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

Background and ObjectiveThis study aimed to identify the active ingredients, key targets, and signaling pathways of Sanliangsan Lipid-Lowering (SLL) in the treatment of hyperlipidemia (HLP) using network pharmacology, and to further clarify the material basis and mechanism responsible for its lipid-lowering efficacy. MethodsAll active ingredients of SLL were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), followed by the identification of target proteins corresponding to each active ingredient. Target genes associated with these target proteins were obtained from the Uniprot database, with duplicate genes excluded to generate the predicted targets of the active ingredients.Cytoscape was utilized to construct the "Herb-Active Ingredient-Predicted Target" network for SLL. Targets related to hyperlipidemia (HLP) were retrieved from the GeneCards, OMIM, and DrugBank databases using "hyperlipidemia" as the keyword. The intersection of the predicted targets of SLL active ingredients and HLP-related targets was defined as the potential therapeutic targets of SLL for HLP. Utilizing Cytoscape 3.9.1 software, the top 20 targets with the highest Degree values were selected to construct the "Herb-Active Ingredient-Target-Disease" network, (esignated as the "Herb-Active Ingredient-Target Network of SLL against HLP." The STRING database was employed to build a protein-protein interaction (PPI) network of the potential therapeutic targets, which was subsequently analyzed using the Network Analysis, CytoNCA, and CytoHubba plugins in Cytoscape to identify the top 10 key targets. The Metascape platform was used for Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the key targets, and the Bioinformatics online tool was applied to visualize the analytical results. Furthermore, AutoDock Tools 1.5 software was utilized to perform molecular docking between the active ingredients and key targets, thereby validating the results of the network pharmacology analysis. ResultsA total of 115 active ingredients and 255 corresponding predicted targets of SLL were screened from the TCMSP database. Additionally, 2106 HLP-related targets were retrieved from the aforementioned databases, and 140 common targets (potential therapeutic targets) were identified through intersection analysis. From these common targets, the top 20 with the highest Degree values were selected for the construction of the "Active Ingredient-Potential Target-Disease" network. Network analysis revealed that kaempferol, quercetin, and formononetin were the top three active ingredients in terms of content, while tumor necrosis factor (TNF), interleukin-6 (IL6), protein kinase B1 (AKT1), interleukin-1{beta} (IL1B), prostaglandin-endoperoxide synthase 2 (PTGS2), peroxisome proliferator-activated receptor {gamma} (PPARG), caspase 3 (CASP3), and hypoxia-inducible factor 1 (HIF1A) were the targets associated with the largest number of active ingredients. The PPI network consisted of 140 nodes and 3,207 edges. By using the CytoHubba plugin for analysis, six key targets were identified: AKT1, TNF-, IL-1 {beta}, IL6, PPARG and PTGS2. The GO functional enrichment analysis revealed 30 entries, which encompassed 10 biological processes (such as positive regulation of transcription by RNA polymerase II), 10 cellular components (such as extracellular space), and 10 molecular functions (such as transcription coactivator binding). Additionally, 10 KEGG signaling pathways were identified, including Lipid and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and Fluid shear stress and atherosclerosis. The findings indicated that SLL exerts a therapeutic effect on HLP by regulating multiple signaling pathways. Molecular docking results demonstrated that the binding energies of the three core ingredients--kaempferol, quercetin, and formononetin--with all key targets were less than -5.0 kJ/mol, with the lowest binding energy (-10.1 kJ/mol) observed between formononetin and PTGS2, indicating a strong binding affinity between these two molecules. ConclusionThis study identified the potential active ingredients, key targets, and core signaling pathways of SLL in treating HLP, confirming that SLL exerts its lipid-lowering effect through a synergistic "multi-component, multi-target, multi-pathway" mechanism. It provides a theoretical basis for further experimental validation and clinical application of SLL in managing HLP.