Major depressive disorder (MDD) is highly prevalent in the general population and is associated with grave consequences, including excessive mortality, disability and secondary morbidity. Antidepressants are the most common treatment for MDD and are among the most prescribed medications. While they have demonstrated effectiveness, on average, 30-40% of patients do not achieve response even following multiple adequate trials over several months. In contrast to drug treatment for other medical disorders, and despite a clear biological basis to MDD, personalized treatment planning is not currently possible due to a lack of objective biological predictors of antidepressant response.

The primary goal of this proposal is to develop a clinically useful biomarker panel to increase the precision of MDD treatment. We will expand on our previous work on identifying biological markers, focusing on micro RNAs, and will combine this information with clinical markers defined by a deep learning-based investigation of clinical trial data. This multidisciplinary approach to investigating both clinical and biological markers will further advance biomarker research in MDD for more personalized approach to treatment.