Researchers found that people who switched from an SSRI to another type of antidepressant had similar genetic patterns to those who didn’t get better in clinical trials. This finding could help us develop more personalised ways of prescribing antidepressants in the future.
Antidepressants don’t work for everyone, but identifying who they don’t work for can be tricky. In clinical trials, researchers usually study the changes in depression symptoms to see if the antidepressant worked. However, these studies often use a small sample of people, making it hard to study the role of genetics in treatment response.
To overcome this, we looked at over 22,000 medical records of people with depression. Instead of focusing on symptoms and how these change, we focused on whether people switched from the first-line antidepressant prescription (often an SSRI*) to another drug. Switching medications often happens when the first one doesn’t work well, so it can be a useful sign of poor response.
We found that people who switched from SSRIs had similar genetic patterns to those who didn’t get better in clinical trials of antidepressants. However, this genetic pattern was different from those that estimate your risk of developing depression or having a family history of it. This suggests that the genetics of not responding to treatment may be different from the genetics of having depression in the first place.
Our approach shows that it’s possible to identify likely non-responders to antidepressants in ‘Big Data’ (prescription records). This could help future research move us towards personalised treatment for depression (e.g., a genetic test which can predict whether you will respond to an antidepressant).
Link to paper: Lo et al (2025) Antidepressant Switching as a Proxy Phenotype for Drug Nonresponse: Investigating Clinical, Demographic, and Genetic Characteristics. https://www.sciencedirect.com/science/article/pii/S2667174325000564
* SSRI stands for serotonin-reuptake inhibitor, a type of antidepressant.
Elise Darragh worked with Chris Lo and Iona Beange on this lay summary as part of her placement for the MSc in Science Communication and Public Engagement, University of Edinburgh
