Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis
Rheumatoid 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 flu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-04-01
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| Series: | Autoimmunity |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/08916934.2022.2027922 |
| _version_ | 1797684585370222592 |
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| author | Ulku Unal Betul Comertpay Talip Yasir Demirtas Esra Gov |
| author_facet | Ulku Unal Betul Comertpay Talip Yasir Demirtas Esra Gov |
| author_sort | Ulku Unal |
| collection | DOAJ |
| description | Rheumatoid 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. |
| first_indexed | 2024-03-12T00:31:53Z |
| format | Article |
| id | doaj.art-161fbfde5f97436db6ab79e231a0bf49 |
| institution | Directory Open Access Journal |
| issn | 0891-6934 1607-842X |
| language | English |
| last_indexed | 2024-03-12T00:31:53Z |
| publishDate | 2022-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Autoimmunity |
| spelling | doaj.art-161fbfde5f97436db6ab79e231a0bf492023-09-15T10:12:24ZengTaylor & Francis GroupAutoimmunity0891-69341607-842X2022-04-0155314715610.1080/08916934.2022.20279222027922Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysisUlku Unal0Betul Comertpay1Talip Yasir Demirtas2Esra Gov3Department of Bioengineering, Adana Alparslan Türkeş Science and Technology UniversityDepartment of Bioengineering, Adana Alparslan Türkeş Science and Technology UniversityDepartment of Bioengineering, Adana Alparslan Türkeş Science and Technology UniversityDepartment of Bioengineering, Adana Alparslan Türkeş Science and Technology UniversityRheumatoid 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.http://dx.doi.org/10.1080/08916934.2022.2027922autoimmunerheumatoid arthritisdrug repurposingtext miningbioinformatics |
| spellingShingle | Ulku Unal Betul Comertpay Talip Yasir Demirtas Esra Gov Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis Autoimmunity autoimmune rheumatoid arthritis drug repurposing text mining bioinformatics |
| title | Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis |
| title_full | Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis |
| title_fullStr | Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis |
| title_full_unstemmed | Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis |
| title_short | Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis |
| title_sort | drug repurposing for rheumatoid arthritis identification of new drug candidates via bioinformatics and text mining analysis |
| topic | autoimmune rheumatoid arthritis drug repurposing text mining bioinformatics |
| url | http://dx.doi.org/10.1080/08916934.2022.2027922 |
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