Mechanism of for the treatment of diabetic kidney disease based on network pharmacology.

BACKGROUND

Although has been used for the treatment of diabetic kidney disease (DKD), the relevant mechanisms remain unclear. The purpose of this study was to investigate the potential targets and mechanisms of in treating DKD, utilizing network pharmacology.

METHODS

Active compounds of were obtained from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform database. SwissTargetPrediction was used to obtain the potential targets of active ingredients. DKD-associated targets were gathered from the GeneCards, DisGeNET, and OMIM databases. The STRING database and Cytoscape 3.7.2 were used for investigating core targets and interactions among targets. Gene Ontology and Kyoto Encyclopedia of Gene Genomes enrichment were performed using DAVID database. Molecular docking was performed using AutoDock-1.5.7 based on the crystal structures of the targets as deposited in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank.

RESULTS

The top 10 core targets were identified, namely PPARG, AKT1, EGFR, STAT3, CASP3, PPARA, ICAM1, PTGS2, SRC, and MMP9. Enrichment analysis revealed that the primary pathways involving these targets including prolactin signaling pathway, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, relaxin signaling pathway, VEGF signaling pathway, and FoxO signaling pathway. Molecular docking demonstrated that mandenol exhibited a strong binding affinity toward EGFR domain, and wallichilide displayed pronounced binding affinity toward AKT1, EGFR, STAT3, and PTGS2 domains. Additionally, myricanone and senkyunone also showed strong binding affinity for AKT1, EGFR, CASP3, STAT3, and PTGS2 domains.

CONCLUSIONS

This study revealed the potential multi-component and multi-target mechanisms of in treating DKD through network pharmacology. Supplementary experiments are required to further verify these findings.