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Current Drug Targets

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ISSN (Print): 1389-4501
ISSN (Online): 1873-5592

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Review Article

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Author(s): Zhiwei Yang*, Xinhui Cai, Qiushi Ye, Yizhen Zhao, Xuhua Li, Shengli Zhang and Lei Zhang*

Volume 24, Issue 6, 2023

Published on: 27 March, 2023

Page: [532 - 545] Pages: 14

DOI: 10.2174/1389450124666230306141725

Price: $65

Abstract

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

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[1]
Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. N Engl J Med 2020; 382(13): 1199-207.
[http://dx.doi.org/10.1056/NEJMoa2001316] [PMID: 31995857]
[2]
Chan JFW, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 2020; 395(10223): 514-23.
[http://dx.doi.org/10.1016/S0140-6736(20)30154-9] [PMID: 31986261]
[3]
Zhou P, Yang XL, Wang XG, et al. Addendum: A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020; 588(7836): E6.
[http://dx.doi.org/10.1038/s41586-020-2951-z] [PMID: 33199918]
[4]
Zhu N, Zhang D, Wang W, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med 2020; 382(8): 727-33.
[http://dx.doi.org/10.1056/NEJMoa2001017] [PMID: 31978945]
[5]
Zhang L, Lin D, Sun X, et al. Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science 2020; 368(6489): 409-12.
[http://dx.doi.org/10.1126/science.abb3405] [PMID: 32198291]
[6]
Smith E, Davis-Gardner ME, Garcia-Ordonez RD, et al. High-throughput screening for drugs that inhibit papain-like protease in SARS-CoV-2. SLAS Discov 2020; 25(10): 1152-61.
[http://dx.doi.org/10.1177/2472555220963667] [PMID: 33043784]
[7]
Fu L, Ye F, Feng Y, et al. Both Boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease. Nat Commun 2020; 11(1): 4417.
[http://dx.doi.org/10.1038/s41467-020-18233-x] [PMID: 32887884]
[8]
Brown AS, Ackerley DF, Calcott MJ. High-throughput screening for inhibitors of the SARS-CoV-2 protease using a FRET-biosensor. Molecules 2020; 25(20): 4666.
[http://dx.doi.org/10.3390/molecules25204666] [PMID: 33066278]
[9]
Zhao Y, Du X, Duan Y, et al. High-throughput screening identifies established drugs as SARS-CoV-2 PLpro inhibitors. Protein Cell 2021; 12(11): 877-88.
[http://dx.doi.org/10.1007/s13238-021-00836-9] [PMID: 33864621]
[10]
Lim CT, Tan KW, Wu M, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of Nsp3 papain-like protease. Biochem J 2021; 478(13): 2517-31.
[http://dx.doi.org/10.1042/BCJ20210244] [PMID: 34198325]
[11]
Virdi RS, Bavisotto RV, Hopper NC, et al. Discovery of Drug-Like Ligands for the Mac1 Domain of SARS-CoV-2 Nsp3. SLAS Discov 2020; 25(10): 1162-70.
[http://dx.doi.org/10.1177/2472555220960428] [PMID: 32981460]
[12]
Canal B, Fujisawa R, McClure AW, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp15 endoribonuclease. Biochem J 2021; 478(13): 2465-79.
[http://dx.doi.org/10.1042/BCJ20210199] [PMID: 34198324]
[13]
Zeng J, Weissmann F, Bertolin AP, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp13 helicase. Biochem J 2021; 478(13): 2405-23.
[http://dx.doi.org/10.1042/BCJ20210201] [PMID: 34198322]
[14]
Breidenbach J, Lemke C, Pillaiyar T, et al. Targeting the Main Protease of SARS-CoV-2: From the Establishment of High Throughput Screening to the Design of Tailored Inhibitors. Angew Chem Int Ed 2021; 60(18): 10423-9.
[http://dx.doi.org/10.1002/anie.202016961] [PMID: 33655614]
[15]
Baker JD, Uhrich RL, Kraemer GC, Love JE, Kraemer BC. A drug repurposing screen identifies hepatitis C antivirals as inhibitors of the SARS-CoV2 main protease. PLoS One 2021; 16(2): e0245962.
[http://dx.doi.org/10.1371/journal.pone.0245962] [PMID: 33524017]
[16]
Xu T, Zheng W, Huang R. High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions. Drug Discov Today 2021; 26(10): 2439-44.
[http://dx.doi.org/10.1016/j.drudis.2021.05.012] [PMID: 34048893]
[17]
Blay V, Tolani B, Ho SP, Arkin MR. High-Throughput Screening: today’s biochemical and cell-based approaches. Drug Discov Today 2020; 25(10): 1807-21.
[http://dx.doi.org/10.1016/j.drudis.2020.07.024] [PMID: 32801051]
[18]
Macarrón R, Hertzberg RP. Design and implementation of high throughput screening assays. Mol Biotechnol 2011; 47(3): 270-85.
[http://dx.doi.org/10.1007/s12033-010-9335-9] [PMID: 20865348]
[19]
Mayr LM, Bojanic D. Novel trends in high-throughput screening. Curr Opin Pharmacol 2009; 9(5): 580-8.
