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基于 PBPK 模型和虚拟临床研究方法预测 CYP2D6 基因指导下他莫昔芬剂量的结果的应用。

Application of PBPK Modeling and Virtual Clinical Study Approaches to Predict the Outcomes of CYP2D6 Genotype-Guided Dosing of Tamoxifen.

机构信息

DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, Hino, Tokyo, Japan.

Sugiyama Laboratory, RIKEN Innovation Center, RIKEN, Yokohama, Kanagawa, Japan.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2018 Jul;7(7):474-482. doi: 10.1002/psp4.12307. Epub 2018 Jun 19.

DOI:10.1002/psp4.12307
PMID:29920987
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6063740/
Abstract

The Tamoxifen Response by CYP2D6 Genotype-based Treatment-1 (TARGET-1) study (n = 180) was conducted from 2012-2017 in Japan to determine the efficacy of tamoxifen dosing guided by cytochrome P450 2D6 (CYP2D6) genotypes. To predict its outcomes prior to completion, we constructed the comprehensive physiologically based pharmacokinetic (PBPK) models of tamoxifen and its metabolites and performed virtual TARGET-1 studies. Our analyses indicated that the expected probability to achieve the end point (demonstrating the superior efficacy of the escalated tamoxifen dose over the standard dose in patients carrying CYP2D6 variants) was 0.469 on average. As the population size of this virtual clinical study (VCS) increased, the expected probability was substantially increased (0.674 for n = 260). Our analyses also informed that the probability to achieve the end point in the TARGET-1 study was negatively impacted by a large variability in endoxifen levels. Our current efforts demonstrate the promising utility of the PBPK modeling and VCS approaches in prospectively designing effective clinical trials.

摘要

CYP2D6 基因分型指导治疗 1(TARGET-1)研究(n=180)于 2012-2017 年在日本进行,旨在确定他莫昔芬剂量与细胞色素 P450 2D6(CYP2D6)基因型的相关性。为了在完成之前预测其结果,我们构建了他莫昔芬及其代谢物的综合生理基于药代动力学(PBPK)模型,并进行了虚拟 TARGET-1 研究。我们的分析表明,携带 CYP2D6 变异的患者接受递增剂量他莫昔芬的疗效优于标准剂量的预期终点概率(证明其疗效优越)平均为 0.469。随着虚拟临床研究(VCS)人群规模的增加,预期概率显著增加(n=260 时为 0.674)。我们的分析还表明,内消旋他莫昔芬水平的较大变异性对达到终点的概率有负面影响。我们目前的努力证明了 PBPK 建模和 VCS 方法在前瞻性设计有效临床试验方面具有广阔的应用前景。

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