MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Abstract

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Tenofovir, Another Inexpensive, Well-Known and Widely Available Old Drug Repurposed for SARS-COV-2 Infection

“…Notably, in one of these studies [ 81 ] a better binding activity of tenofovir with the P323L mutated RdRp protein was observed in comparison with remdesivir, further suggesting tenofovir use in COVID-19 treatment. Interestingly, these drugs also seem to bind other crucial viral proteins, such as PLpro and Mpro/3CLpro [ 84 , 85 , 86 ], and to interfere in the interaction between the S protein and ACE2 receptor [ 82 , 83 ], suggesting multiple targets in the virus life cycle for these drugs.…”

“…Finally, Feng and colleagues [ 86 ] applied a novel in silico approach considering the energy contributions of each key residue involved in the binding between a target protein and its ligands. The authors determined the residue energy contributions in the binding pocket of SARS-CoV-2 Mpro/3CLpro toward binding the known inhibitor N3, and the total energy contribution of key residues around the ligand was used to construct an energy contribution vector of the protein.…”

Identification of potential antiviral compounds against SARS-CoV-2 structural and non structural protein targets: A pharmacoinformatics study of the CAS COVID-19 dataset

“…Likewise, Natesh et al described culinary compounds with potential to inhibit main protease, spike protein and ACE2 receptors of SARS-CoV-2 [ 25 ]. In a similar framework, Feng et al used pattern recognition computing to identify ten potential anti-viral compounds from the CAS COVID-19 dataset [ 26 ]. Similarly, during the first months of the COVID-19 outbreak, machine learning approaches have been applied to a wide spectrum of areas in an effort to better understand and contain the virus, including genomics, prediction of patient outcomes and infections, assay development and potential drug discovery as inhibitors of COVID-19 [ [27] , [28] , [29] , [30] , [31] , [32] ].…”

Clinical Trial of Efficacy and Toxicity of Disoproxil Tenofovir Fumarate and Emtricitabine for Mild to Moderate SARS-CoV-2 Infections

“…Several reports of preclinical studies of these drugs for SARS-CoV-2 have been found, including several in silico studies, which have shown the ability of TDF to bind to RdRp of SARS-CoV-2 7,[38][39][40][41][42][43][44][45][46] . TDF also binds with other important virus proteins, including the papain-like protease (PLpro) and the main or 3C-like protease (Mpro/3CLpro) [47][48][49] , in addition to interacting with the S protein and the ACE2 receptor 50,51 , suggesting multiple binding and interference targets in the replication cycle of the virus. In vitro studies have shown contradictory results in the efficacy of TDF, but some have shown efficacy in reducing viral load in cell cultures and viral titers in nasopharyngeal samples in the experimental SARS-CoV-2 infection model in ferrets [6][7][8] .…”