Abstract
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.
Highlights
In late 2019 and early 2020 the new coronavirus SARS-CoV-2 spread rapidly from China to the rest of the world, with over 3 million deaths recorded as of April 2021
The Natural Language Processing (NLP) pipeline that produces these relationships allows for the quick inclusion of new data permitting rapid augmentation of novel concepts, such as those pertaining to COVID-19 and SARS-CoV-2
We first augmented the graph with coronavirus related information from recent literature through our NLP pipeline
Summary
In late 2019 and early 2020 the new coronavirus SARS-CoV-2 spread rapidly from China to the rest of the world, with over 3 million deaths recorded as of April 2021. Little was known about the virus SARS-CoV-2 and its resultant disease COVID-19 at the turn of 2020, the experience of the SARS epidemic in 2003 offered some insights as to the characteristics of a suitable treatment Both viruses resulted in protracted respiratory illness over several weeks, with resulting Acute Respiratory Distress Syndrome (ARDS), a major cause of death. Computer-enhanced methods for repurposing approved drugs have been developed (Zhou et al, 2020a; Jarada et al, 2020) These include AI approaches (Nadeau et al, 2020; Zhou et al, 2020b), and unbiased laboratory methods to identify the protein-protein interaction (PPI) networks operating between the virus and the host (Gordon et al, 2020). The antiviral drugs that were available had been developed for the treatment of other viruses (e.g. lopinavir/ ritonavir for HIV and remdesivir for Ebola), so there was a distinct possibility that they would not be effective against SARSCoV-2
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