PURPOSE: Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P450 3A4 to an active metabolite (SU12662). The objective of this analysis was to assess sunitinib and SU12662 pharmacokinetics and to identify covariates that might explain variability in exposure following oral administration. EXPERIMENTAL DESIGN: Data from 590 subjects (73 volunteers and 517 patients) in 14 studies were analyzed. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling to estimate population pharmacokinetic parameters, as well as relationships between these parameters and gender, race, age, weight, creatinine clearance, Eastern Cooperative Oncology Group score, and tumor type. Simulations were done to determine the predicted effect of these covariates on exposure. RESULTS: Separate models were developed for sunitinib and SU12662 (each a two-compartment model with first-order absorption and elimination). Sunitinib parameters were estimated as CL/F, 51.8 L/h and Vd/F(central), 2,030 liters. SU12662 parameters were estimated as CL/F, 29.6 L/h and Vd/F(central), 3,080 liters. Tumor type (except acute myeloid leukemia), Asian race, gender, body weight, and elevated Eastern Cooperative Oncology Group score described a portion of the variability in CL/F for sunitinib and metabolite; gender and body weight explained some of the variability in Vd/F(central) for sunitinib and metabolite. Among patients, the predicted changes in sunitinib and metabolite AUC and C(max) as a result of the individual covariates ranged up to 17%. CONCLUSION: The magnitude of the predicted changes in exposure with the covariates studied minimizes the necessity for dose adjustment in any of these subpopulations.
PURPOSE:Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P450 3A4 to an active metabolite (SU12662). The objective of this analysis was to assess sunitinib and SU12662 pharmacokinetics and to identify covariates that might explain variability in exposure following oral administration. EXPERIMENTAL DESIGN: Data from 590 subjects (73 volunteers and 517 patients) in 14 studies were analyzed. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling to estimate population pharmacokinetic parameters, as well as relationships between these parameters and gender, race, age, weight, creatinine clearance, Eastern Cooperative Oncology Group score, and tumor type. Simulations were done to determine the predicted effect of these covariates on exposure. RESULTS: Separate models were developed for sunitinib and SU12662 (each a two-compartment model with first-order absorption and elimination). Sunitinib parameters were estimated as CL/F, 51.8 L/h and Vd/F(central), 2,030 liters. SU12662 parameters were estimated as CL/F, 29.6 L/h and Vd/F(central), 3,080 liters. Tumor type (except acute myeloid leukemia), Asian race, gender, body weight, and elevated Eastern Cooperative Oncology Group score described a portion of the variability in CL/F for sunitinib and metabolite; gender and body weight explained some of the variability in Vd/F(central) for sunitinib and metabolite. Among patients, the predicted changes in sunitinib and metabolite AUC and C(max) as a result of the individual covariates ranged up to 17%. CONCLUSION: The magnitude of the predicted changes in exposure with the covariates studied minimizes the necessity for dose adjustment in any of these subpopulations.
Authors: Huixin Yu; Neeltje Steeghs; Jacqueline S L Kloth; Djoeke de Wit; J G Coen van Hasselt; Nielka P van Erp; Jos H Beijnen; Jan H M Schellens; Ron H J Mathijssen; Alwin D R Huitema Journal: Br J Clin Pharmacol Date: 2015-05 Impact factor: 4.335
Authors: Ying Chen; Akiyuki Suzuki; Michael A Tortorici; May Garrett; Robert R LaBadie; Yoshiko Umeyama; Yazdi K Pithavala Journal: Invest New Drugs Date: 2015-02-08 Impact factor: 3.850
Authors: Gracia M Amaya; Rebecca Durandis; David S Bourgeois; James A Perkins; Arsany A Abouda; Kahari J Wines; Mohamed Mohamud; Samuel A Starks; R Nathan Daniels; Klarissa D Jackson Journal: Chem Res Toxicol Date: 2018-06-18 Impact factor: 3.739