Biomarkers in mood disorders research: developing new and improved therapeutics

Abstract

Background Recently, surrogate neurobiological biomarkers that correlate with target engagement and therapeutic response have been developed and tested in early phase studies of mood disorders. Objective The identification of biomarkers could help develop personalized psychiatric treatments that may impact public health. Methods These biomarkers, which are associated with clinical response post-treatment, can be directly validated using multimodal approaches including genetic tools, proteomics/metabolomics, peripheral measures, neuroimaging, biostatistical predictors, and clinical predictors. Results To date, early phase biomarker studies have sought to identify measures that can serve as “biosignatures”, or biological patterns of clinical response. These studies have also sought to identify clinical predictors and surrogate outcomes associated with pathophysiological domains consistently described in the National Institute of Mental Health’s (NIMH) new Research Domain Criteria (RDoC). Using the N-methyl-D-aspartate (NMDA) antagonist ketamine as an example, we identified changes in several domains (clinical, cognitive, and neurophysiological) that predicted ketamine’s rapid and sustained antidepressant effects in individuals with treatment-resistant major depressive disorder (MDD) or bipolar depression. Discussion These approaches may ultimately provide clues into the neurobiology of psychiatric disorders and may have enormous impact Backon the development of novel therapeutics.

Startle amplitude during unpleasant pictures is greater in veterans with a history of multiple‐suicide attempts and predicts a future suicide attempt

“…Additional work with a larger sample size is needed to examine whether startle during unpleasant‐picture processing is a potential biomarker for predicting future suicidal behavior. There is great discussion of the importance of discovering relevant biomarkers that could facilitate the diagnosis and treatment of psychiatric disorders (e.g., Niciu et al, ), making our preliminary finding that veterans at high risk for suicide who also have elevated startle during unpleasant pictures may be at risk for a future suicide attempt promising for further study.…”

The role of genetic variation of human metabolism for BMI, mental traits and mental disorders

“…The identification of biomarkers [47] may improve diagnostic classification systems of mental disorders and related phenotypes [48] . The current lack of biomarkers represents a key challenge for psychiatric research according to the U.S. National Institute of Mental Health's (NIMH) new Research Domain Criteria (RDoC) [49] .…”

Depressive disorders: Treatment failures and poor prognosis over the last 50 years

“…However, the single most comforting suggestion for psychopharmacology is that the powerful antidepressant effects of these drugs are actually masked by the inadequacy of current clinical trial designs and that the research strategy for the evaluation of novel psychotropic agents, according to Matthews and colleagues over a decade ago, needs significant rethinking and reevaluation . One immediate and possible solution to de‐risking clinical trial design failure would be the availability of robust biomarkers but presently there are no approved biomarkers for MDD (see below) …”

“…And, stating that “a crisis of confidence has followed the failure of this and other programs in neuropsychiatry, with a far reaching and detrimental impact on pharmaceutical research.” However, major advances in imaging technology, functional magnetic resonance (fMRI) have recently reported that patients with depression can be divided into 4 neurophysiological subtypes (“biotypes”) defined by distinct patterns of dysfunctional connectivity in limbic frontostriatal networks. Therefore, clustering (stratifying) patients and enabling the development of diagnostic classifiers (biomarkers) that most likely will target specific patient phenotypes . The validation of these assessments against relevant biomarkers, across large multi‐site studies will add to their cogency.…”