From mice and men: Predicting anti-depressant response

From mice and men: Predicting anti-depressant response

Last Updated on January 2, 2018 by Joseph Gut – thasso

January 01, 2018 – Antidepressants are not at all a “one-size-fits-all pill”. Predicting treatment response in depression in order to get the “right” treatment, efficacious and adverse effects free at the same time, is still a huge challenge. Inter-individual variability to treatment spanning a spectrum from successfully realised clinical response to disastrous self-destructing behaviour up to completed suicide in some individuals is tremendous. Up to now, finding the most effective antidepressant medication for each individual patient did largely depends on trial and error; still today, it does so. In the age of theragenomic medicine, this underlines the urgent need to establish conceptually novel strategies for the identification of predictive biomarkers firmly associated with a positive response.

New evidence from a study in mice suggests why an antidepressant treatment can alleviate depression in one person but not another. The study, just published in PLOS Biology, reported on the development of a mouse model that allowed the research team to identify blood signatures associated with response to antidepressant treatment and could show the importance of the stress-related glucocorticoid receptor in recovery from depression.
The researchers  utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, the team translated the murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed
Depressed struggling mice with (upper) and without (lover) antidepressant treatment.

prediction of response status with an accuracy of 76% in the patient population. Finally, the researchers showed that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Overall, the findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Moreover, these data also highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species.

Major depression is the leading cause of disability according to the World Health Organization (WHO), affecting an estimated 350 million people worldwide. Only one-third of patients benefit from the first antidepressant prescribed. Although the currently available treatments are “safe”, however with the very broad spectrum of outcomes referred to above. The establishment of the present novel experimental approach in animals focusing on extreme phenotypes in response to antidepressant treatment, simulating the clinical situation by identifying good and poor responders to antidepressant treatment constitutes a huge step forward for the understanding of who could be a responder and who would not. Of course, there will have to be much larger trials in humans to confirm this concept, and also to understand the role genetic variation in the GR-regulated genes in individual patients might play in order to become a good or a bad responder.


Ph.D.; Professor in Pharmacology and Toxicology. Senior expert in theragenomic and personalized medicine and individualized drug safety. Senior expert in pharmaco- and toxicogenetics. Senior expert in human safety of drugs, chemicals, environmental pollutants, and dietary ingredients.