A multi-center study to evaluate genetic and neurophysiological predictors of treatment with esketamine in patients with treatment resistant depression

Treatment resistant depression (TRD) is defined as a depressive condition in which two or more adequate antidepressant trials fail to meet response criteria. Esketamine, the S- enantiomer of racemic ketamine, is an N-methyl-D-aspartate (NMDA) receptor antagonist that, in combination with a SSRI or SNRI, is indicated for adults suffering from TRD. Despite the proven efficacy of esketamine, as demonstrated by different studies, there is a percentage of patients that does not obtain clinical response with esketamine. In the era of precision medicine, it is critical to study reliable and reproducible biomarkers to guide treatment selection and monitor biological response, in order to identify markers that may predict treatment response. In genetics studies, Polygenic Risk Scores (PRS) are used frequently to understand the genetic background of complex traits. A PRS summarizes the estimated effect of multiple Single Nucleotide Polymorphisms that individually have small effects on a certain phenotype, thereby capturing the cumulative risk across the genome. Previous studies found suggestive associations between some PRS for psychopathology and clinical response to esketamine. Magnetoencephalography (MEG) measures magnetic fields created by electrical currents with greater spatial localization than electroencephalogram and greater temporal precision than functional Magnetic Resonance Imaging. The resting-state MEG literature about the response to ketamine treatment is suggestive of enhanced functional connectivity (FC) associated with depression improvement, but conclusive results are not available. The present study aims to analyze clinical and neurophysiological phenotype that can be associated to the response to esketamine nasal spray. At the University Clinics of Bari and Chieti, 104 subjects affected by TRD will be enrolled. Subjects will undergo clinicalevaluations, peripheral venous blood sampling and MEG before starting esketamine treatment (T0). Subsequently they will repeat clinical evaluations and MEG after 4 weeks (T1) and finally after 24 weeks of treatment (T2). Regarding data analysis, first we will evaluate functional connectivity patterns at resting state associated with response status and remission status at 4-weeks. Statistical analysis will be performed by dividing the TRD group into responders and non-responders at T1. Two sample T-tests will be used to compare differences in FC in each band (full band (4–48 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–48 Hz)) between responders and non-responders. Then, using a regression model, we will evaluate the association between response status and remission status at T1 with PRS of three different phenotypes: depression, depressive symptoms and schizophrenia. Finally, we will combine genetic, neurophysiological and clinical variables to optimize prediction of antidepressant treatment outcome using machine learning models.