Abstract

Background: COVID-19, first reported in China, from the new strain of severe acute respiratory syndrome coronaviruses (SARS-CoV-2), poses a great threat to the world by claiming uncountable lives. SARS-CoV-2 is a highly infectious virus that has been spreading rapidly throughout the world. In the absence of any specific medicine to cure COVID-19, there is an urgent need to develop novel therapeutics, including drug repositioning along with diagnostics and vaccines to combat the COVID-19. Many antivirals, antimalarials, antiparasitic, antibacterials, immunosuppressive anti-inflammatory, and immunoregulatory agents are being clinically investigated for the treatment of COVID-19.

Objectives: The earlier developed one parameter regression model correlating the dock scores with in vitro anti-SARS-CoV-2 main protease activity well predicted the six drugs viz remdesivir, chloroquine, favipiravir, ribavirin, penciclovir, and nitazoxanide as potential anti-COVID agents. To further validate our earlier model, the biological activity of nine more recently published SARS-CoV-2 main protease inhibitors has been predicted using our previously reported model.

Methods: In the present study, this regression model has been used to screen the existing antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics utilizing dock score analyses to explore the potential including mechanism of action of these compounds in combating SARS-CoV-2 main protease.

Results: The high correlation (R=0.91) explaining 82.3% variance between the experimental versus predicted activities for the nine compounds is observed. It proves the robustness of our developed model. Therefore, this robust model has been further improved, taking a total number of 15 compounds to formulate another model with an R-value of 0.887 and the explained variance of 78.6%. These models have been used for high throughput screening (HTS) of the 21 diverse compounds belonging to antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics as potential repurpose agents to combat SARS-CoV-2 main protease. The models screened that the drugs bedaquiline and lefamulin have higher binding affinities (dock scores of -8.989 and -9.153 Kcal/mol respectively) than the reference compound {N}-[2-(5-fluoranyl-1~{H}-indol-3-yl)ethyl]ethanamide (dock score of -7.998 Kcal/Mol), as well as higher predicted activities with pEC50 of 0.783 and 0.937 μM and the 0.611 and 0.724 μM respectively. The clinically used repurposed drugs dexamethasone and cefixime have been predicted with pEC50 values of -0.463 and -0.622 μM and -0.311 and -0.428 μM respectively for optimal inhibition. The drugs such as doxycycline, cefpodoxime, ciprofloxacin, sparfloxacin, moxifloxacin, and TBAJ-876 showed moderate binding affinity corresponding to the moderate predicted activity (-1.540 to -1.109 μM).

Conclusion: In the present study, validation of our previously developed dock score-based one parametric regression model has been carried out by predicting 9 more SARS-CoV-2 main protease inhibitors. Another model has been formulated to explore the model's robustness. These models have been taken as a barometer for the screening of more potent compounds. The HTS revealed that the drugs such as bedaquiline and lefamulin are highly predicted active compounds, whereas dexamethasone and cefixime have optimal inhibition towards SARS-CoV-2 main protease. The drugs such as doxycycline, cefpodoxime, ciprofloxacin, sparfloxacin, moxifloxacin, and TBAJ-876 have moderately active compounds towards the target inhibition.

Keywords: COVID-19, SARS-CoV-2 main protease, drug repurposing, molecular docking, high throughput screening (HTS), structure-based drug design.

Graphical Abstract