Comparative Computational Study of SARS-CoV-2 Receptors Antagonists from Already Approved Drugs
2020-04-15T12:25:27Z (GMT) by
According to the World Health Organisation, on April 12, 2020, the number of confirmed cases of COVID-19 has already exceeded an estimate of 1 600 000 and 105 000 deaths worldwide. Given this, the impact of COVID-19 on humanity cannot be overlooked, and basic research are urgently needed. This research aims to find antagonists already approved for another diseases, that may inhibit activity of the main protease (Mpro) of the SARS-CoV-2 virus, as well as modulate the ACE2 receptors, largely found in lung cells and reduce viral replication by inhibiting NSP12 RNA Polymerase. Docking molecular simulations were realized among a total of 28 ligands published in the literature against COVID-19. Docking studies were made with algorithm of AutoDock Vina 1.1.2 software. A structure-based virtual screening was performed with MTiOpenScreen. Subsequently, the physical-chemical and pharmacokinetic parameters were analyzed with SwissADME in order to select only the most promising ones. Finally, simulations of molecular dynamics with elapsed time of 4 nanoseconds (ns) were analysed in order to better understand the action of drugs to the detriment of the limitations of molecular docking. This work has shown that, in comparative terms, simeprevir, paritaprevir and elbasvir are currently among the most theoretical promising drugs in remission of symptoms from the disease. An important structure that has already been reported in preclinical and clinical studies, in which theoretical results also corroborates high modulation in viral receptors is: indinavir. The second group of promising drugs includes remdesivir, baricitinib and azithromycin, which also have clinical tests. Apparently, the repurposing drugs (hydroxy)chloroquine and chloroquine were not showed effective, as monotherapies, against SARS-CoV-2 virus or ACE2 receptors found predominantly in pneumocytes. Meanwhile, it has not been able to reach conclusive results due to the limitations of computational techniques that do not take into account numerous empirical parameters.
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