Abstract
This study utilised computational analysis to investigate the therapeutic potential of phloroglucinol against rheumatoid arthritis (RA). The Differentially expressed genes were identified from the GEO dataset GSE1919 and RA-associated targets were retrieved from the Comparative Toxicogenomics Database (CTD). By comparing these datasets, we identified 17 overlapping targets using a Venn diagram. Then, the top five genes are identified using Cytoscape and CytoHubba plugin. Gene Ontology and KEGG pathway enrichment analyses revealed involvement of these targets in inflammatory signalling. The ADME analysis through the QikProp module demonstrated favourable pharmacokinetic properties, including a molecular weight of 126.112 Da, QPlogPo/w of -0.020, oral bioavailability of 70.508% and no violation of Lipinski's Rule of Five. The molecular docking analysis indicated moderate binding affinities with key inflammatory proteins such as IL6 (-3.583) IL-10 (-2.735), IL-1β (-3.764), ICAM1 (-2.890), and TNF-α. (-4.568). The phloroglucinol-TNF-α complex was subjected to 500 ns molecular dynamics simulation, which confirmed structural stability as evidenced by RMSD values and preserved secondary structure throughout the simulation. These findings identify phloroglucinol as a promising natural small-molecule with TNF-α as its primary molecular target.
Keywords
Rheumatoid Arthritis, Phloroglucinol, Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, TNF-Α, Anti-Inflammatory,Downloads
References
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