In silico identification of approved drugs for potential prophylactic uses against SARS-CoV-2 infection
1 Department of Pharmaceutical Microbiology and Biotechnology, Faculty of Pharmaceutical Sciences, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria.
2 Department of Pharmaceutical Microbiology and Biotechnology, Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Agulu Campus, Anambra State, Nigeria.
3 Department of Pharmacognosy and Traditional Medicine, Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Agulu Campus, Anambra State, Nigeria.
4 Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria.
5 Department of Applied Microbiology, Faculty of Sciences, Ebonyi State University, Abakaliki, Nigeria.
6 Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Agulu Campus, Anambra State, Nigeria.
Research Article
Magna Scientia Advanced Biology and Pharmacy, 2024, 11(02), 057–071
Article DOI: 10.30574/msabp.2024.11.2.0023
Publication history:
Received on 26 February 2024; revised on 08 April 2024; accepted on 10 April 2024
Abstract:
The outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) responsible for Coronavirus disease 2019 (COVID-19) constitute a major public health threat and economic burden. Despite the multifold research all over the world. COVID-19 has remained without a treatment. Hence, the urgent need to repurpose the FDA-approved drugs through in silico computational technique to identify inhibitors of SARS-CoV-2. Bioinformatics techniques were used to screen 2015 FDA- approved drugs against SARS-CoV-2 proteins and associated human protein, angiotensin-converting enzyme 2 (ACE2) to identify drug for new indication. The ligands were downloaded from Selleckchem drug data bank in SDF format. Open Babel was used to separate the ligands into their compounds and prepared for docking simulation using shell script and saved as PDBQT. Experimental crystal structures of the receptors (ACE2 and Spike proteins) were retrieved from Protein Data Bank (PDB), Autodock tools was used to prepare the proteins and saved as PDBQT files. Docking protocols were validated, molecular docking simulations were performed on a Linux platform using AutoDock Vina in quadruplicate. The binding energies (BEs) kcal/mol were calculated and expressed as mean ± standard deviation. Discovery studio was used for visualization. Density Functional Theory (DFT) was used to determine the predictive maximal inhibitory concentration (pIC50) of best hits. Ninety-one (91) new hits were identified for prophylaxis. Four (4) compounds were subjected to DFT based IC50 calculation with molnupiravir as a reference compound. Significantly, calculated IC50 of molnupiravir in Vero cells and Calu-3 were 0.299 and 0.008µM respectively while the experimental IC50 in Vero cells and Calu-3 as reported in literature were 0.3 and 0.08 µM respectively. The newly identified hits are promising therapeutic agents and there is no significant difference between the experimental IC50 of molnupiravir and the calculated IC50.
Keywords:
SARS-CoV-2; Prophylaxis; In silico; Approved Drugs; DFT; IC50
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