Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach

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作者
Sinead M. O’Donovan
Ali Imami
Hunter Eby
Nicholas D. Henkel
Justin Fortune Creeden
Sophie Asah
Xiaolu Zhang
Xiaojun Wu
Rawan Alnafisah
R. Travis Taylor
James Reigle
Alexander Thorman
Behrouz Shamsaei
Jarek Meller
Robert E. McCullumsmith
机构
[1] University of Toledo College of Medicine and Life Sciences,Department of Neurosciences
[2] University of Toledo,Department of Medical Microbiology and Immunology
[3] Cincinnati Children’s Hospital Medical Center,Division of Biomedical Informatics
[4] University of Cincinnati College of Medicine,Department of Biomedical Informatics
[5] University of Cincinnati College of Medicine,Department of Cancer Biology
[6] University of Cincinnati College of Medicine,Department of Environmental Health
[7] University of Cincinnati College of Medicine,Department of Electrical Engineering and Computing Systems
[8] Nicolaus Copernicus University,Department of Informatics
[9] Promedica,Neurosciences Institute
来源
Scientific Reports | / 11卷
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摘要
The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.
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