Screening of FDA Approved Drugs Against SARS-CoV-2 Main Protease: Coronavirus Disease
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Updated: Jul 31, 2026

A Fluorogenic Peptide Cleavage Assay to Screen for Proteolytic Activity: Applications for coronavirus spike protein activation
Published on: January 9, 2019
Vijayakumar Balakrishnan1, Karthik Lakshminarayanan2
1São Carlos Institute of Physics (IFSC), University of São Paulo, Av. Joao Dagnone, 1100 - Jardim Santa Angelina, São Carlos, 13563-120 Brazil.
Insights
Researchers screened FDA-approved drugs against COVID-19 targets. Sincalide and Pentagastrin showed promise as potential treatments for Coronavirus disease 2019 (COVID-19), offering a faster drug discovery pathway.
Area of Science:
- Virology and Drug Discovery
- Computational Chemistry and Pharmacology
Background:
- The emergence of COVID-19 (Coronavirus disease 2019) in late 2019 led to a global pandemic.
- Rapid identification of effective treatments is crucial due to rapid human-to-human transmission.
- Traditional drug development timelines are lengthy, necessitating accelerated approaches for COVID-19 therapeutics.
Purpose of the Study:
- To accelerate the identification of potential drug candidates for COVID-19 treatment.
- To screen existing FDA-approved drugs for efficacy against the COVID-19 main protease (Mpro).
- To identify novel therapeutic agents through virtual screening and molecular docking.
Main Methods:
- High Throughput Virtual Screening (HTVS) was employed to screen a library of FDA-approved drugs.
- The COVID-19 main protease (COVID-19 Mpro) was selected as the primary drug target.
- Induced Fit Docking (IFD) using Schrodinger's GLIDE was performed on promising candidates for flexible docking analysis.
Main Results:
- Four FDA-approved drugs, including Sincalide, Pentagastrin, Ritonavir, and Phytonadione, were identified through HTVS and IFD.
- Sincalide and Pentagastrin demonstrated particularly favorable docking scores and energy profiles.
- These findings suggest potential for repurposing Sincalide and Pentagastrin as COVID-19 therapeutics.
Conclusions:
- Virtual screening of FDA-approved drugs offers an efficient strategy for identifying COVID-19 treatment candidates.
- Sincalide and Pentagastrin emerge as promising lead compounds for further investigation against COVID-19.
- This study highlights the potential of computational methods in accelerating drug discovery for emerging infectious diseases.
Abstract:
At the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission was human-to-human contact and hence resulted in a rapid surge across the globe where more than 24 million people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy, and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger's GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir, and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.
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