[http://dx.doi.org/10.1016/j.coph.2009.08.004] [PMID: 19775937]
[20]
Selvaraj C, Panwar U, Dinesh DC, et al. Microsecond MD simulation and multiple-conformation virtual screening to identify potential anti-COVID-19 inhibitors against SARS-CoV-2 main protease. Front Chem 2021; 8: 595273.
[http://dx.doi.org/10.3389/fchem.2020.595273] [PMID: 33585398]
[21]
Giacomotto J, Ségalat L. High-throughput screening and small animal models, where are we? Br J Pharmacol 2010; 160(2): 204-16.
[http://dx.doi.org/10.1111/j.1476-5381.2010.00725.x] [PMID: 20423335]
[22]
Kunz-Schughart LA, Freyer JP, Hofstaedter F, Ebner R. The use of 3-D cultures for high-throughput screening: the multicellular spheroid model. SLAS Discov 2004; 9(4): 273-85.
[http://dx.doi.org/10.1177/1087057104265040] [PMID: 15191644]
[23]
Mirza MU, Froeyen M. Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase. J Pharm Anal 2020; 10(4): 320-8.
[http://dx.doi.org/10.1016/j.jpha.2020.04.008] [PMID: 32346490]
[24]
Yadav R, Imran M, Dhamija P, Chaurasia DK, Handu S. Virtual screening, ADMET prediction and dynamics simulation of potential compounds targeting the main protease of SARS-CoV-2. J Biomol Struct Dyn 2021; 39(17): 6617-32.
[http://dx.doi.org/10.1080/07391102.2020.1796812] [PMID: 32715956]
[25]
Joshi T, Sharma P, Joshi T, Pundir H, Mathpal S, Chandra S. Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19. Mol Divers 2021; 25(3): 1665-77.
[http://dx.doi.org/10.1007/s11030-020-10118-x] [PMID: 32602074]
[26]
Olubiyi OO, Olagunju M, Keutmann M, Loschwitz J, Strodel B. High throughput virtual screening to discover inhibitors of the main protease of the coronavirus SARS-CoV-2. Molecules 2020; 25(14): 3193.
[http://dx.doi.org/10.3390/molecules25143193] [PMID: 32668701]
[27]
Sharma P, Vijayan V, Pant P, et al. Identification of potential drug candidates to combat COVID-19: a structural study using the main protease (mpro) of SARS-CoV-2. J Biomol Struct Dyn 2021; 39(17): 6649-59.
[http://dx.doi.org/10.1080/07391102.2020.1798286] [PMID: 32741313]
[28]
Achilonu I, Iwuchukwu EA, Achilonu OJ, Fernandes MA, Sayed Y. Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2–P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach. J Mol Graph Model 2020; 101: 107730.
[http://dx.doi.org/10.1016/j.jmgm.2020.107730] [PMID: 32920239]
[29]
Muteeb G, Alshoaibi A, Aatif M, Rehman MT, Qayyum MZ. Screening marine algae metabolites as high-affinity inhibitors of SARS-CoV-2 main protease (3CLpro): An in silico analysis to identify novel drug candidates to combat COVID-19 pandemic. Appl Biol Chem 2020; 63(1): 79.
[http://dx.doi.org/10.1186/s13765-020-00564-4] [PMID: 33251389]
[30]
Jukič M, Janežič D, Bren U. Ensemble Docking Coupled to Linear Interaction Energy Calculations for Identification of Coronavirus Main Protease (3CLpro) Non-Covalent Small-Molecule Inhibitors. Molecules 2020; 25(24): 5808.
[http://dx.doi.org/10.3390/molecules25245808] [PMID: 33316996]
[31]
Sabe VT, Ntombela T, Jhamba LA, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224: 113705.
[http://dx.doi.org/10.1016/j.ejmech.2021.113705] [PMID: 34303871]
[32]
Huang R, Xu M, Zhu H, et al. Biological activity-based modeling identifies antiviral leads against SARS-CoV-2. Nat Biotechnol 2021; 39(6): 747-53.
[http://dx.doi.org/10.1038/s41587-021-00839-1] [PMID: 33623157]
[33]
Haiyan Q, Gangan Y, Zhenghao F, Xiaoping L, Yunyu C. Miniaturized high-throughput screening assays for the discovery of SARS-CoV-2 main protease inhibitors. Chem Life 2021; 41(2): 207-14.
[http://dx.doi.org/10.13488/j.smhx.20200689]
[34]
Shrimp JH, Janiszewski J, Chen CZ, et al. A suite of TMPRSS2 assays for screening drug repurposing candidates as potential treatments of COVID-19. bioRxiv 2022.
[http://dx.doi.org/10.1101/2022.02.04.479134]
[35]
Narayanan N, Nair DT. Vitamin B12 may inhibit RNA-DEPENDENT-RNA polymerase activity of nsp12 from the SARS-COV -2 virus. IUBMB Life 2020; 72(10): 2112-20.
[http://dx.doi.org/10.1002/iub.2359] [PMID: 32812340]
[36]
Zhu W, Chen CZ, Gorshkov K, Xu M, Lo DC, Zheng W. RNA-Dependent RNA Polymerase as a Target for COVID-19 Drug Discovery. SLAS Discov 2020; 25(10): 1141-51.
