The Effect of ABCG2 Genotype on the Population Pharmacokinetics of Sunitinib in Patients With Renal Cell Carcinoma
The Effect of ABCG2 Genotype on the Population Pharmacokinetics of Sunitinib in Patients With Renal Cell Carcinoma
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DOI：
10.1097/ftd.0000000000000025
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发表时间：
2014-06-01
影响因子：
2.5
通讯作者：
Matsubara, Kazuo
中科院分区：
文献类型：
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作者：
Mizuno, Tomoyuki;Fukudo, Masahide;Matsubara, Kazuo
背景：舒尼替尼是一种多靶点酪氨酸激酶抑制剂，对晚期肾细胞癌患者具有良好的治疗效果。然而，为了最大限度地发挥临床效益，必须采取有效的治疗管理策略，并进行剂量优化。本分析的目的是描述舒尼替尼的药代动力学（PK）的人口PK方法和定量评估的影响，包括ABCG 2基因型的潜在预测因素对PK的sunitinib.Methods：血浆浓度-时间曲线连续3天，包括245舒尼替尼血浆浓度从19例日本肾细胞癌患者。在治疗开始后第2、8和15天采集血样。使用NONMEM 7.2进行群体PK分析。将体重、性别和ABCG 2 421 C>A基因型作为潜在的协变量进行评估。3个采样日之间的病例间变异性（IOV）也被评估为一个随机效应parameter.Results：舒尼替尼的药代动力学曲线是最好的一个一级吸收的一室模型描述。ABCG 2 421 C>A基因型被确定为预测口服清除率（CL/F）的显著协变量。通过纳入体重和/或性别，未观察到模型拟合的显著改善。在第2天和第8天之间观察到群体CL/F估计值的系统性差异，定量为随时间推移降低约30%。该差异在模型中被描述为CL/F的协变量。IOV作为随机效应参数显著改善了模型fit.Conclusions：该分析提供了以ABCG 2 421 C>A基因型作为CL/F预测协变量的舒尼替尼群体PK模型。这也表明需要考虑IOV和CL/F随时间的变化，以更准确地预测舒尼替尼的PK。这些发现将用于优化舒尼替尼的药物治疗。
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.