Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; email:
Annu Rev Pharmacol Toxicol. 2023 Jan 20;63:65-76. doi: 10.1146/annurev-pharmtox-051421-111324.
A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.
长期以来,人们认识到人类遗传学研究的信息有可能加速药物发现,这导致了数十年来对如何利用遗传和表型信息进行药物发现的研究。已建立的简单和先进的统计方法,允许通过全基因组和表型组分析、与转录组学中的数量性状基因座数据的共定位分析以及来自不同组织的蛋白质组学数据集进行同时分析,以及孟德尔随机化,是后基因组时代药物开发的重要工具。许多研究表明,基因组数据如何为识别新的药物靶点、药物再利用和药物安全分析提供机会。随着越来越多的生物库能够通过电子健康记录将深入的组学数据与丰富的表型特征存储库联系起来,通过不同的临床研究学科,将继续扩大药物靶点评估和验证的更强大方法。