Aim : This study presents an efficient weighted network centrality measureapproach and its application in network pharmacology for exploring mechanismsof action of the Ruellia prostrata (RP) and Ruellia bignoniiflora (RB) herbalformula for treating rheumatoid arthritis. Method: A tripartite network of RP-RB compositive compound-knowntherapeutic targets-putative target genes was first built. The interconnectivityscore was used to prioritizes network candidate network components based ontheir overall connectivity with shared neighborhood network components. A newinterconnectivity based weighted centrality score calculated from traditionalnetwork centrality measure was used identify and explore relationships of themajor network components. The functionality of our proposed method wasdemonstrated by an application in network pharmacology. Result : Network pharmacology results revealed that out of 196 compounds 22were actively interacting with known rheumatoid arthritis therapeutic targets andputative target genes. A multi-level network simulation identifies 33 majorcandidate targets including 8 compositive compounds, 10 therapeutic targets and15 putative target genes. The gene ontology and KEGG pathway enrichmenttarget validation demonstrated that candidate putative target genes were frequently involved in TNF, CCR5, IL-17 and G-protein coupled receptorssignaling pathways which are critical in the progression of rheumatoid arthritis.The molecular docking simulation and molecular descriptor analysis indicatedthat Glyceryl diacetate-2-Oleate, Hexadecanoic acid, 2,3-bis(acetyloxy)propylester, Glycidyl oleate, and Oleoyl chloride had a high binding affinity on theirmajor targets with their molecular descriptor properties indicating their potentialdrug likeliness for the treatment of rheumatoid arthritis. Conclusion : The findings of this study provides promising results that couldlead to design and discovery alternative drug compounds for management andtreatment of rheumatoid arthritis. Although our approach is purely based on insilico method, clinical experiments are still needed to test and validate thehypotheses of our computational methods.
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