Computational Assessment of Lactobacillus helveticus and Bifidobacterium longum Metabolites for Perinatal Depression Therapy

Perinatal depression (PND) is a severe mood disorder affecting mothers during pregnancy and postpartum, with implications for both maternal and neonatal health. Emerging evidence suggests that gut microbiota-derived metabolites play a critical role in neuroinflammation and neurotransmission. In this study, we employed an in silico approach to evaluate the pharmacokinetic and therapeutic potential of metabolites produced by Lactobacillus helveticus and Bifidobacterium longum in targeting key proteins implicated in PND, including BDNF, CCL2, TNF, IL17A, IL1B, CXCL8, IL6, IL10. The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of selected microbial metabolites, including acetate, lactate, formate, folic acid, riboflavin, kynurenic acid, γ-aminobutyric acid, and vitamin B12 were assessed using computational tools to predict their bioavailability and safety. Enrichment analysis was performed to identify biological pathways and molecular mechanisms modulated by these metabolites, with a focus on neuroinflammation, stress response, and neurogenesis. Additionally, molecular docking studies were conducted to evaluate the binding affinities of these metabolites toward the selected PND-associated targets, providing insights into their potential as neuroactive agents. Our findings suggest that specific microbial metabolites exhibit favorable ADMET properties and strong binding interactions with key proteins implicated in PND pathophysiology. These results highlight the therapeutic potential of gut microbiota-derived metabolites in modulating neuroinflammatory and neuroendocrine pathways, paving the way for novel microbiome-based interventions for perinatal depression. Further experimental validation is warranted to confirm these computational predictions and explore the clinical relevance of these findings.