Min Zhu, Zhaoran Wang, Ziming Zhu, Cuifeng Zhang, Fanrong Wu
{"title":"缬沙坦治疗慢性肾衰竭的机制探讨:网络药理学与实验验证","authors":"Min Zhu, Zhaoran Wang, Ziming Zhu, Cuifeng Zhang, Fanrong Wu","doi":"10.1155/2023/4837743","DOIUrl":null,"url":null,"abstract":"Objective. To investigate the targets and mechanisms of valsartan in the treatment of chronic renal failure based on network pharmacology and animal experiment validation. Methods. The objectives of using valsartan were predicted with the PubChem and SwissTargetPrediction databases. Relevant targets of chronic renal failure have been searched in various disease databases, with the common purposes of drugs and diseases extracted. Network analysis was carried out with the STRING database to construct a protein-protein interaction (PPI) network, as Cytoscape 3.9.1 software was used to analyze network topology of the key targets and establish the “valsartan-core target gene” network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on core targets to explore their possible molecular mechanisms. The chronic renal failure mouse model was established by the plat method. Hematoxylin-eosin (H&E) and Masson staining observed morphological changes in renal problems of each group, as levels of serum Cre, BUN, T-SOD, and MDA in each group were detected by kit; real-time PCR was used to detect the relative expression of mRNA of TNF-αIL-1β, IL-6, and IL-10 in renal disease of mice in each group, with WB detect CALM, PKCα, and CaMKIV protein expression levels in renal disease from each group. Results. The network pharmacology approach identified 10 key targets for treatment of chronic renal failure with valsartan, including EGFR, PTGS2, PPARG, and ERBB2. KEGG enrichment analysis predicted that the drug exerted neuroactive ligand-receptor interaction, the calcium signaling pathway, the HIF-1 signaling pathway, the proteoglycans in cancer, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Results from animal experiments were compared to those of the model group, as renal function was significantly improved in the valsartan-dose group. The serum levels of Cre, BUN, and MDA and relative mRNA expression of TNF-α, IL-1β, and IL-6 decreased significantly, while serum T-SOD levels, relative mRNA expression of IL-10, and the protein expression level of CALM, PKCα, and CaMKIV increased significantly ( <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"> <mi>P</mi> </math> < 0.05 and <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\"> <mi>P</mi> </math> < 0.001). Conclusion. Valsartan yields certain renal protection, which may improve chronic renal failure in mice through the calcium signaling pathway.","PeriodicalId":15381,"journal":{"name":"Journal of Clinical Pharmacy and Therapeutics","volume":"34 1","pages":"0"},"PeriodicalIF":2.1000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploration of the Mechanism of Valsartan Treatment in Chronic Renal Failure: Network Pharmacology and Experimental Validation\",\"authors\":\"Min Zhu, Zhaoran Wang, Ziming Zhu, Cuifeng Zhang, Fanrong Wu\",\"doi\":\"10.1155/2023/4837743\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective. To investigate the targets and mechanisms of valsartan in the treatment of chronic renal failure based on network pharmacology and animal experiment validation. Methods. The objectives of using valsartan were predicted with the PubChem and SwissTargetPrediction databases. Relevant targets of chronic renal failure have been searched in various disease databases, with the common purposes of drugs and diseases extracted. Network analysis was carried out with the STRING database to construct a protein-protein interaction (PPI) network, as Cytoscape 3.9.1 software was used to analyze network topology of the key targets and establish the “valsartan-core target gene” network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on core targets to explore their possible molecular mechanisms. The chronic renal failure mouse model was established by the plat method. Hematoxylin-eosin (H&E) and Masson staining observed morphological changes in renal problems of each group, as levels of serum Cre, BUN, T-SOD, and MDA in each group were detected by kit; real-time PCR was used to detect the relative expression of mRNA of TNF-αIL-1β, IL-6, and IL-10 in renal disease of mice in each group, with WB detect CALM, PKCα, and CaMKIV protein expression levels in renal disease from each group. Results. The network pharmacology approach identified 10 key targets for treatment of chronic renal failure with valsartan, including EGFR, PTGS2, PPARG, and ERBB2. KEGG enrichment analysis predicted that the drug exerted neuroactive