Treatment resistant depression: A multi-scale, systems biology approach.

Abstract

An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. Targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.

DOI: 10.1016/j.neubiorev.2017.08.019

Cite this paper

@article{Akil2017TreatmentRD, title={Treatment resistant depression: A multi-scale, systems biology approach.}, author={Huda Akil and Joshua Gordon and Ren{\'e} Hen and Jonathan A. Javitch and Helen S. Mayberg and Bruce S. McEwen and Michael J. Meaney and Eric J Nestler}, journal={Neuroscience and biobehavioral reviews}, year={2017} }