Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Total glucosides of peony (TGP) is extracted from Paeonia lactiflora Pallas, which has been approved for rheumatoid arthritis (RA) treatment. There were approximately 15 monoterpene glycosides identified in TGP. Pervious researches focused on the effects of TGP and the major ingredient paeoniflorin (PF), but the functions of other monoterpene glycosides and their interactions were not clear. Network pharmacology has been one of the new strategies for multi-target drug discovery. In this study, we investigate the functions of all components of TGP and their interactions in RA treatment based on network pharmacology methods.
Methods: The components of TGP were searched out the Web of Science, PubMed, China National Knowledge Infrastructure databases; then we identified the potential targets based of chemical similarity in the Similarity Ensemble Approach. The molecular related with RA were obtained from DrugBank, GeneCards, DisGeNET and Online Mendelian Inheritance in Man (OMIM) databases. The components-targets-disease network was constructed and analyzed with Cytoscape software; Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted with R for function analysis. The hub components-targets interactions were validated with Autodock Vina.
Results: Twenty potential targets of TGP were predicted for RA treatment. The major components of TGP, PF and albiflorin (AF) had more predicted targets. Hub targets of TGP were LGALS3/9, VEGFA, FGF1, FGF2, IL-6, IL-2, SELP, PRKCA and ERAP1. These targets ameliorated RA mainly through inhibiting leukocyte recruitment and angiogenesis. Enriched pathways including VEGFR pathway, signaling by interleukins, PI3K-Akt signaling pathway, platelet activation, extracellular matrix organization, and so on. The combination of PF, AF and lactiflorin (LF) with the hub targets was further validated using docking program.
Conclusions: We investigated the comprehensive mechanism of TGP for RA treatment. We analyzed the different targets of the components in TGP and predicted the new effects of TGP on inhibiting leukocyte recruitment and angiogenesis. This study provides a better understanding of TGP on the RA treatment.
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http://dx.doi.org/10.21037/apm-21-2203 | DOI Listing |
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