[http://dx.doi.org/10.1177/2472555220942123] [PMID: 32660307]
[37]
Romeo A, Iacovelli F, Falconi M. Targeting the SARS-CoV-2 spike glycoprotein prefusion conformation: Virtual screening and molecular dynamics simulations applied to the identification of potential fusion inhibitors. Virus Res 2020; 286: 198068.
[http://dx.doi.org/10.1016/j.virusres.2020.198068] [PMID: 32565126]
[38]
Chen CZ, Xu M, Pradhan M, et al. Identifying SARS-CoV-2 entry inhibitors through drug repurposing screens of SARS-S and MERS-S pseudotyped particles. ACS Pharmacol Transl Sci 2020; 3(6): 1165-75.
[http://dx.doi.org/10.1021/acsptsci.0c00112] [PMID: 33330839]
[39]
Dieterle ME, Haslwanter D, Bortz RH III, et al. A replication-competent vesicular stomatitis virus for studies of SARS-CoV-2 spike-mediated cell entry and its inhibition. Cell Host Microbe 2020; 28(3): 486-496.e6.
[http://dx.doi.org/10.1016/j.chom.2020.06.020] [PMID: 32738193]
[40]
Awad IE, Abu-Saleh AAAA, Sharma S, Yadav A, Poirier RA. High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein. J Biomol Struct Dyn 2022; 40(5): 2099-112.
[http://dx.doi.org/10.1080/07391102.2020.1835721] [PMID: 33103586]
[41]
Dhameliya TM, Nagar PR, Gajjar ND. Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations. Mol Divers 2022; 26(5): 2775-92.
[http://dx.doi.org/10.1007/s11030-022-10394-9] [PMID: 35132518]
[42]
Power H, Wu J, Turville S, et al. Virtual screening and in vitro validation of natural compound inhibitors against SARS-CoV-2 spike protein. Bioorg Chem 2022; 119: 105574.
[http://dx.doi.org/10.1016/j.bioorg.2021.105574] [PMID: 34971947]
[43]
Shen M, Liu C, Xu R, et al. Predicting the animal susceptibility and therapeutic drugs to SARS-CoV-2 based on spike glycoprotein combined with ACE2. Front Genet 2020; 11: 575012.
[http://dx.doi.org/10.3389/fgene.2020.575012] [PMID: 33193684]
[44]
Hanson QM, Wilson KM, Shen M, et al. Targeting ACE2–RBD interaction as a platform for COVID-19 therapeutics: development and drug-repurposing screen of an AlphaLISA proximity assay. ACS Pharmacol Transl Sci 2020; 3(6): 1352-60.
[http://dx.doi.org/10.1021/acsptsci.0c00161] [PMID: 33330843]
[45]
Tietjen I, Cassel J, Register ET, et al. The natural stilbenoid (–)-hopeaphenol inhibits cellular entry of SARS-CoV-2 USA-WA1/2020, B.1.1.7, and B.1.351 variants. Antimicrob Agents Chemother 2021; 65(12): e00772-21.
[http://dx.doi.org/10.1128/AAC.00772-21] [PMID: 34543092]
[46]
Gopinath K, Jokinen EM, Kurkinen ST, Pentikäinen OT. Screening of natural products targeting SARS-CoV-2–ACE2 receptor interface – a MixMD based HTVS pipeline. Front Chem 2020; 8: 589769.
[http://dx.doi.org/10.3389/fchem.2020.589769] [PMID: 33330376]
[47]
Zhang Y, Wang S, Wu Y, et al. Virus-Free and Live-Cell visualizing SARS-CoV-2 cell entry for studies of neutralizing antibodies and compound inhibitors. Small Methods 2021; 5(2): 2001031.
[http://dx.doi.org/10.1002/smtd.202001031] [PMID: 33614907]
[48]
Aatif M, Muteeb G, Alsultan A, Alshoaibi A, Khelif BY. Dieckol and its derivatives as potential inhibitors of SARS-CoV-2 spike protein (UK Strain: VUI 202012/01): A computational study. Mar Drugs 2021; 19(5): 242.
[http://dx.doi.org/10.3390/md19050242] [PMID: 33922914]
[49]
David AB, Diamant E, Dor E, et al. Identification of SARS-CoV-2 receptor binding inhibitors by in vitro screening of drug libraries. Molecules 2021; 26(11): 3213.
[http://dx.doi.org/10.3390/molecules26113213] [PMID: 34072087]
[50]
Haga K, Takai-Todaka R, Sawada A, Katayama K. Luciferase-based quantification of membrane fusion induced by SARS-COV-2 S protein. Genes Cells 2022; 27(8): 537-43.
[http://dx.doi.org/10.1111/gtc.12945] [PMID: 35488742]
[51]
Taha Z, Arulanandam R, Maznyi G, et al. Identification of FDA-approved bifonazole as a SARS-CoV-2 blocking agent following a bioreporter drug screen. Mol Ther 2022; 30(9): 2998-3016.
[http://dx.doi.org/10.1016/j.ymthe.2022.04.025] [PMID: 35526097]
[52]
Li C, Zhou H, Guo L, et al. Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2. J Transl Med 2022; 20(1): 314.
