Identification and Validation of Antidepressant Small Molecules Using Bioinformatics and Mouse Depression Models

Area of Science:

• Computational biology and pharmacology
• Neuroscience and psychiatric drug discovery

Background:

• Depression is a widespread psychiatric disorder with limited treatment options.
• Repurposing existing small molecules using bioinformatics is a promising strategy for developing novel antidepressant therapies.
• Previous in silico studies have identified potential neuropsychiatric drugs, but few combined computational predictions with in vivo validation for depression.

Purpose of the Study:

• To identify potential antidepressant small-molecule compounds through bioinformatics analysis and in vivo experimental validation.
• To explore drug repurposing as an efficient method for discovering new treatments for depression.

Main Methods:

• Utilized data from the Gene Expression Omnibus (GEO) database for bioinformatics analysis.
• Employed the Connectivity Map (CMAP) platform to screen for potential antidepressant small-molecule compounds.
• Validated the antidepressant effects of candidate compounds in a chronic restraint stress (CRS) mouse model.

Main Results:

• Identified 311 differentially expressed genes (DEGs) associated with PI3K-Akt, MAPK, and neurotrophic factor signaling pathways.
• Screened five candidate compounds using CMAP; pyrimethamine, pifithrin-mu, and mibefradil demonstrated significant antidepressant effects in CRS mice.
• These compounds improved depressive behaviors by regulating key protein expression in PI3K-Akt and neurotrophic factor pathways, evidenced by increased movement and decreased immobility in behavioral tests.

Conclusions:

• Pyrimethamine, pifithrin-mu, and mibefradil show potential for alleviating depression by modulating PI3K-Akt and neurotrophic factor signaling.
• Bioinformatics-driven drug repurposing is a more efficient approach for antidepressant discovery compared to de novo drug development.
• This study provides an exploratory demonstration of the efficacy of combining in silico predictions with in vivo validation for identifying novel antidepressant candidates.