Given the scale and rapid spread of the 2019 novel coronavirus (2019-nCoV) acute respiratory disease, there is an immediate need for medicines that can help before a vaccine can be produced. Results of rapid sequencing of 2019-nCoV, coupled with molecular modelling based on the genomes of related virus proteins,1 have suggested a few compounds that are likely to be effective, including the anti-HIV lopinavir plus ritonavir combination.
BenevolentAI’s knowledge graph is a large repository of structured medical information, including numerous connections extracted from scientific literature by machine learning.
Most viruses enter cells through receptor-mediated endocytosis. The receptor that 2019-nCoV uses to infect lung cells might be ACE2, a cellsurface protein on cells in the kidney, blood vessels, heart, and, importantly, lung AT2 alveolar epithelial cells (figure). These AT2 cells are particularly prone to viral infection. One of the known regulators of endocytosis is the AP2-associated protein kinase (AAK1). Disruption of AAK1 might, in turn, interrupt the passage of the virus into cells and also the intracellular assembly of virus particles.
Of 378 AAK1 inhibitors in the knowledge graph, have been approved for medical use and six inhibited AAK1 with high affinity. These included a number of oncology drugs such as sunitinib and erlotinib, both of which have been shown to inhibit viral infection of cells through the inhibition of AAK1.5 However, these compounds bring serious side-effects, and our data infer high doses to inhibit AAK1 effectively. We do not consider these drugs would be a safe therapy for a population of sick and infected people.
By contrast, one of the six highaffinity AAK1-binding drugs was the janus kinase inhibitor baricitinib, which also binds the cyclin G-associated kinase, another regulator of endocytosis. Because the plasma concentration of baricitinib on therapeutic dosing (either as 2 mg or 4 mg once daily) is sufficient to inhibit AAK1, we suggest it could be trialled, using an appropriate patient population with 2019-nCoV acute respiratory disease, to reduce both the viral entry and the inflammation in patients, using endpoints such as the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia.
JS is editor-in-chief of Oncogene. JS has previously sat on a number of scientific advisory boards,
including BenevolentAI, and has consulted with Lansdowne partners, Vitruvian, and Social Impact
Capital; he now sits on the Board of Directors for BB Biotech Healthcare Trust and chairs Xerion
Healthcare. All other authors are employees of BenevolentAI. Events in relation to the 2019-nCoV
outbreak are evolving rapidly, and we make our initial thoughts available in this Correspondence in
good faith and to assist in the global response. Our early investigations and suggestions require
further detailed work and analysis and should not be relied on as constituting any kind of medical or
other advice or recommendation.
Peter Richardson, Ivan Griffin, Catherine Tucker, Dan Smith, Olly Oechsle, Anne Phelan,
*Justin Stebbing
j.stebbing@imperial.ac.uk Benevolent AI, London, UK (PR, IG, CT, DS, OO, AP); and Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK (JS)
1 Gruber CC, Steinkellner G. Wuhan coronavirus 2019-nCoV—what we can find out on a structural bioinformatics level. Jan 23, 2020. https://innophore.com/2019-ncov/ (accessed
Feb 3, 2020).
2 Segler MHS, Preuss M, Waller P. Planning chemical syntheses with deep neural
networks and symbolic AI. Nature 2018;555: 604–10.
3 Zhao Y Zhao Z Wang Y et al. Single-cell RNA expression profiling of ACE2, the putative
receptor of Wuhan 2019-nCov. BioRxiv 2020; published online Jan 26. DOI:https://
doi.19985.
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