Gill Dipender, Georgakis Marios K, Walker Venexia M, Schmidt A Floriaan, Gkatzionis Apostolos, Freitag Daniel F, Finan Chris, Hingorani Aroon D, Howson Joanna M M, Burgess Stephen, Swerdlow Daniel I, Davey Smith George, Holmes Michael V, Dichgans Martin, Scott Robert A, Zheng Jie, Psaty Bruce M, Davies Neil M
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK.
Wellcome Open Res. 2021 Feb 10;6:16. doi: 10.12688/wellcomeopenres.16544.2. eCollection 2021.
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
其靶点有遗传学证据支持疗效和安全性的药物在临床开发后更有可能获批。在本文中,我们概述了编码药物靶点的基因中的自然序列变异如何用于孟德尔随机化分析,以深入了解基于机制的疗效和不良反应。汇总水平的遗传关联数据的大型数据库越来越多,可用于识别和验证作为药物靶点扰动替代指标的变异。与所有实证研究一样,孟德尔随机化有其局限性,包括遗传混杂、对终身效应的考量以及与不同组织和人群中的异质性相关的问题。当合理应用时,孟德尔随机化提供了一个有用的实证框架,用于利用人群水平的数据提高药物开发流程的成功率。