ligand-receptor interaction, the calcium signaling pathway, the HIF-1 signaling pathway, the proteoglycans in cancer, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Results from animal experiments were compared to those of the model group, as renal function was significantly improved in the valsartan-dose group. The serum levels of Cre, BUN, and MDA and relative mRNA expression of TNF-α, IL-1β, and IL-6 decreased significantly, while serum T-SOD levels, relative mRNA expression of IL-10, and the protein expression level of CALM, PKCα, and CaMKIV increased significantly ( <math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\" id=\\\"M1\\\"> <mi>P</mi> </math> < 0.05 and <math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\" id=\\\"M2\\\"> <mi>P</mi> </math> < 0.001). Conclusion. Valsartan yields certain renal protection, which may improve chronic renal failure in mice through the calcium signaling pathway.\",\"PeriodicalId\":15381,\"journal\":{\"name\":\"Journal of Clinical Pharmacy and Therapeutics\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Pharmacy and Therapeutics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/4837743\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Pharmacy and Therapeutics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/4837743","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Exploration of the Mechanism of Valsartan Treatment in Chronic Renal Failure: Network Pharmacology and Experimental Validation
Objective. To investigate the targets and mechanisms of valsartan in the treatment of chronic renal failure based on network pharmacology and animal experiment validation. Methods. The objectives of using valsartan were predicted with the PubChem and SwissTargetPrediction databases. Relevant targets of chronic renal failure have been searched in various disease databases, with the common purposes of drugs and diseases extracted. Network analysis was carried out with the STRING database to construct a protein-protein interaction (PPI) network, as Cytoscape 3.9.1 software was used to analyze network topology of the key targets and establish the “valsartan-core target gene” network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on core targets to explore their possible molecular mechanisms. The chronic renal failure mouse model was established by the plat method. Hematoxylin-eosin (H&E) and Masson staining observed morphological changes in renal problems of each group, as levels of serum Cre, BUN, T-SOD, and MDA in each group were detected by kit; real-time PCR was used to detect the relative expression of mRNA of TNF-αIL-1β, IL-6, and IL-10 in renal disease of mice in each group, with WB detect CALM, PKCα, and CaMKIV protein expression levels in renal disease from each group. Results. The network pharmacology approach identified 10 key targets for treatment of chronic renal failure with valsartan, including EGFR, PTGS2, PPARG, and ERBB2. KEGG enrichment analysis predicted that the drug exerted neuroactive ligand-receptor interaction, the calcium signaling pathway, the HIF-1 signaling pathway, the proteoglycans in cancer, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Results from animal experiments were compared to those of the model group, as renal function was significantly improved in the valsartan-dose group. The serum levels of Cre, BUN, and MDA and relative mRNA expression of TNF-α, IL-1β, and IL-6 decreased significantly, while serum T-SOD levels, relative mRNA expression of IL-10, and the protein expression level of CALM, PKCα, and CaMKIV increased significantly ( < 0.05 and < 0.001). Conclusion. Valsartan yields certain renal protection, which may improve chronic renal failure in mice through the calcium signaling pathway.
期刊介绍:
The Journal of Clinical Pharmacy and Therapeutics provides a forum for clinicians, pharmacists and pharmacologists to explore and report on issues of common interest. Reports and commentaries on current issues in medical and pharmaceutical practice are encouraged. Papers on evidence-based clinical practice and multidisciplinary collaborative work are particularly welcome. Regular sections in the journal include: editorials, commentaries, reviews (including systematic overviews and meta-analyses), original research and reports, and book reviews. Its scope embraces all aspects of clinical drug development and therapeutics, including:
Rational therapeutics
Evidence-based practice
Safety, cost-effectiveness and clinical efficacy of drugs
Drug interactions
Clinical impact of drug formulations
Pharmacogenetics
Personalised, stratified and translational medicine
Clinical pharmacokinetics.