Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis

dc.authoridDemirtas, Talip Yasir/0000-0003-0122-2767
dc.authoridUNAL, ULKU/0000-0001-8063-5599
dc.authoridGOV, ESRA/0000-0002-5256-4778
dc.contributor.authorUnal, Ulku
dc.contributor.authorComertpay, Betul
dc.contributor.authorDemirtas, Talip Yasir
dc.contributor.authorGov, Esra
dc.date.accessioned2025-01-06T17:36:30Z
dc.date.available2025-01-06T17:36:30Z
dc.date.issued2022
dc.description.abstractRheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.
dc.description.sponsorshipAdana Alparslan Turkes Science and Technology University Scientific Research Projects Committee (BAPKO) [19103010]
dc.description.sponsorshipSupport by Adana Alparslan Turkes Science and Technology University Scientific Research Projects Committee (BAPKO) in the context of the project 19103010.
dc.identifier.doi10.1080/08916934.2022.2027922
dc.identifier.endpage156
dc.identifier.issn0891-6934
dc.identifier.issn1607-842X
dc.identifier.issue3
dc.identifier.pmid35048767
dc.identifier.scopus2-s2.0-85123314873
dc.identifier.scopusqualityQ2
dc.identifier.startpage147
dc.identifier.urihttps://doi.org/10.1080/08916934.2022.2027922
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1907
dc.identifier.volume55
dc.identifier.wosWOS:000744715600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofAutoimmunity
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectAutoimmune
dc.subjectrheumatoid arthritis
dc.subjectdrug repurposing
dc.subjecttext mining
dc.subjectbioinformatics
dc.titleDrug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis
dc.typeArticle

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