Genetic inference of on-target and off-target side-effects of antipsychotic medications
Andrew R. Elmore, Aws Sadik, Lavinia Paternoster, Golam M. Khandaker, Tom R. Gaunt, Gibran Hemani
Abstract
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.
Introduction
Schizophrenia is one of the leading causes of disability worldwide [1], despite its relatively low estimated international prevalence of 0.33% to 0.75% [2,3]. Antipsychotic drugs exhibit considerable variations in their pharmacological and clinical impacts, including efficacy and tolerability [4].
Materials and method
Identifying drug targets and side-effect phenotypes
We focussed on six commonly prescribed antipsychotic drugs, including clozapine, which has the highest overall efficacy [4]: aripiprazole, clozapine, olanzapine, paliperidone, quetiapine, and risperidone. These drugs bind to a total of 68 target receptors based on information from two sources: The Psychoactive Drug Screening Program (PDSP) Ki database [10], and the DrugBank database [22].
Results
Of the 68 receptors that are targeted by the 6 antipsychotic medications, 30 receptors show evidence consistent with a potential causal effect on a reported side-effect, while 38 do not show evidence of a causal effect. Of the 30 receptors, 26 of those were due to off-target effects of the drugs. Three out of the 5 known on-target receptors showed evidence of causing a reported side-effect (DRD2, DRD4, and HTR2A), as well as 1 out of the 3 suspected-target receptors (HRH3).
Discussion
Our novel method allows us to use genetic data to unpick the mechanism of action that a drug has on a specific side-effect, and further allows us to compare the size of those effects between drugs. Our results closely corroborate many known side-effect profiles and explains their occurrence due to specific targets and differential target-binding between drugs.
Acknowledgments
We would like to thank the participants that were involved in the data behind the summary statistics that we used. This includes participants from UK Biobank, MetaBrain, eQTLgen, and FinnGen, among others. Further we would like to thank the participants from whom the drug side-effect information was collected.
Citation: Elmore AR, Sadik A, Paternoster L, Khandaker GM, Gaunt TR, Hemani G (2025) Genetic inference of on-target and off-target side-effects of antipsychotic medications. PLoS Genet 21(7): e1011793. https://doi.org/10.1371/journal.pgen.1011793
Editor: Xiang Zhou, University of Michigan, UNITED STATES OF AMERICA
Received: January 14, 2025; Accepted: July 1, 2025; Published: July 28, 2025
Copyright: © 2025 Elmore et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All the data used for this investigation was already publicly available and can be accessed from the original sources. Drug-receptor interaction data were accessed from the Psychoactive Drug Screening Program (PDSP) Ki database (https://doi.org/10.1177/107385840000600408), and the DrugBank database (https://doi.org/10.1093/nar/gkx1037). Side-effect data were accessed from the Side-effect Resource (SIDER) database (https://doi.org/10.1093/nar/gkv1075). GWAS data were accessed from the OpenGWAS repository (https://doi.org/10.7554/eLife.34408). QTL data were accessed from MetaBrain (https://doi.org/10.1038/s41588-023-01300-6) and eQTLgen (https://doi.org/10.1038/s41588-021-00913-z) eQTL datasets. The results of our analysis can be found in the supplementary tables provided, and a code example of the algorithm to calculate side-effect scores in R can be found at https://github.com/andrew-e/side-effect-score. This manuscript follows the STROBE-MR reporting guideline(doi:10.1001/jama.2021.18236). The results of our analysis and all relevant data are within the paper and its Supporting Information files. A code example of the algorithm to calculate side-effect scores in R can be found at https://github.com/andrew-e/side-effect-score. Data analysis was performed using Linux (v8.9 Green Obsidian), R (v4.3.2 https://www.r-project.org/, coloc (v5.2.3), TwoSampleMR (v0.5.6), and ieugwasr (v0.1.5).
Funding: This work was supported by the National Institute for Health and Care Research (NIHR) Bristol Biomedical Research Centre; grant no: NIHR 203315. The recipients of this award are AE, GMK, LP, TRG, and GH. https://www.nihr.ac.uk/ This work is also supported by grants from the Medical Research Council (MRC) to the MRC Integrative Epidemiology Unit at the University of Bristol (grant nos: MC_UU_00032/01, MC_UU_00032/03, and MC_UU_00032/06). The recipients of this award are AE, GMK, LP, TRG, and GH. https://www.ukri.org/councils/mrc/ The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the Medical Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: T.R.G. and G.H. receive funding from Biogen for research not presented here. T.R.G. receives funding from GSK for research not presented here. L.P. is a part of an Innovative Medicines Initiative-European funded consortia (biomap-imi.eu) with multiple industry partners. The remaining authors declare no competing interest.
