Associated organisationsKing's College London · University of Edinburgh · The University of QueenslandEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£4.7M
PeriodAug 2023 — Jul 2028
In plain English
AI plain-English summary
Antidepressants work for some people but not others, and no one knows exactly why. This project will analyse NHS health records and genetic data from tens of thousands of patients to pinpoint the biological mechanisms that determine who responds to antidepressants and who does not. The core problem is that doctors prescribe antidepressants largely by trial and error. Patients often wait weeks to discover a drug does not work, then try another. The underlying biology of antidepressant action remains poorly understood, and no reliable test exists to predict response. This project fills that gap by linking real-world prescription data with genomic markers—DNA variants, gene activity, protein levels, and metabolites—to identify causal pathways. If successful, the research could lead to blood tests or genetic screens that help doctors match patients to the right antidepressant from the start. That would shorten suffering, reduce side effects, and save the NHS money spent on ineffective prescriptions. The team will also test their genetic findings in lab-grown human cells to confirm the mechanisms. Crucially, people with lived experience of depression will help shape the research questions and ensure the results are useful and trustworthy. All data and methods will be made openly available to accelerate progress across the field.
View original technical description
This proposal will use genomic approaches to advance our mechanistic understanding of antidepressant action and response. We will leverage recent advances in causal inference, electronic health data and genomic datasets to deliver real-world antidepressant exposure and response data at an unprecedented scale to better understand the active ingredients of how antidepressants work and why individuals vary in their response. We will openly share new methods and datasets generated from this project. This proposal will deliver mechanistic insights, paving the way for clinical predictors of antidepressant action, framed by lived experience throughout. Our key goals are to - Develop measures of antidepressant exposure and response from NHS electronic health records, linked to research studies and genomic datasets - Provide large, publicly available genome-wide association study datasets of antidepressant exposure and response - Provide association study datasets of antidepressant exposure and genomic markers (DNA methylation, protein expression, metabolites) - Use these genomic association datasets to improve our mechanistic understanding of antidepressant actions and response. - Test the results from our genetic studies in experimental cell-based in vitro models - Conduct our research in collaboration with individuals with lived experience to increase trust, facilitate research that is relevant and important to patients, and improve dissemination and impact
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