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Network pharmacology of cellular targets in major depressive disorder and differential mechanisms of fluoxetine, ketamine and esketamine
Date
2026-01-01
Abstract
Major depressive disorder (MDD) is a multifactorial mental health condition involving genetic, environmental, and neurobiological factors. Conventional antidepressants such as fluoxetine, a selective serotonin reuptake inhibitor, require weeks to exert therapeutic effects, whereas ketamine and esketamine act rapidly via glutamatergic modulation. These drugs may also converge on the inhibition of glycogen synthase kinase 3 beta (GSK3B) as a key mechanism for their antidepressant effects, increasing neuroplasticity, synaptic transmission, and neuronal survival through upregulation of brain-derived neurotrophic factor (BDNF). Part of the antidepressant effects of ketamine also seems to depend on opioid receptor activation. Despite recent progress, variability in antidepressant response in MDD remains unclear. This work explores, via meta-analysis and network fragility analysis, key molecular mechanisms in MDD, how these drugs exert actions, and highlights potential therapeutic targets for MDD. We performed a network pharmacology approach to unravel the key cellular processes involved in MDD, including altered synaptic plasticity, neurogenesis, apoptosis, and neuroinflammation. Second, we explored the therapeutic role of these treatments on these altered cellular processes. By integrating drug-target data with MDD-associated genes, we identified the opioid receptor mu 1 (OPRM1), epidermal growth factor receptor (EGFR) and GSK3B as key druggable targets. Network analysis further suggested that nuclear factor kappa B (NFKB) may regulate all three, positioning it as a central node linking inflammation, synaptic plasticity, and neuronal metabolism in MDD. We hypothesize that targeted modulation of these genes may optimize the therapeutic efficacy, while NFKB emerges as a promising candidate biomarker for guiding treatment strategies in MDD.
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Description
Publisher
Elsevier
Citation
Computational and Structural Biotechnology Journal 31, pp. 235–249
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Barreto_2026_Network.pdf
Adobe PDF, 7.11 MB
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Funding code
Funding Information
Sustainable Development Goals
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License
Attribution-NonCommercial-ShareAlike 4.0 International