Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation

Skelton, David J.; Alsobhe, Aoesha; Anastasi, Elisa; Atallah, Christian; Bird, Jasmine E.; Brown, Bradley; Didon, Dwayne; Gater, Phoenix; James, Katherine; Lennon, David D.; McLaughlin, James; Moreland, Pollyanna E. J.; Pocock, Matthew; Whitaker, Caroline J.; Wipat, Anil

Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory requirements. In this work we used a combination of network algorithms and human curation to search integrated knowledge graphs, identifying drug repurposing opportunities for COVID-19. We demonstrate the value of this approach, reporting on eight potential repurposing opportunities identified, and discuss how this approach could be incorporated into future studies.