Elmore Andrew R, Sadik Aws, Paternoster Lavinia, Khandaker Golam M, Gaunt Tom R, Hemani Gibran
NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, United Kingdom.
MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, United Kingdom.
PLoS Genet. 2025 Jul 28;21(7):e1011793. doi: 10.1371/journal.pgen.1011793. eCollection 2025 Jul.
It is often difficult to ascertain whether patient-reported side-effects are caused by a drug, and if so, through which mechanism. Adverse side-effects are the primary cause of antipsychotic drug discontinuation rather than poor efficacy. Using a novel method combining genetic and drug binding affinity data, we investigated evidence of causal mechanisms for 80 reported side-effects of 6 commonly prescribed antipsychotic drugs which together target 68 receptors. We analysed publicly available drug binding affinity data and genetic association data using Mendelian randomization and genetic colocalization to devise a representative 'score' for each combination of drug, side-effect, and receptor. We show that 36 side-effects are likely caused by drug action through 30 receptors, which are mainly attributable to off-target effects (26 off-target receptors underlying 39 side-effects). This method allowed us to distinguish which reported side-effects have evidence of causality. Of individual drugs, clozapine has the largest cumulative side-effect profile (Score = 57.5, SE = 11.2), and the largest number of side-effects (n = 36). We show that two well-known side-effects for clozapine, neutropenia and weight change, are underpinned by the action of GABA and CHRM3 receptors respectively. Our novel genetic approach can map side-effects to drugs and elucidate underlying mechanisms, which could potentially inform clinical practice, drug repurposing, and pharmacological development. Further, this method can be generalized to infer the on-target and off-target effects of drugs at any stage of the drug development pipeline.
通常很难确定患者报告的副作用是否由药物引起，如果是，其作用机制是什么。不良副作用是抗精神病药物停药的主要原因，而非疗效不佳。我们采用一种结合基因和药物结合亲和力数据的新方法，研究了6种常用抗精神病药物80种报告副作用的因果机制证据，这些药物共作用于68种受体。我们使用孟德尔随机化和基因共定位分析公开可用的药物结合亲和力数据和基因关联数据，为每种药物、副作用和受体的组合设计一个代表性的“分数”。我们发现36种副作用可能是由药物通过30种受体起作用引起的，这主要归因于脱靶效应（39种副作用背后有26种脱靶受体）。这种方法使我们能够区分哪些报告的副作用有因果关系的证据。在个别药物中，氯氮平的累积副作用概况最大（分数=57.5，标准误=11.2），副作用数量最多（n=36）。我们发现氯氮平的两种众所周知的副作用，中性粒细胞减少和体重变化，分别由GABA和CHRM3受体的作用所支撑。我们新颖的基因方法可以将副作用与药物对应起来，并阐明潜在机制，这可能为临床实践、药物再利用和药理学发展提供信息。此外，这种方法可以推广到推断药物在研发管道任何阶段的靶向和脱靶效应。
