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Abstract
The sudden outbreak of novel corona virus at the end of 2019 has caused a global threat to mankind due to its extreme infection rate and mortality. Despite extensive research, still there is no an approved drug or vaccine to combat SARS-CoV-2 infections. Hence, the study was designed to evaluate some plant-based active compounds for drug candidacy against SARS-CoV-2 by using virtual screening methods and various computational analysis. A total of 27 plant metabolites were screened against SARS-Cov-2 main protease proteins (MPP), Nsp9 RNA binding protein, spike receptor binding domain, spike ecto-domain and HR2 domain using molecular docking approach. Four metabolites i.e. asiatic acid, avicularin, guajaverin and withaferin showed maximum binding affinity with all key proteins in terms of lowest global binding energy. The top candidates were further employed for ADME (absorption, distribution, metabolism, and excretion) analysis to investigate their drug profiles. Results suggest that none of the compounds render any undesirable consequences that could reduce their drug likeness properties. The analysis of toxicity pattern revealed no significant tumorigenic, mutagenic, irritating or reproductive effects by the compounds. However, witheferin was comparatively toxic among the top four candidates with considerable cytotoxicity and immunotoxicity. Most of the target class by top drug candidates belonged to enzyme groups (e.g. oxidoreductases hydrolases, phosphatases). Moreover, results of drug similarity prediction identified two approved structural analogs of Asiatic acid from DrugBank, Hydrocortisone (DB00741) (previously used for SARS-CoV-1 and MERS) and Dinoprost-tromethamine (DB01160). In addition, two other biologically active compounds, Mupirocin (DB00410) and Simvastatin (DB00641) could be an alternative choice to witheferin for the treatment of viral infections. The study may pave the way to develop effective medications and preventive measure against SARS-CoV-2 in the future. However, the results were based solely on computational tools and algorithms. Due to the encouraging results, we highly recommend further in vivo trials for the experimental validation of our findings.
