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. 2021 Jun 10;11:12310. doi: 10.1038/s41598-021-91625-1

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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COVID-19 transcriptomics-based bioinformatics approach for drug repositioning. We generated lists of statistically significant differentially expressed genes from the analysis of three published studies of SARS-CoV-2 and COVID-19. The drug repositioning computational pipeline compares the ranked differential expression of the COVID-19 disease signature with that of drug profiles from CMap. A reversal score based on the Kolmogorov–Smirnov statistic is generated for each disease-drug pair. If a drug profile significantly (FDR < 0.05) reverses the disease signature, then the drug could be therapeutic for the disease. Across all datasets, a total of 102 drugs have been identified as potentially therapeutic for COVID-19. Twenty-five drugs were identified in analyses of at least two of the three datasets. We further conducted pathways analyses and targeted analyses on the results, focusing on the 25 shared hits. Finally, we validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays.