Exploring the Mechanism of Zhang's Waist Pain Formula in Treating Lumbar Intervertebral Disc Degeneration Based on Network Pharmacology and Molecular Docking
Objective:Based on network pharmacology and molecular docking methods,this study aims to explore the molecular biological mechanism of Zhang's Waist Pain Formula in the treatment of lumbar intervertebral disc degeneration(LIDD).Methods:By searching the database,the active ingredients and targets of Zhang's Waist Pain Formula,as well as the related genes of LIDD,were screened.Software R was used to screen common target genes of Zhang's Waist Pain Formula.A"drug-component-target-disease"network and a protein interaction network with common target genes were constructed,while exploring potential protein functional modules in the network.GO functional enrichment analysis and KEEG pathway enrichment analysis on key target genes were performed to reveal their potential biological functions and pathways.Results:A total of 202 active ingredients and 164 common target genes were screened,including 10 core target genes.The enrichment analysis results indicate that the targets of Zhang's Waist Pain Formula in treating LIDD mainly act on AGE-RAGE signaling pathway,TNF signaling pathway,cell apoptosis,and HIF-1 signaling pathway.These biological processes are mainly involved in the regulation of inflammatory response,oxidative stress,and cell apoptosis.The molecular docking results showed that the main active ingredients of Zhang's Waist Pain Formula for treating LIDD,including divaricatol,quercetin,O-acetylcolumbidin,corynoline,and Isomucronulatol 7-O-glucoside have good binding ability with core targets TNF,AKT1,interleukin-6,TP53,and vascular endothelial growth factor A.Conclusion:This study preliminarily confirms that Zhang's Waist Pain Formula has the characteristics of"multiple components,multiple targets,and multiple pathways"in the treatment of LIDD,mainly playing a therapeutic role in anti-inflammatory,antioxidant stress,inhibition of cell apoptosis,and autophagy.
万方数据
维普