[http://dx.doi.org/10.1186/s12967-022-03501-9] [PMID: 35836239]
[53]
Vivek-Ananth RP, Rana A, Rajan N, Biswal HS, Samal A. In silico identification of potential natural product inhibitors of human proteases Key to SARS-CoV-2 Infection. Molecules 2020; 25(17): 3822.
[http://dx.doi.org/10.3390/molecules25173822] [PMID: 32842606]
[54]
Shrimp JH, Kales SC, Sanderson PE, Simeonov A, Shen M, Hall MD. An Enzymatic TMPRSS2 Assay for Assessment of Clinical Candidates and Discovery of Inhibitors as Potential Treatment of COVID-19. ACS Pharmacol Transl Sci 2020; 3(5): 997-1007.
[http://dx.doi.org/10.1021/acsptsci.0c00106] [PMID: 33062952]
[55]
Chen Y, Lear TB, Evankovich JW, et al. A high-throughput screen for TMPRSS2 expression identifies FDA-approved compounds that can limit SARS-CoV-2 entry. Nat Commun 2021; 12(1): 3907.
[http://dx.doi.org/10.1038/s41467-021-24156-y] [PMID: 34162861]
[56]
Talluri S. Virtual high throughput screening based prediction of potential drugs for COVID-19. Preprints 2020.
[http://dx.doi.org/10.20944/preprints202002.0418.v1]
[57]
Singh S, Florez H. Coronavirus disease 2019 drug discovery through molecular docking. F1000 Res 2020; 9: 502.
[http://dx.doi.org/10.12688/f1000research.24218.1] [PMID: 32704354]
[58]
Balakrishnan V, Lakshminarayanan K. Screening of FDA Approved Drugs Against SARS-CoV-2 Main Protease: Coronavirus Disease. Int J Pept Res Ther 2021; 27(1): 651-8.
[http://dx.doi.org/10.1007/s10989-020-10115-6] [PMID: 33013255]
[59]
Haider Z, Subhani MM, Farooq MA, et al. In-silico pharmacophoric and molecular docking-based drug discovery against the Main Protease (Mpro) of SARS-CoV-2, a causative agent COVID-19. Pak J Pharm Sci 2020; 33(6): 2697-705.
[PMID: 33867348]
[60]
Kumar R, Kumar V, Lee KW. A computational drug repurposing approach in identifying the cephalosporin antibiotic and anti-hepatitis C drug derivatives for COVID-19 treatment. Comput Biol Med 2021; 130: 104186.
[http://dx.doi.org/10.1016/j.compbiomed.2020.104186] [PMID: 33360831]
[61]
Ahmed MZ, Zia Q, Haque A, et al. Aminoglycosides as potential inhibitors of SARS-CoV-2 main protease: an in silico drug repurposing study on FDA-approved antiviral and anti-infection agents. J Infect Public Health 2021; 14(5): 611-9.
[http://dx.doi.org/10.1016/j.jiph.2021.01.016] [PMID: 33866129]
[62]
Jairajpuri DS, Hussain A, Nasreen K, et al. Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations. Saudi J Biol Sci 2021; 28(4): 2423-31.
[http://dx.doi.org/10.1016/j.sjbs.2021.01.040] [PMID: 33526965]
[63]
Liu W-S. Screening potential FDA-approved inhibitors of the SARS-CoV-2 major protease 3CLpro through high-throughput virtual screening and molecular dynamics simulation. Aging 2021.
[64]
Omrani M, Bayati M, Mehrbod P, Asmari Bardazard K, Nejad-Ebrahimi S. Natural products as inhibitors of COVID-19 main protease–A virtual screening by molecular docking. Ulum-i Daruyi 2021.
[http://dx.doi.org/10.34172/PS.2021.11]
[65]
Singh S, Florez H. Bioinformatic study to discover natural molecules with activity against COVID-19. F1000 Res 2020; 9: 1203.
[http://dx.doi.org/10.12688/f1000research.26731.1] [PMID: 33145015]
[66]
Jade D, Ayyamperumal S, Tallapaneni V, et al. Virtual high throughput screening: Potential inhibitors for SARS-CoV-2 PLPRO and 3CLPRO proteases. Eur J Pharmacol 2021; 901: 174082.
[http://dx.doi.org/10.1016/j.ejphar.2021.174082] [PMID: 33823185]
[67]
Kumar V, Parate S, Yoon S, Lee G, Lee KW. Computational Simulations Identified Marine-Derived Natural Bioactive Compounds as Replication Inhibitors of SARS-CoV-2. Front Microbiol 2021; 12: 647295.
[http://dx.doi.org/10.3389/fmicb.2021.647295] [PMID: 33967984]
[68]
Pohl MO, Busnadiego I, Marrafino F, et al. Combined computational and cellular screening identifies synergistic inhibition of SARS-CoV-2 by lenvatinib and remdesivir. J Gen Virol 2021; 102(7)
[http://dx.doi.org/10.1099/jgv.0.001625]
[69]
Sobhia ME, Kumar GS, Sivangula S, et al. Rapid structure-based identification of potential SARS-CoV-2 main protease inhibitors. Future Med Chem 2021; 13(17): 1435-50.
