Constructing a prediction interval for time to reach a threshold concentration based on a population pharmacokinetic analysis: an application to basiliximab in renal transplantation.

F Mentré, J Kovarik, C Gerbeau
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引用次数: 21

Abstract

Basiliximab is an immunosuppressant chimeric monoclonal antibody directed to the human interleukin-2 receptor alpha-chain used for prevention of acute rejection episodes in organ transplantation. The minimally effective serum concentration necessary to saturate receptor epitopes in kidney transplant patients is 0.2 microgram/ml. To guide dose selection for Phase 3 efficacy trials, a population pharmacostatistical model was fitted to intensively sampled Phase 2 pharmacokinetic data. This served as a basis from which to examine candidate dose regimens with respect to the duration over which receptor-saturating concentrations would be achieved posttransplant. Three prediction methods were assessed: one based on simulations, and two others based on first-order approximation using either inverse regression or inversion of confidence intervals. An 80% prediction interval was generated by each method to evaluate its predictive performance against prospectively collected Phase 3 data in 39 renal transplant patients who received two injections of 20 mg basiliximab, one prior to surgery and one on Day 4 posttransplant. All methods provided correct prediction of the duration of receptor-saturating concentration. As anticipated, the best performance was obtained from the simulation method which predicted 30 values in the 80% prediction interval, 19.7-52.7 days. The actually observed 80% interval from the Phase 3 data was 23.7-58.3 days.

基于群体药代动力学分析构建达到阈值浓度时间的预测区间:巴昔昔单抗在肾移植中的应用。
Basiliximab是一种针对人白细胞介素-2受体α链的免疫抑制剂嵌合单克隆抗体,用于预防器官移植中的急性排斥反应。使肾移植患者受体表位饱和所需的最低有效血清浓度为0.2微克/毫升。为了指导三期疗效试验的剂量选择,将一个群体药物统计模型拟合到密集采样的二期药代动力学数据中。这是研究移植后受体饱和浓度持续时间的候选剂量方案的基础。评估了三种预测方法:一种基于模拟,另两种基于一阶近似,使用逆回归或反转置信区间。每种方法产生80%的预测区间,以评估其对39例肾移植患者前瞻性收集的3期数据的预测性能,这些患者接受两次注射20mg basiliximab,一次在手术前,一次在移植后第4天。所有方法均能准确预测受体饱和浓度持续时间。正如预期的那样,模拟方法在80%的预测区间(19.7-52.7天)内预测了30个值,获得了最好的效果。从3期数据中实际观察到的80%的间隔为23.7-58.3天。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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