact on steady-state exposure, suggesting no need for dose adjustments and supporting the recommended dose for all patients.
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Kelong Han, Jin Jin, Stephen Eppler, Nicholas Choong, Stephen P Hack, Nalin Tikoo, Mark Dresser, Luna Musib and Nageshwar Budha receive salary from Genentech and hold stocks of Roche Pharmaceuticals; in addition, Jin Jin and Luna Musib also hold stock in Eli Lilly; Mathilde Marchand and Rene Bruno receive salary from Pharsight Consulting Services.
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Supplementary Fig. 1
Correlation between cobimetinib pharmacokinetic parameters from the base model and age and body weight. Blue lines represent the locally weighted scatterplot smoothing (LOESS). CL/F = apparent clearance (L/day); V2/F = apparent central volume of distribution (L). (PDF 116 kb)
Supplementary Fig. 2
Correlation between cobimetinib apparent clearance CL/F (L/day) from the final model and clinically relevant covariates that were not statistically significant. (PDF 179 kb)
Supplementary Fig. 3
Correlation between cobimetinib apparent central volume of distribution V2/F (L) from the final model and clinically relevant covariates that were not statistically significant. (PDF 173 kb)
Supplementary Fig. 4
Goodness-of-fit plots for the final PK model of cobimetinib. Gray lines: LOESS (locally weighted scatterplot smoothing; some of the smooth lines were constrained to rich data). (PDF 1357 kb)
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Han, K., Jin, J.Y., Marchand, M. et al. Population pharmacokinetics and dosing implications for cobimetinib in patients with solid tumors. Cancer Chemother Pharmacol 76, 917–924 (2015). https://doi.org/10.1007/s00280-015-2862-0
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DOI: https://doi.org/10.1007/s00280-015-2862-0