[PMID: 34169728]
[70]
Clyde A, Galanie S, Kneller DW, et al. High-throughput virtual screening and validation of a SARS-CoV-2 main protease noncovalent inhibitor. J Chem Inf Model 2022; 62(1): 116-28.
[http://dx.doi.org/10.1021/acs.jcim.1c00851] [PMID: 34793155]
[71]
Gupta Y, Kumar S, Zak SE, et al. Antiviral evaluation of hydroxyethylamine analogs: Inhibitors of SARS-CoV-2 main protease (3CLpro), a virtual screening and simulation approach. Bioorg Med Chem 2021; 47: 116393.
[http://dx.doi.org/10.1016/j.bmc.2021.116393] [PMID: 34509862]
[72]
Omer SE, Ibrahim TM, Krar OA, et al. Drug repurposing for SARS-CoV-2 main protease: Molecular docking and molecular dynamics investigations. Biochem Biophys Rep 2022; 29: 101225.
[http://dx.doi.org/10.1016/j.bbrep.2022.101225] [PMID: 35128086]
[73]
Gupta SS, Kumar A, Shankar R, Sharma U. In silico approach for identifying natural lead molecules against SARS-COV-2. J Mol Graph Model 2021; 106: 107916.
[http://dx.doi.org/10.1016/j.jmgm.2021.107916] [PMID: 33892297]
[74]
Gupta Y, Maciorowski D, Zak SE, et al. Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening pipeline and in vitro validation assays. Methods 2021; 195: 57-71.
[http://dx.doi.org/10.1016/j.ymeth.2021.01.003] [PMID: 33453392]
[75]
Plassche MAT, Barniol-Xicota M, Verhelst SHL. Peptidyl acyloxymethyl ketones as activity-based probes for the main protease of SARS-CoV-2**. ChemBioChem 2020; 21(23): 3383-8.
[http://dx.doi.org/10.1002/cbic.202000371] [PMID: 32717117]
[76]
Zhu W, Xu M, Chen CZ, et al. Identification of SARS-CoV-2 3CL protease inhibitors by a quantitative high-throughput screening. ACS Pharmacol Transl Sci 2020; 3(5): 1008-16.
[http://dx.doi.org/10.1021/acsptsci.0c00108] [PMID: 33062953]
[77]
Sencanski M, Perovic V, Pajovic SB, Adzic M, Paessler S, Glisic S. Drug repurposing for candidate SARS-CoV-2 main protease inhibitors by a novel in silico method. Molecules 2020; 25(17): 3830.
[http://dx.doi.org/10.3390/molecules25173830] [PMID: 32842509]
[78]
Jin Z, Du X, Xu Y, et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 2020; 582(7811): 289-93.
[http://dx.doi.org/10.1038/s41586-020-2223-y] [PMID: 32272481]
[79]
Coelho C, Gallo G, Campos CB, Hardy L, Würtele M. Biochemical screening for SARS-CoV-2 main protease inhibitors. PLoS One 2020; 15(10): e0240079.
[http://dx.doi.org/10.1371/journal.pone.0240079] [PMID: 33022015]
[80]
Milligan JC, Zeisner TU, Papageorgiou G, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of Nsp5 main protease. Biochem J 2021; 478(13): 2499-515.
[http://dx.doi.org/10.1042/BCJ20210197] [PMID: 34198327]
[81]
Pundir H, Joshi T, Joshi T, et al. Using Chou’s 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors. Mol Divers 2021; 25(3): 1731-44.
[http://dx.doi.org/10.1007/s11030-020-10148-5] [PMID: 33079314]
[82]
Froggat HM. Development of a fluorescence-based, high-throughput SARS-CoV-2 3CLpro reporter assay. J Virol 2020.
[http://dx.doi.org/10.1128/JVI.01265-20]
[83]
Rothan HA, Teoh TC. Cell-based high-throughput screening protocol for discovering antiviral inhibitors against SARS-COV-2 main protease (3CLpro). Mol Biotechnol 2021; 63(3): 240-8.
[http://dx.doi.org/10.1007/s12033-021-00299-7] [PMID: 33464543]
[84]
Li X, Lidsky PV, Xiao Y, et al. Ethacridine inhibits SARS-CoV-2 by inactivating viral particles. PLoS Pathog 2021; 17(9): e1009898.
[http://dx.doi.org/10.1371/journal.ppat.1009898] [PMID: 34478458]
[85]
Ishola AA, Adedirin O, Joshi T, Chandra S. QSAR modeling and pharmacoinformatics of SARS coronavirus 3C-like protease inhibitors. Comput Biol Med 2021; 134: 104483.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104483] [PMID: 34020129]
[86]
Gogoi B, Chowdhury P, Goswami N, et al. Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation. Mol Divers 2021; 25(3): 1963-77.
[http://dx.doi.org/10.1007/s11030-021-10211-9] [PMID: 33856591]
[87]
Zhang S, Pei R, Li M, et al. Cocktail polysaccharides isolated from Ecklonia kurome against the SARS-CoV-2 infection. Carbohydr Polym 2022; 275: 118779.
