Expert-Augmented computational drug repurposing identified baricitinib as a treatment for COVID-19
Open accessAbstract
The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.
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Based on an in-house drug discovery knowledge graph, BenevolentAI scientists proposed baricitinib, at the time approved for rheumatoid arthritis, as a drug repurposing candidate for severe COVID-19.
“Based on an in-house drug discovery knowledge graph, BenevolentAI scientists proposed baricitinib, at the time approved for rheumatoid arthritis, as a drug repurposing candidate for severe COVID-19 (52,53).”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [53]
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- Authors
- Smith, D. P., Oechsle, O., Rawling, M. J., Savory, E., Lacoste, A. M. B., & Richardson, P. J.
- Affiliations
- BenevolentAI (United Kingdom) (GB)
- Corresponding author
- Richardson, Peter John
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2021-07-28
- Volume
- 12
- Pages
- 709856-709856
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC8356560
- OpenAlex
- W3185323030
- Cited by
- 62
- References
- 46
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 9 · 2023: 15 · 2022: 9 · 2021: 1
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- baricitinib, COVID-19 pandemic, SARS-CoV-2, drug repurposing, visual analytics, biomedical knowledge graphs, human-computer collaboration, antiviral therapy, anti-inflammatory therapy, rheumatoid arthritis
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