Adavosertib (AZD1775)在实体瘤患者中的群体药代动力学模型。

Martin Johnson PhD, Daniel Kaschek PhD, Dana Ghiorghiu MD, PhD, Shankar Lanke PhD, Kowser Miah PhD, Henning Schmidt PhD, Ganesh M. Mugundu PhD
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摘要

Adavosertib(AZD1775)是一种强效的Wee1激酶小分子抑制剂。这项分析利用了8项阿达沃舍替I/II期研究的药代动力学数据,描述了阿达沃舍替在实体瘤患者(n = 538)中的群体药代动力学特征,并评估了协变量对暴露的影响。研究采用非线性混合效应建模方法,从临床试验数据中估算群体和个体参数。表观清除率(CL)的时间依赖性模型是逐步建立的,最终模型通过视觉预测检查(VPC)进行了验证。模拟分析采用阿达韦色替布剂量 300 毫克/天,每天一次,5 天/2 天停药的给药计划,在 3 周周期内给药 2 周,评估了协变量对以下稳态暴露指标的影响:21 天周期内的最大浓度、21 天周期内的曲线下面积 (AUC)、治疗周期第二周的 AUC 和治疗周期第 12 天的 AUC。最终模型是一个线性二室模型,用药室有滞后时间,中心室有一阶吸收,CL随时间变化,所有模型参数均有随机效应。VPC 和稳态观察结果证实,最终模型满足了对具有不同协变量特征的随机取样的一期和二期人群进行可靠模拟的所有要求。基于模拟的分析表明,体重、肾功能损害状况和种族是决定药物暴露指标变异性的关键因素。
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Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors

Adavosertib (AZD1775) is a potent small-molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed-effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3-week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state: maximum concentration during a 21-day cycle, area under the curve (AUC) during a 21-day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2-compartment model with lag time into the dosing compartment and first-order absorption into the central compartment, time-varying CL, and random effects on all model parameters. VPCs and steady-state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation-based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug-exposure metrics.

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