[http://dx.doi.org/10.1016/j.carbpol.2021.118779] [PMID: 34742404]
[88]
Yan G, Li D, Lin Y, et al. Development of a simple and miniaturized sandwich-like fluorescence polarization assay for rapid screening of SARS-CoV-2 main protease inhibitors. Cell Biosci 2021; 11(1): 199.
[http://dx.doi.org/10.1186/s13578-021-00720-3] [PMID: 34865653]
[89]
Hasegawa T, Imamura RM, Suzuki T, et al. Application of acoustic ejection MS system to high-throughput screening for SARS-CoV-2 3CL protease inhibitors. Chem Pharm Bull (Tokyo) 2022; 70(3): 199-201.
[http://dx.doi.org/10.1248/cpb.c21-01003] [PMID: 34937844]
[90]
Chen J, Zhang Y, Zeng D, et al. Merbromin is a mixed-type inhibitor of 3-chyomotrypsin like protease of SARS-CoV-2. Biochem Biophys Res Commun 2022; 591: 118-23.
[http://dx.doi.org/10.1016/j.bbrc.2021.12.108] [PMID: 35007835]
[91]
Legare S, Heide F, Bailey-Elkin BA, Stetefeld J. Improved SARS-CoV-2 main protease high-throughput screening assay using a 5-carboxyfluorescein substrate. J Biol Chem 2022; 298(4): 101739.
[http://dx.doi.org/10.1016/j.jbc.2022.101739] [PMID: 35182525]
[92]
Chen KY, Krischuns T, Varga LO, et al. A highly sensitive cell-based luciferase assay for high-throughput automated screening of SARS-CoV-2 nsp5/3CLpro inhibitors. Antiviral Res 2022; 201: 105272.
[http://dx.doi.org/10.1016/j.antiviral.2022.105272] [PMID: 35278581]
[93]
Heilmann E, Costacurta F, Geley S, et al. A VSV-based assay quantifies coronavirus Mpro/3CLpro/Nsp5 main protease activity and chemical inhibition. Commun Biol 2022; 5(1): 391.
[http://dx.doi.org/10.1038/s42003-022-03277-0] [PMID: 35478219]
[94]
Hou N, Peng C, Zhang L, Zhu Y, Hu Q. BRET-based self-cleaving biosensors for SARS-CoV-2 3CLpro inhibitor discovery. Microbiol Spectr 2022; 10(4): e02559-21.
[http://dx.doi.org/10.1128/spectrum.02559-21] [PMID: 35758897]
[95]
Qi X, Li B, Omarini AB, Gand M, Zhang X, Wang J. Discovery of TCMs and derivatives against the main protease of SARS-CoV-2 via high throughput screening, ADMET analysis, and inhibition assay in vitro. J Mol Struct 2022; 1268: 133709.
[http://dx.doi.org/10.1016/j.molstruc.2022.133709] [PMID: 35846732]
[96]
Chen Z, Cui Q, Cooper L, et al. Ginkgolic acid and anacardic acid are specific covalent inhibitors of SARS-CoV-2 cysteine proteases. Cell Biosci 2021; 11(1): 45.
[http://dx.doi.org/10.1186/s13578-021-00564-x] [PMID: 33640032]
[97]
Huynh T, Cornell W, Luan B. In silico exploration of inhibitors for SARS-CoV-2's papain-like protease. Front Chem 2021; 8: 624163.
[http://dx.doi.org/10.3389/fchem.2020.624163] [PMID: 33614597]
[98]
Jupudi S, Rajagopal K, Murugesan S, et al. Identification of Papain-Like Protease inhibitors of SARS CoV-2 through HTVS, Molecular docking, MMGBSA and Molecular dynamics approach. S Afr J Bot 2022; 151: 82-91.
[PMID: 34876768]
[99]
Parmar P, Rao P, Sharma A, et al. Meticulous assessment of natural compounds from NPASS database for identifying analogue of GRL0617, the only known inhibitor for SARS-CoV2 papain-like protease (PLpro) using rigorous computational workflow. Mol Divers 2022; 26(1): 389-407.
[http://dx.doi.org/10.1007/s11030-021-10233-3] [PMID: 34008129]
[100]
Ma C, Sacco MD, Xia Z, et al. Discovery of SARS-CoV-2 Papain-like Protease Inhibitors through a Combination of High-Throughput Screening and a FlipGFP-Based Reporter Assay. ACS Cent Sci 2021; 7(7): 1245-60.
[http://dx.doi.org/10.1021/acscentsci.1c00519] [PMID: 34341772]
[101]
Xu Y, Chen K, Pan J, et al. Repurposing clinically approved drugs for COVID-19 treatment targeting SARS-CoV-2 papain-like protease. Int J Biol Macromol 2021; 188: 137-46.
[http://dx.doi.org/10.1016/j.ijbiomac.2021.07.184] [PMID: 34364941]
[102]
Napolitano V, Dabrowska A, Schorpp K, et al. Acriflavine, a clinically approved drug, inhibits SARS-CoV-2 and other betacoronaviruses. Cell Chem Biol 2022; 29(5): 774-784.e8.
[http://dx.doi.org/10.1016/j.chembiol.2021.11.006] [PMID: 35021060]
[103]
Yan H, Liu Z, Yan G, et al. A robust high-throughput fluorescence polarization assay for rapid screening of SARS-CoV-2 papain-like protease inhibitors. Virology 2022; 574: 18-24.
