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

Area of Science:

• Neuroscience
• Genetics
• Pharmacology

Background:

• Major depressive disorder (MDD) affects millions, with ~50% of patients inadequately responding to existing treatments.
• Treatment resistance and heterogeneity in MDD necessitate novel therapeutic strategies and patient stratification biomarkers.
• Current treatment selection relies on trial-and-error, lacking objective guidelines for optimal patient-therapist matching.

Purpose of the Study:

• To develop a multi-scale research framework to understand the biological underpinnings of depression.
• To identify specific brain circuits and molecular changes associated with depression subtypes.
• To pave the way for developing targeted, personalized antidepressant medications.

Main Methods:

• Utilizing a multi-scale framework combining preclinical animal models of depression with human genetic and neuroimaging data.
• Investigating dysfunctional brain circuits and associated gene expression changes in validated depression models.
• Correlating findings from preclinical models with human genetic and imaging studies to identify conserved biomarkers.

Main Results:

• Preclinical studies are identifying candidate brain circuits and molecules implicated in depression.
• Evidence suggests these candidate circuits and molecules are altered in both animal models and human patient populations.
• The research is beginning to delineate distinct molecular and circuit-based subtypes of depression.

Conclusions:

• A multi-scale research approach is crucial for dissecting depression's complexity.
• Identifying specific biomarkers can enable the stratification of patients with depression.
• Targeting identified circuits and molecules holds promise for developing next-generation, personalized antidepressants.