Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach
Authors: Sinead M O&#x27;Donovan
Xiaolu Zhang
Rawan Alnafisah
Justin Fortune Creeden
Hunter Eby
Xiaojun Wu
Robert E McCullumsmith
Behrouz Shamsaei
James Reigle
Nicholas D Henkel
Alexander Thorman
R Travis Taylor
Jarek Meller
Sophie Asah
Ali Imami
Journal: Scientific Reports (2021)
Discipline: Multidisciplinary
Keywords: COVID-19; SARS-CoV-2; therapeutics; bioinformatics; candidate drugs
Affiliations: Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA., Department of Medical Microbiology and Immunology, University of Toledo, Toledo, OH, USA., Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA., Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Department of Informatics, Nicolaus Copernicus University, Torun, Poland., Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA. ., Neurosciences Institute, Promedica, Toledo, OH, USA. .

Abstract:  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. 

Retracted: No

PubPeer comments:
 Surprisingly, the entire study is based on A549 model which is not at all an appropriate model for SARS-CoV-2 infection or COVID-19. It s not clear why the authors chose this model when the relatively more representative in vitro (e.g., Calu-3) and in vivo models with transcriptomics data are available. In fact, the data set which the authors use - GSE147507 - has several other models (including ferret). , 
 #1 That you for your thoughtful comment. The cell line used is part of the LINCS database, a repository of datasets where different cell lines are treated with thousands of different drugs. As such, we did not  choose this cell cell, rather, it was the most relevant in the database compared to the signatures from COVID-19 treated samples. , 
 #2 So, if I understand correctly, A549 was selected just because it is represented in the LINCS database. That s even more surprising because this doesn t change the fact that A549 is not a representative model. The explanation that A549 was used because that s represented in LINCS also is surprising because when using the signatures from LINCS, it is not limited just to A549 but all cell lines ( To standardize our analysis, we combined gene signature data from 6 different cell lines for each drug.  Table S3). It s just the claim that  our approach has identified candidate repurposable drugs, from theu2009 u200920,000 small molecules in the LINCS repository, that may be utilized to combat COVID-19 is troubling! How reliable or relevant are these predictions given the fact that they are based on a bad model of SARS-CoV-2 infection? ,