[http://dx.doi.org/10.1016/j.virol.2022.07.006] [PMID: 35870326]
[104]
Jukič M, Janežič D, Bren U. Potential Novel Thioether-Amide or Guanidine-Linker Class of SARS-CoV-2 Virus RNA-Dependent RNA Polymerase Inhibitors Identified by High-Throughput Virtual Screening Coupled to Free-Energy Calculations. Int J Mol Sci 2021; 22(20): 11143.
[http://dx.doi.org/10.3390/ijms222011143] [PMID: 34681802]
[105]
Khater S, Kumar P, Dasgupta N, Das G, Ray S, Prakash A. Combining SARS-CoV-2 proofreading exonuclease and RNA-dependent RNA polymerase inhibitors as a strategy to combat COVID-19: A high-throughput in silico screening. Front Microbiol 2021; 12: 647693.
[http://dx.doi.org/10.3389/fmicb.2021.647693] [PMID: 34354677]
[106]
Pirzada RH, Haseeb M, Batool M, Kim M, Choi S. Remdesivir and Ledipasvir among the FDA-Approved Antiviral Drugs Have Potential to Inhibit SARS-CoV-2 Replication. Cells 2021; 10(5): 1052.
[http://dx.doi.org/10.3390/cells10051052] [PMID: 33946869]
[107]
Bertolin AP, Weissmann F, Zeng J, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp12/7/8 RNA-dependent RNA polymerase. Biochem J 2021; 478(13): 2425-43.
[http://dx.doi.org/10.1042/BCJ20210200] [PMID: 34198323]
[108]
Min JS, Kwon S, Jin YH. SARS-CoV-2 RdRp Inhibitors Selected from a Cell-Based SARS-CoV-2 RdRp Activity Assay System. Biomedicines 2021; 9(8): 996.
[http://dx.doi.org/10.3390/biomedicines9080996] [PMID: 34440200]
[109]
Bai X, Sun H, Wu S, Li Y, Wang L, Hong B. Identifying Small-Molecule Inhibitors of SARS-CoV-2 RNA-Dependent RNA Polymerase by Establishing a Fluorometric Assay. Front Immunol 2022; 13: 844749.
[http://dx.doi.org/10.3389/fimmu.2022.844749] [PMID: 35464436]
[110]
Alturki NA, Mashraqi MM, Alzamami A, et al. In-Silico Screening and Molecular Dynamics Simulation of Drug Bank Experimental Compounds against SARS-CoV-2. Molecules 2022; 27(14): 4391.
[http://dx.doi.org/10.3390/molecules27144391] [PMID: 35889265]
[111]
Ruan Z, Liu C, Guo Y, et al. SARS-CoV-2 and SARS-CoV: Virtual screening of potential inhibitors targeting RNA-dependent RNA polymerase activity (NSP12). J Med Virol 2021; 93(1): 389-400.
[http://dx.doi.org/10.1002/jmv.26222] [PMID: 32579254]
[112]
Ahmad S, Waheed Y, Ismail S, Bhatti S, Abbasi SW, Muhammad K. Structure-based virtual screening identifies multiple stable binding sites at the RecA domains of SARS-CoV-2 helicase enzyme. Molecules 2021; 26(5): 1446.
[http://dx.doi.org/10.3390/molecules26051446] [PMID: 33800013]
[113]
Yazdi AK, Pakarian P, Perveen S, et al. Kinetic characterization of SARS-CoV-2 nsp13 ATPase activity and discovery of small-molecule inhibitors. ACS Infect Dis 2022; 8(8): 1533-42.
[http://dx.doi.org/10.1021/acsinfecdis.2c00165] [PMID: 35822715]
[114]
El Hassab MA, Eldehna WM, Al-Rashood ST, et al. Multi-stage structure-based virtual screening approach towards identification of potential SARS-CoV-2 NSP13 helicase inhibitors. J Enzyme Inhib Med Chem 2022; 37(1): 563-72.
[http://dx.doi.org/10.1080/14756366.2021.2022659] [PMID: 35012384]
[115]
Basu S, Mak T, Ulferts R, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of Nsp14 RNA cap methyltransferase. Biochem J 2021; 478(13): 2481-97.
[http://dx.doi.org/10.1042/BCJ20210219] [PMID: 34198328]
[116]
Bobrovs R, Kanepe I, Narvaiss N, et al. Discovery of SARS-CoV-2 Nsp14 and Nsp16 Methyltransferase Inhibitors by High-Throughput Virtual Screening. Pharmaceuticals (Basel) 2021; 14(12): 1243.
[http://dx.doi.org/10.3390/ph14121243] [PMID: 34959647]
[117]
Canal B, McClure AW, Curran JF, et al. Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp14/nsp10 exoribonuclease. Biochem J 2021; 478(13): 2445-64.
[http://dx.doi.org/10.1042/BCJ20210198] [PMID: 34198326]
[118]
Pearson LA, Green CJ, Lin D, et al. Development of a High-Throughput Screening Assay to Identify Inhibitors of the SARS-CoV-2 Guanine-N7-Methyltransferase Using RapidFire Mass Spectrometry. SLAS Discov 2021; 26(6): 749-56.
