Antiviral activity of traditional medicinal plants from Ayurveda against SARS-CoV-2 infection

Abstract

SARS-CoV-2 is the etiological agent of COVID-19 and responsible for more than 6 million cases globally, for which no vaccine or antiviral is available. Therefore, this study was planned to investigate the antiviral role of the active constituents against spike glycoprotein of SARS-CoV-2 as well as its host ACE2 receptor. Structure-based drug design approach has been used to elucidate the antiviral activity of active constituents present in traditional medicinal plants from Ayurveda. Further, parameters like drug-likeness, pharmacokinetics, and toxicity were determined to ensure the safety and efficacy of active constituents. Gene network analysis was performed to investigate the pathways altered during COVID-19. The prediction of drug–target interactions was performed to discover novel targets for active constituents. The results suggested that amarogentin, eufoliatorin, α-amyrin, caesalpinins, kutkin, β-sitosterol, and belladonnine are the top-ranked molecules have the highest affinity towards both the spike glycoprotein and ACE2. Most active constituents have passed the criteria of drug-likeness and demonstrated good pharmacokinetic profile with minimum predicted toxicity level. Gene network analysis confirmed that G-protein coupled receptor, protein kinase B signaling, protein secretion, peptidyl-serine phosphorylation, nuclear transport, apoptotic pathway, tumor necrosis factor, regulation of angiotensin level, positive regulation of ion transport, and membrane protein proteolysis were altered during COVID-19. The target prediction analysis revealed that most active constituents target the same pathways which are found to be altered during COVID-19. Collectively, our data encourages the use of active constituents as a potential therapy for COVID-19. However, further studies are ongoing to confirm its efficacy against disease.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• COVID-19
• structure-based drug design
• attachment inhibitor
• gene network analysis
• target prediction
• pharmacokinetics
• Ayurveda

Acknowledgements

The authors are grateful to the Vice Chancellor, King George’s Medical University (KGMU), Lucknow, India for the encouragement for this work. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Disclosure statement

The authors declare no conflict of interest.