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Research on neuroimmune regulatory mechanisms and intervention strategies for chronic insomnia based on network pharmacology.

PMID: 41482795 · DOI: 10.36721/PJPS.2026.39.1.REG.13817.1 · Pakistan journal of pharmaceutical sciences, 2026 · Yun Lu
📄 Abstract

Chronic insomnia impairs health-related quality of life and current pharmacotherapies carry substantial adverse-effect profiles, prompting the search for safer multi-target interventions. Kong Sheng Pillow Zhongdan (KSPZ), a classical herbal formula, is empirically used for sleep disturbance, yet its molecular basis remains unclear. To elucidate the putative mechanisms of KSPZ against chronic insomnia through a network-pharmacology approach and to prioritise targets for experimental validation. Active compounds were retrieved from TCMSP, HIT2.0 and TCMIP and filtered by oral bioavailability ≥30% and blood-brain barrier permeability ≥-0.3. Insomnia-related genes were collected from DisGeNET, GeneCards and OMIM. Overlapping targets defined the "core prescription-insomnia" interactome (126 genes). Protein-protein interaction networks were constructed with STRING and hub nodes identified by CytoHubba. GO, KEGG and Reactome enrichment analyses were performed with clusterProfiler; key ligand-target pairs were evaluated by AutoDock Vina. A drug-ingredient-target-disease network was visualised in Cytoscape. Twenty-eight bioactive compounds (e.g., quercetin, kaempferol, luteolin) were mapped to 126 shared targets enriched in neuro-inflammation (IL-17, TNF, NF-κB), serotonergic and dopaminergic synapses, circadian rhythm and cAMP signalling. Top hub genes included TNF, IL6, AKT1, PTGS2, BDNF and DRD2. Molecular docking showed high affinities (ΔG ≤ -8.5 kcal mol KSPZ exerts multi-level effects on neuro-immune regulation, inflammation and circadian pathways, providing a rational basis for its empirical use in chronic insomnia. In-vivo validation of the predicted neurotransmitter and cytokine targets is warranted to translate these network findings into clinical applications.

Confidence: 0.02 · 1 полей извлечено
Идентификация (6 полей)
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Network pharmacology analysis, molecular docking (AutoDock Vina), protein-protein interaction networks (STRING), hub gene identification (CytoHubba), enrichment analyses (GO, KEGG, Reactome), drug-ingredient-target-disease network visualization (Cytoscape)
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