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Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks
DC Field | Value | Language |
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dc.contributor.author | Nam, Yonghyun | - |
dc.contributor.author | Lucas, Anastasia | - |
dc.contributor.author | Yun, Jae-Seung | - |
dc.contributor.author | Lee, Seung Mi | - |
dc.contributor.author | Park, Ji W. | - |
dc.contributor.author | Chen, Ziqi | - |
dc.contributor.author | Lee, Brian | - |
dc.contributor.author | Ning, Xia | - |
dc.contributor.author | Shen, Li | - |
dc.contributor.author | Verma, Anurag | - |
dc.contributor.author | Kim, Dokyoon | - |
dc.date.accessioned | 2023-09-13T02:33:53Z | - |
dc.date.available | 2023-09-13T11:35:18Z | - |
dc.date.issued | 2023-06-26 | - |
dc.identifier.citation | Journal of Translational Medicine, Vol.21(1):415 | ko_KR |
dc.identifier.issn | 1479-5876 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195534 | - |
dc.description.abstract | Background
Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks. | ko_KR |
dc.description.sponsorship | This work was supported by the National Institutes of Health [R01 AG071470]. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BMC | ko_KR |
dc.subject | Drug repurposing | - |
dc.subject | Network medicine | - |
dc.subject | Graph-based semi-supervised learning | - |
dc.subject | COVID-19 | - |
dc.title | Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks | ko_KR |
dc.type | Article | ko_KR |
dc.identifier.doi | 10.1186/s12967-023-04223-2 | ko_KR |
dc.citation.journaltitle | Journal of Translational Medicine | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2023-07-02T03:11:57Z | - |
dc.citation.volume | 21 | ko_KR |
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