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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.

SUBMITTER: Feng Z

PROVIDER: S-EPMC7665328 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Feng Zhiwei Z Chen Maozi M Xue Ying Y Liang Tianjian T Chen Hui H Zhou Yuehan Y Nolin Thomas D TD Smith Randall B RB Xie Xiang-Qun XQ

Briefings in bioinformatics 20210301 2

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 ...[more]

PMID: 33078827

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Project description:BackgroundRepositioning of existing drugs has been suggested as a fast track for developing new anti-malarial agents. The compound libraries of GlaxoSmithKline (GSK), Pfizer and AstraZeneca (AZ) comprising drugs that have undergone clinical studies in other therapeutic areas, but not achieved approval, and a set of US Food and Drug Administration (FDA)-approved drugs and other bio-actives were tested against Plasmodium falciparum blood stages.MethodsMolecules were tested initially against erythrocytic co-cultures of P. falciparum to measure proliferation inhibition using one of the following methods: SYBR®I dye DNA staining assay (3D7, K1 or NF54 strains); [(3)H] hypoxanthine radioisotope incorporation assay (3D7 and 3D7A strain); or 4',6-diamidino-2-phenylindole (DAPI) DNA imaging assay (3D7 and Dd2 strains). After review of the available clinical pharmacokinetic and safety data, selected compounds with low ?M activity and a suitable clinical profile were tested in vivo either in a Plasmodium berghei four-day test or in the P. falciparum Pf3D7(0087/N9) huSCID 'humanized' mouse model.ResultsOf the compounds included in the GSK and Pfizer sets, 3.8% (9/238) had relevant in vitro anti-malarial activity while 6/100 compounds from the AZ candidate drug library were active. In comparison, around 0.6% (24/3,800) of the FDA-approved drugs and other bio-actives were active. After evaluation of available clinical data, four investigational drugs, active in vitro were tested in the P. falciparum humanized mouse model: UK-112,214 (PAF-H1 inhibitor), CEP-701 (protein kinase inhibitor), CEP-1347 (protein kinase inhibitor), and PSC-833 (p-glycoprotein inhibitor). Only UK-112,214 showed significant efficacy against P. falciparum in vivo, although at high doses (ED90 131.3 mg/kg [95% CI 112.3, 156.7]), and parasitaemia was still present 96 hours after treatment commencement. Of the six actives from the AZ library, two compounds (AZ-1 and AZ-3) were marginally efficacious in vivo in a P. berghei model.ConclusionsRepositioning of existing therapeutics in malaria is an attractive proposal. Compounds active in vitro at ?M concentrations were identified. However, therapeutic concentrations may not be effectively achieved in mice or humans because of poor bio-availability and/or safety concerns. Stringent safety requirements for anti-malarial drugs, given their widespread use in children, make this a challenging area in which to reposition therapy.

Dataset Information

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

Publications

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

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