Objective: The mechanism of action of Sanhua Decoction (SHD) in the treatment of ischemic stroke (IS) was analyzed based on the network pharmacology technology, and the pharmacodynamics and key targets were verified using the rat middle cerebral artery occlusion (MCAO) model.
Methods: The GEO database was used to collect IS-related gene set S D , and DrugBank and TTD databases were used to obtain the therapeutic drug target set S T . IS disease gene set S I was collected from DisGeNET, GeneCards, and OMIM databases. These three different gene sets obtained from various sources were merged, duplicates were removed, and the resulting IS disease gene set S IS was imported into the STRING database to establish the protein-protein interaction (PPI) network. Two methods were used to screen the key targets of IS disease based on the PPI network analysis. The TCMSP database and PubChem were applied to retrieve the main chemical components of SHD, and the ACD/Labs software and the SwissADME online system were utilized for ADMET screening. HitPick, SEA, and SwissTarget Prediction online systems were used to predict the set of potential targets for SHD to treat IS. The predicted set of potential targets and the IS disease gene set were intersected. Subsequently, the set of potential targets for SHD treatment of IS was identified, the target information was confirmed through the UniProt database, and finally, the component-target data set for SHD treatment of IS was obtained. clusterProfiler was used for GO function annotation and KEGG pathway enrichment analysis on the target set of SHD active ingredients. A rat MCAO model was established to evaluate the pharmacodynamics of SHD in the treatment of IS, and Western blot analysis assessed the level of proteins in the related pathways.
Results: This study obtained 1,009 IS disease gene sets. PPI network analysis identified 12 key targets: AGT, SAA1, KNG1, APP, GNB3, C3, CXCR4, CXCL12, CXCL8, CXCL1, F2, and EDN1. Database analyses retrieved 40 active ingredients and 47 target genes in SHD. The network proximity algorithm was used to optimize the six key components in SHD. KEGG enrichment showed that the signaling pathways related to IS were endocrine resistance, estrogen, TNF signal pathway, and AGEs/RAGE. Compound-disease-target regulatory network analysis showed that AKT1, IL-6, TNF-α, TP53, VEGFA, and APP were related to the treatment of IS with SHD. Animal experiments demonstrated that SHD significantly reduces the neurological function of rat defect symptoms (P < 0.05), the area of cerebral avascular necrosis, and neuronal necrosis while increasing the levels of IL-6 and APP proteins (P < 0.05) and reducing the levels of AKT1 and VEGFA proteins (P < 0.05).
Conclusion: The effective components of SHD may regulate multiple signaling pathways through IL-6, APP, AKT1, and VEGFA to reduce brain damage and inflammatory damage and exert a neuroprotective role in the treatment of IS diseases. Thus, this study provides a feasible method to study the pharmacological mechanism of traditional Chinese medicine compound prescriptions and a theoretical basis for the development of SHD into a new drug for IS treatment.
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