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利用大规模的生物库来发现治疗目标

Brian R Ferolito¹, Hesam Dashti², Claudia Giambartolomei³

¹Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, 2 Avenue de Lafayette, Boston, MA 02111, USA.

|December 11, 2025

概括

这项研究协调了大型生物库,以确定药物标发现的因果基因特征关系. 这些发现显著增加了基因成为批准药物标的可能性.

科学领域:

遗传学是一个遗传学.
药理学药理学是指药理学的学科.
生物信息学是一种生物信息学.

背景情况:

大规模的生物银行,如百万退伍军人计划 (MVP),英国生物银行和FinnGen提供超过一百万个人的遗传数据.
了解基因特征关联对于识别新型药物标和阐明现有药物标的机制至关重要.

研究的目的:

协调来自主要生物库的数据,并执行两样本的门德尔随机化 (MR) 来识别因果基因特征关系.
评估已识别的基因特征对作为药物标的潜力,并开发药物开发成功的预测模型.

主要方法:

协调的遗传关联结果来自多个生物库的100多万个体.
使用基因表达 (GTEx,eQTLGen) 和血蛋白水平 (ARIC,Fenland,deCODE) 作为对2,003种表型的目标调制的代理进行了两样本的门德尔随机化 (MR).
开发了一种基于已批准的药物标 (ChEMBL 34) 和生物注释的预测排名模型.

主要成果:

鉴定了69669个基因-特征对,有因果效应的证据 (p ≤1.6 × 10-9),包括6447个基因,至少有一种特征有强有力的证据.
确定的基因特征对与获得批准的药物标和适用性更高的几率显著相关,其中9%的已批准的标被重新发现.
预测模型实现了0.79的接收器操作特征曲线下的精度回忆区域,用于预测药物开发成功和临床指示.

结论:

这项研究提供了基因特征因果关系的全面资源,大大提高了潜在药物标的识别.
这些发现证实了大型生物库和MR在药物发现中的实用性,并提供了一个预测工具,以优先考虑制药开发的目标.
在CIPHER中公开提供的结果促进了进一步的研究,并加速将遗传发现转化为临床应用.

关键词:

门德尔的随机化基因组学就是基因组学.目标识别目标识别

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