Computational insight of dexamethasone against potential targets of SARS-CoV-2

Abstract

The health sector has been on the race to find a potent therapy for coronavirus disease (COVID)-19, a diseases caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2. Repurposed anti-viral drugs have played a huge role in combating the virus, and most recently, dexamethasone (Dex) have shown its therapeutic activity in severe cases of COVID-19 patients. The study sought to provide insights on the anti-COVID-19 mechanism of Dex at both atomic and molecular level against SARS-CoV-2 targets. Computational methods were employed to predict the binding affinity of Dex to SARS-CoV-2 using the Schrodinger suite (v2020-2). The target molecules and ligand (Dex) were retrieved from PDB and PubChem, respectively. The selected targets were SARS-CoV-2 main protease (Mpro), and host secreted molecules glucocorticoid receptor, and Interleukin-6 (IL-6). Critical analyses such as Protein and ligand preparation, molecular docking, molecular dynamic (MD) simulations, and absorption, distribution, metabolism, excretion (ADME), and toxicity analyses were performed using the targets and the ligand as inputs. Dex showed stronger affinity to its theoretical (glucocorticoid) receptor with a superior docking score of −14.7 and a good binding energy value of −147.48 kcal/mol; while short hydrogen bond distances were observed in both Mpro and IL-6 when compared to glucocorticoid receptor. Based on these findings, Dex-target complexes were used to perform MD simulations to analyze Dex stability at 50 ns. This study demonstrates that Dex could bind to both the viral and host receptors as a potential drug candidate for COVID-19. To ascertain the biological fitness of this study, other SARS-CoV-2 targets should be explored. Also, the in vitro studies of dexamethasone against several SARS-CoV-2 targets warrant further investigation.

Communicated by Ramaswamy H. Sarma

Keywords:

• Dexamethasone
• COVID-19
• interleukins
• glucocorticoid
• SARS-CoV-2
• main protease

Acknowledgements

We would like to acknowledge the National Integrated Cyberinfrastructure system, Center for High Performance Computing (CHPC), Department of Science and Technology, Republic of South Africa for the license to the Lengau cluster and other modules under the Schrodinger suit.

Disclosure statement

The authors declare no competing interests.