Maastricht University Medical Centre, Maastricht, The Netherlands.
Ther Drug Monit. 2011 Apr;33(2):133-46. doi: 10.1097/FTD.0b013e31820f40f8.
Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.
最大后验贝叶斯（MAPB）药代动力学参数估计是一种使用个体血药浓度和先验信息准确且灵活地估计个体药代动力学参数的方法。在过去的十年中，许多研究已经开发了最佳采样策略，以使用多回归分析或 MAPB 估计尽可能准确地估计药代动力学参数。这已经针对许多药物进行了研究，特别是免疫抑制剂和抗癌药物。开发最佳采样策略（OSS）的方法多种多样，各不相同。本综述全面概述了使用 MAPB 药代动力学参数估计的 OSS 开发方法，确定了已发表的 OSS 的可转移性，并比较了 MAPB 估计和多回归分析确定的采样策略。OSS 开发有以下几个组成部分：1）先验分布；2）参考值确定；3）最佳采样时间识别；4）OSS 验证。已发表的 OSS 通常缺乏使 OSS 在临床上可转移的所有必要数据。MAPB 估计在预测性能方面与多回归分析相似，但在灵活性方面更优。
