Yan Yuhe, Shi Honghong, Li Yue, Wan Xing, Li Jinxin, Wang Lihua
Division of Nephrology, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Shanxi Kidney Disease Institute, Taiyuan, China.
Ren Fail. 2025 Dec;47(1):2524528. doi: 10.1080/0886022X.2025.2524528. Epub 2025 Aug 21.
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.
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.
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.
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.
尽管[具体药物名称未给出]已被用于治疗糖尿病肾病（DKD），但其相关机制仍不清楚。本研究的目的是利用网络药理学研究[具体药物名称未给出]治疗DKD的潜在靶点和机制。
从中药系统药理学数据库和分析平台数据库中获取[具体药物名称未给出]的活性成分。使用SwissTargetPrediction获取活性成分的潜在靶点。从GeneCards、DisGeNET和OMIM数据库中收集DKD相关靶点。利用STRING数据库和Cytoscape 3.7.2研究核心靶点及靶点间的相互作用。使用DAVID数据库进行基因本体论和京都基因与基因组百科全书富集分析。基于结构生物信息学研究合作实验室（RCSB）蛋白质数据库中所存靶点的晶体结构，使用AutoDock-1.5.7进行分子对接。
确定了前10个核心靶点，即PPARG、AKT1、EGFR、STAT3、CASP3、PPARA、ICAM1、PTGS2、SRC和MMP9。富集分析表明，涉及这些靶点的主要途径包括催乳素信号通路、糖尿病并发症中的AGE-RAGE信号通路、TNF信号通路、松弛素信号通路、VEGF信号通路和FoxO信号通路。分子对接表明，曼德诺对EGFR结构域表现出很强的结合亲和力，沃利奇利德对AKT1、EGFR、STAT3和PTGS2结构域表现出明显的结合亲和力。此外，杨梅酮和川芎嗪对AKT1、EGFR、CASP3、STAT3和PTGS2结构域也表现出很强的结合亲和力。
本研究通过网络药理学揭示了[具体药物名称未给出]治疗DKD的潜在多成分、多靶点机制。需要补充实验进一步验证这些发现。
