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一种用于分析药物组合数据的新型布利斯独立性模型。

A New Bliss Independence Model to Analyze Drug Combination Data.

作者信息

Zhao Wei, Sachsenmeier Kris, Zhang Lanju, Sult Erin, Hollingsworth Robert E, Yang Harry

机构信息

MedImmune LLC, Gaithersburg, MD, USA

MedImmune LLC, Gaithersburg, MD, USA.

出版信息

J Biomol Screen. 2014 Jun;19(5):817-21. doi: 10.1177/1087057114521867. Epub 2014 Feb 3.

Abstract

The Bliss independence model is widely used to analyze drug combination data when screening for candidate drug combinations. The method compares the observed combination response (Y(O)) with the predicted combination response (Y(P)), which was obtained based on the assumption that there is no effect from drug-drug interactions. Typically, the combination effect is declared synergistic if Y(O) is greater than Y(P). However, this method lacks statistical rigor because it does not take into account the variability of the response measures and can frequently cause false-positive claims. In this article, we introduce a two-stage response surface model to describe the drug interaction across all dose combinations tested. This new method enables robust statistical testing for synergism at any dose combination, thus reducing the risk of false positives. The use of the method is illustrated through an application describing statistically significant "synergy regions" for candidate drug combinations targeting epidermal growth factor receptor and the insulin-like growth factor 1 receptor.

摘要

在筛选候选药物组合时, Bliss独立模型被广泛用于分析药物组合数据。该方法将观察到的组合反应(Y(O))与预测的组合反应(Y(P))进行比较,Y(P)是基于药物-药物相互作用无影响的假设获得的。通常,如果Y(O)大于Y(P),则组合效应被判定为协同作用。然而,该方法缺乏统计严谨性,因为它没有考虑反应测量的变异性,并且经常会导致假阳性结果。在本文中,我们引入了一个两阶段反应表面模型来描述所有测试剂量组合的药物相互作用。这种新方法能够对任何剂量组合的协同作用进行稳健的统计检验,从而降低假阳性风险。通过一个应用实例说明了该方法的使用,该实例描述了针对表皮生长因子受体和胰岛素样生长因子1受体的候选药物组合具有统计学意义的“协同区域”。

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