Mechanism of Sanliangsan Lipid-Lowering in treating hyperlipidemia: insights from network pharmacology and molecular docking

Abstract   Background and Objective   This study used network pharmacology to identify active ingredients, key targets, and signaling pathways of Sanliangsan Lipid-Lowering (SLL) for hyperlipidemia (HLP), and clarify its lipid-lowering material basis and mechanism.       Methods   SLL active ingredients were retrieved from TCMSP, with corresponding targets obtained from Uniprot (duplicates excluded). Cytoscape constructed the "Herb-Active Ingredient-Predicted Target" network. HLP-related targets were from GeneCards, OMIM, DrugBank; intersections with SLL targets were potential therapeutic targets. Top 20 targets (by Degree) built the "Herb-Active Ingredient-Target-Disease" network. STRING constructed a PPI network, with top 10 key targets identified via Cytoscape plugins. Metascape performed GO/KEGG analysis (visualized via Bioinformatics tools). AutoDock Tools validated via molecular docking.       Results   115 SLL active ingredients (255 predicted targets) and 2106 HLP targets were screened, with 140 common targets. Top active ingredients: kaempferol, quercetin, formononetin. Key targets: AKT1, TNF-α, IL-1β, IL6, PPARG, PTGS2. GO covered 30 entries (10 biological processes, 10 cellular components, 10 molecular functions). KEGG included Lipid and atherosclerosis. Molecular docking showed core ingredients-key targets binding energies < -5.0 kJ/mol (lowest: formononetin-PTGS2, -10.1 kJ/mol).       Conclusion   SLL treats HLP via "multi-component, multi-target, multi-pathway" synergy, providing a theoretical basis for further validation and clinical use.