An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
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Abstract
The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.
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Results from trials show that ML can predict effective drug candidates against SARS-CoV-2.
“Results from trials show that ML can predict effective drug candidates against SARS-CoV-2.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [125]
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In situ click chemistry generation of cyclooxygenase-2 inhibitors
Bhardwaj, A., Kaur, J., Wuest, M., & Wuest, F. (2017) · Nature Communications · doi:10.1038/s41467-016-0009-6 · Cited by 7,448 · see citing works
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works
Pathogenic T-cells and inflammatory monocytes incite inflammatory storms in severe COVID-19 patients
Zhou, Y., Fu, B., Zheng, X., Wang, D., Zhao, C., Qi, Y., Sun, R., Tian, Z., Xu, X., & Wei, H. (2020) · National Science Review · doi:10.1093/nsr/nwaa041 · Cited by 1,295 · see citing works
Xiong, Y., Liu, Y., Cao, L., Wang, D., Guo, M., Jiang, A., Guo, D., Hu, W., Yang, J., Tang, Z., et al. (2020) · Emerging Microbes & Infections · doi:10.1080/22221751.2020.1747363 · Cited by 1,076 · see citing works
Favipiravir versus Arbidol for COVID-19: A Randomized Clinical Trial
Chen, C., Zhang, Y., Huang, J., Yin, P., Cheng, Z., Wu, J., Chen, S., Zhang, Y., Chen, B., Lu, M., et al. (2020) · doi:10.1101/2020.03.17.20037432 · Cited by 852 · cited 1× · see citing works
“24 In particular, the inhibitory activities against SARS-CoV-2 replication of favipiravir at 30 μ M, CVL218 at 3.5 μ M and a combination of both were measured in Vero E6 cells (Methods).”
BackgroundInfluentialSelf-citationWan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty543 · Cited by 381 · see citing works
Self-citationTherapeutic applications of PARP inhibitors: Anticancer therapy and beyond
Curtin, N. J., & Szabo, C. (2013) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2013.01.006 · Cited by 369 · see citing works
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Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03583-3 · Cited by 884 · see citing works
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Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30192-8 · Cited by 636 · see citing works
Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413 · see citing works
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Drug repositioning: Progress and challenges in drug discovery for various diseases
Hua, Y., Dai, X., Xu, Y., Xing, G., Liu, H., Lu, T., Chen, Y., & Zhang, Y. (2022) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2022.114239 · Cited by 232 · see citing works
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Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works
Lv, H., Shi, L., Berkenpas, J. W., Dao, F.-Y., Zulfiqar, H., Ding, H., Zhang, Y., Yang, L., & Cao, R. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab320 · Cited by 107 · see citing works
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Zheng, M., Schultz, M. B., & Sinclair, D. A. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.02.001 · Cited by 62 · see citing works
- Authors
- Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Wang, X., Yang, H., Hong, L., Wu, N., Yuan, E., Luo, Y., Cheng, L., Hu, C., Lei, Y., Shu, H., Feng, X., Jiang, Z., Wu, Y., Chi, Y., Guo, X., Cui, L., Xiao, L., Li, Z., Yang, C., Miao, Z., Chen, L., Li, H., Zeng, H., Zhao, D., Zhu, F., Shen, X., & Zeng, J.
- Affiliations
- Jiangsu Provincial Center for Disease Control and Prevention (CN)
- Chinese Academy of Sciences (CN)
- Tsinghua University (CN)
- Silexon AI Technology (China) (CN)
- University of Illinois Urbana-Champaign (US)
- Tongji Hospital (CN)
- Convalife (China) (CN)
- Capital Medical University (CN)
- Center for Life Sciences (CN)
- Corresponding author
- Zhao, Dan
- Zhu, Fengcai
- Shen, Xiaokun
- Zeng, Jianyang
- ORCID
- Venue
- Signal Transduction and Targeted Therapy
- Published
- 2021-04-24
- Volume
- 6
- Issue
- 1
- Pages
- 165-165
- ISSN
- 2059-3635 (ISSN-L)
- PMC
- PMC8065335
- OpenAlex
- W3159592735
- Cited by
- 300
- References
- 7
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 22 · 2023: 59 · 2022: 50 · 2021: 67 · 2020: 69
- FWCI
- 26.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Signal Transduction and Targeted Therapy (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- National Natural Science Foundation of China (31725014, 61872216, COVID-19, 81630103, 31900862, 2018ZX09711003)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- drug repurposing, COVID-19 pandemic, SARS-CoV-2, PARP-1 inhibitors, nucleocapsid protein, viral replication inhibition, inflammatory cytokines, lipopolysaccharide-induced inflammation, immunopathology, knowledge graph integration, transcriptomic data, machine learning
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Antiviral Agents, Computer Simulation, Humans, Models, Biological, Drug Repositioning
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