A Connectivity Map-Based Drug Repurposing Study and Integrative Analysis of Transcriptomic Profiling of SARS-CoV-2 Infection

15 June 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Aims: The recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.
Materials and methods: In this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including “the primary human lung epithelial cell line NHBE” and “the transformed lung alveolar cell line A549”. Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentations of each pathway are tested in comparison to what is expected randomly.
Key findings: The results suggest the effectiveness of Saquinavir, lansoprazole, folic acid, ebselen, aminocaproic acid, simvastatin, surfactant stimulant drugs, heat shock protein 90 (HSP90) inhibitors, histone deacetylase (HDAC) inhibitors, metronidazole, inhaled corticosteroids (ICS) and many other clinically approved drugs and investigational compounds as potent drugs against COVID-19 outbreak.
Significance: Making new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous research.


drug repurposing
connectivity map
transcriptomic profiling


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