Identification of COVID-19 Drug Candidates through Computational Drug Repurposing

Henrik Zhang

Abstract


The recent outbreak of the COVID-19 pandemic has spread from China to the rest of the world in part because the virus has a high infection rate. The lack of a vaccine and ineffective antiviral treatments also contribute to the growing number of those who die from COVID-19. Though vaccines are on the verge of being delivered, drug treatments are still much needed because of the challenge of distributing the vaccine and the month long immunity development period where people remain vulnerable makes drug treatments essential. Drug repurposing reuses previously approved drugs to be used for another disease. Case in point are  Remdesiver and Chloroquine, drugs previously used to treat ebola and malaria, which are now being repurposed to treat COVID-19. In this study, we used a computational approach to identify potential drugs to be repurposed in COVID-19 treatment based on patient genomic data and a public gene drug database. We retrieved datasets containing COVID-19 patient RNA-seq and proteomics data and used it to determine differentially expressed genes in each dataset and their interrupted functional pathways. Through the Drug Gene Interaction database, we found the specific drugs that target each differentially expressed gene and used the evidence scores from the database to create our final network of drug gene interactions. Our network contains a total of 22 unique drugs, 10 of which have been tested in a clinical setting, while the remaining drugs need to be validated in clinical studies in the future.



Keywords


Drug repurposing, COVID-19, Network, Gene expression, Proteomics, Computational

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References


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