An In-Silico Multi-Targeted Approach in Search of Potential Drug Candidate(s) Against SARS-CoV-2 Lung Infection



The multitargeted computational approach for the design of drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lung infection from herbal sources may lead to compound/s that is/are safe (derived from natural sources), effective (act on predefined targets) and broad spectrum (active in both, adult and juvenile population).



The present work aims at developing a specific and effective treatment for a lung infection in both the adult and juvenile population, caused due to SARS-CoV-2 through a computational approach.



A systematic virtual screening of 27 phytoconstituents from 11 Indian herbs with antiviral, anti-inflammatory, and immunomodulatory activity was performed. After applying the Lipinski rule of five, 19 compounds that fitted well were subjected to molecular docking studies using Molegro virtual docker 6.0 with two targets viz. SARS-CoV-2 main protease (Mpro) (PDB ID 6LU7) and ACE receptor (PDB ID 6M0J). The best-docked complexes were used to develop a merged feature pharmacophore using Ligandscout software, to know the structural requirements to develop multitarget inhibitor(s) of SARS-CoV-2. Drug likeliness and ADMET studies were also performed.



The results revealed that Syringin, a glycoside from Tinospora cordifolia, has a good binding affinity towards both targets as compared to Remdesivir. Furthermore, drug likeliness and ADMET studies established its better bioavailability and low toxicity.



The pharmacophores developed from protein-ligand complexes provided an important understanding to design multitarget inhibitor(s) of SARS-CoV-2 to treat COVID-19 lung infection in both the adult and juvenile populations. Syringin may be subjected to further wet-lab studies to establish the results obtained through In-silico studies.
