Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Clin Pharmacol Ther. 2019 Dec;106(6):1191-1197. doi: 10.1002/cpt.1626. Epub 2019 Oct 26.
In randomized clinical trials (RCTs), it is assumed that nonspecific effects beyond action of pharmacological agents are roughly equivalent in drug and placebo treatment groups. Hence, since the inception of RCTs, drug efficacy is determined by comparing outcomes in active to those in placebo control arms. However, quantitation of efficacy is based on an unproven assumption, that drug and placebo responses are always additive. Response to treatment in RCTs can be differentially influenced by the perturbing effects of patient expectations, side effects, and pharmacogenomic interactions in both drug and placebo arms. Ability to control for these effects requires understanding of when and where they arise, how to mitigate, analyze, and even leverage their impact. Here, we examine three factors that influence additivity: expectation, side effects, and pharmacogenomics. Furthermore, to provide novel insights into nonadditivity and solutions for managing it, we introduce systems pharmacogenomics, a network approach to integrating and analyzing the effects of the numerous interacting perturbations to which a patient is exposed in RCTs.
在随机临床试验 (RCT) 中,假定药物治疗组和安慰剂治疗组的药物作用之外的非特异性效应大致相当。因此,自从 RCT 诞生以来,药物疗效是通过比较活性药物组和安慰剂对照组的结果来确定的。然而,疗效的定量是基于一个未经证实的假设,即药物和安慰剂的反应总是相加的。RCT 中治疗反应会受到患者期望、副作用和药物及安慰剂臂中药物基因组相互作用的干扰效应的不同影响。控制这些影响的能力需要了解它们何时以及在何处出现,如何减轻、分析甚至利用它们的影响。在这里,我们研究了影响加性的三个因素:期望、副作用和药物基因组学。此外,为了提供对非加性的新见解和管理非加性的解决方案,我们引入了系统药物基因组学,这是一种整合和分析患者在 RCT 中暴露于众多相互作用的干扰的网络方法。