Tomoyuki Mizuno1, Masahide Fukudo, Tsuyoshi Fukuda, Tomohiro Terada, Min Dong, Tomomi Kamba, Toshinari Yamasaki, Osamu Ogawa, Toshiya Katsura, Ken-Ichi Inui, Alexander A Vinks, Kazuo Matsubara. 1. *Department of Clinical Pharmacology and Therapeutics, Kyoto University Hospital, Japan; †Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center; ‡Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio; and §Department of Urology, Graduate School of Medicine, Kyoto University, Japan. Dr Terada is now with the Department of Pharmacy, Shiga University of Medical Science Hospital, Japan; Dr Katsura is now with the College of Pharmaceutical Sciences, Ritsumeikan University, Shiga, Japan; and Dr Inui is now with the Kyoto Pharmaceutical University, Japan.
Abstract
BACKGROUND: Sunitinib, a multitargeted tyrosine kinase inhibitor, offers favorable therapeutic outcomes to patients with advanced renal cell carcinoma. However, to maximize the clinical benefits, an effective therapeutic management strategy with dose optimization is essential. The objectives of this analysis were to describe the pharmacokinetics (PK) of sunitinib by a population PK approach and to quantitatively evaluate the effect of potential predictive factors including ABCG2 genotype on the PK of sunitinib. METHODS: Plasma concentration-time profiles at 3 consecutive days including a total of 245 sunitinib plasma concentrations were available from 19 Japanese patients with renal cell carcinoma. Blood samples were collected on days 2, 8, and 15 after the start of the therapy. Population PK analysis was performed using NONMEM 7.2. Body weight, gender, and genotype of ABCG2 421C>A were evaluated as potential covariates. Interoccasion variability (IOV) among the 3 sampling days was also assessed as a random effect parameter. RESULTS: The sunitinib PK profiles were best described by a 1-compartment model with first-order absorption. The ABCG2 421C>A genotype was identified as a significant covariate for the prediction of oral clearance (CL/F). No significant improvement in model fit was observed by including body weight and/or gender. A systematic difference in estimated population CL/F was observed between days 2 and 8, which was quantified as approximately 30% decrease over time. This difference was described as a covariate for CL/F in the model. IOV included as a random effect parameter significantly improved the model fit. CONCLUSIONS: This analysis provides a population PK model of sunitinib with the ABCG2 421C>A genotype as a predictive covariate for CL/F. It also suggests that IOV and change of CL/F over time need to be considered to predict the sunitinib PK more accurately. These findings will be implemented to optimize the pharmacotherapy of sunitinib.
BACKGROUND:Sunitinib, a multitargeted tyrosine kinase inhibitor, offers favorable therapeutic outcomes to patients with advanced renal cell carcinoma. However, to maximize the clinical benefits, an effective therapeutic management strategy with dose optimization is essential. The objectives of this analysis were to describe the pharmacokinetics (PK) of sunitinib by a population PK approach and to quantitatively evaluate the effect of potential predictive factors including ABCG2 genotype on the PK of sunitinib. METHODS: Plasma concentration-time profiles at 3 consecutive days including a total of 245 sunitinib plasma concentrations were available from 19 Japanese patients with renal cell carcinoma. Blood samples were collected on days 2, 8, and 15 after the start of the therapy. Population PK analysis was performed using NONMEM 7.2. Body weight, gender, and genotype of ABCG2 421C>A were evaluated as potential covariates. Interoccasion variability (IOV) among the 3 sampling days was also assessed as a random effect parameter. RESULTS: The sunitinib PK profiles were best described by a 1-compartment model with first-order absorption. The ABCG2 421C>A genotype was identified as a significant covariate for the prediction of oral clearance (CL/F). No significant improvement in model fit was observed by including body weight and/or gender. A systematic difference in estimated population CL/F was observed between days 2 and 8, which was quantified as approximately 30% decrease over time. This difference was described as a covariate for CL/F in the model. IOV included as a random effect parameter significantly improved the model fit. CONCLUSIONS: This analysis provides a population PK model of sunitinib with the ABCG2 421C>A genotype as a predictive covariate for CL/F. It also suggests that IOV and change of CL/F over time need to be considered to predict the sunitinib PK more accurately. These findings will be implemented to optimize the pharmacotherapy of sunitinib.
Authors: Y L Teo; H L Wee; X P Chue; N M Chau; M-H Tan; R Kanesvaran; H L Wee; H K Ho; A Chan Journal: Pharmacogenomics J Date: 2015-03-17 Impact factor: 3.550
Authors: Kim Westerdijk; Ingrid M E Desar; Neeltje Steeghs; Winette T A van der Graaf; Nielka P van Erp Journal: Br J Clin Pharmacol Date: 2020-01-21 Impact factor: 4.335