[http://dx.doi.org/10.1177/24725552211000652] [PMID: 33724070]
[119]
Batool A, Bibi N, Amin F, Kamal MA. Drug designing against NSP15 of SARS-COV2 via high throughput computational screening and structural dynamics approach. Eur J Pharmacol 2021; 892: 173779.
[http://dx.doi.org/10.1016/j.ejphar.2020.173779] [PMID: 33275961]
[120]
Chandra A, Gurjar V, Qamar I, Singh N. Identification of potential inhibitors of SARS-COV-2 endoribonuclease (EndoU) from FDA approved drugs: a drug repurposing approach to find therapeutics for COVID-19. J Biomol Struct Dyn 2021; 39(12): 4201-11.
[http://dx.doi.org/10.1080/07391102.2020.1775127] [PMID: 32462970]
[121]
Choi R, Zhou M, Shek R, et al. High-throughput screening of the ReFRAME, Pandemic Box, and COVID Box drug repurposing libraries against SARS-CoV-2 nsp15 endoribonuclease to identify small-molecule inhibitors of viral activity. PLoS One 2021; 16(4): e0250019.
[http://dx.doi.org/10.1371/journal.pone.0250019] [PMID: 33886614]
[122]
Krishnan DA, Sangeetha G, Vajravijayan S, Nandhagopal N, Gunasekaran K. Structure-based drug designing towards the identification of potential anti-viral for COVID-19 by targeting endoribonuclease NSP15. Inform Med Unlocked 2020; 20: 100392.
[http://dx.doi.org/10.1016/j.imu.2020.100392] [PMID: 32835078]
[123]
Saeed M, Saeed A, Alam MJ, Alreshidi M. Identification of persuasive antiviral natural compounds for COVID-19 by targeting endoribonuclease NSP15: A structural-bioinformatics approach. Molecules 2020; 25(23): 5657.
[http://dx.doi.org/10.3390/molecules25235657] [PMID: 33271751]
[124]
El Hassab MA, Ibrahim TM, Al-Rashood ST, Alharbi A, Eskandrani RO, Eldehna WM. In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches. J Enzyme Inhib Med Chem 2021; 36(1): 727-36.
[http://dx.doi.org/10.1080/14756366.2021.1885396] [PMID: 33685335]
[125]
Khalili Yazdi A, Li F, Devkota K, et al. A high-throughput radioactivity-based assay for screening SARS-CoV-2 nsp10-nsp16 complex. SLAS Discov 2021; 26(6): 757-65.
[http://dx.doi.org/10.1177/24725552211008863] [PMID: 33874769]
[126]
Perveen S, Khalili Yazdi A, Devkota K, et al. A high-throughput RNA displacement assay for screening SARS-CoV-2 nsp10-nsp16 complex toward developing therapeutics for COVID-19. SLAS Discov 2021; 26(5): 620-7.
[http://dx.doi.org/10.1177/2472555220985040] [PMID: 33423577]
[127]
Sulimov A, Kutov D, Ilin I, Xiao Y, Jiang S, Sulimov V. Novel inhibitors of 2′-O-methyltransferase of the SARS-CoV-2 coronavirus. Molecules 2022; 27(9): 2721.
[http://dx.doi.org/10.3390/molecules27092721] [PMID: 35566072]
[128]
Chen CZ, Shinn P, Itkin Z, et al. Drug repurposing screen for compounds inhibiting the cytopathic effect of SARS-CoV-2. Front Pharmacol 2021; 11: 592737.
[http://dx.doi.org/10.3389/fphar.2020.592737] [PMID: 33708112]
[129]
Ju X, Zhu Y, Wang Y, et al. A novel cell culture system modeling the SARS-CoV-2 life cycle. PLoS Pathog 2021; 17(3): e1009439.
[http://dx.doi.org/10.1371/journal.ppat.1009439] [PMID: 33711082]
[130]
Mirabelli C, Wotring JW, Zhang CJ, et al. Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19. Proc Natl Acad Sci USA 2021; 118(36): e2105815118.
[http://dx.doi.org/10.1073/pnas.2105815118] [PMID: 34413211]
[131]
Park JG, Oladunni FS, Chiem K, et al. Rapid in vitro assays for screening neutralizing antibodies and antivirals against SARS-CoV-2. J Virol Methods 2021; 287: 113995.
[http://dx.doi.org/10.1016/j.jviromet.2020.113995] [PMID: 33068703]
[132]
Xiao X, Wang C, Chang D, et al. Identification of potent and safe antiviral therapeutic candidates against SARS-CoV-2. Front Immunol 2020; 11: 586572.
[http://dx.doi.org/10.3389/fimmu.2020.586572] [PMID: 33324406]
[133]
Xu M, Pradhan M, Gorshkov K, et al. A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry. bioRxiv 2021.
[http://dx.doi.org/10.1101/2021.10.04.463106]
[134]
Yang L, Pei R, Li H, et al. Identification of SARS-CoV-2 entry inhibitors among already approved drugs. Acta Pharmacol Sin 2021; 42(8): 1347-53.
[http://dx.doi.org/10.1038/s41401-020-00556-6] [PMID: 33116249]
[135]
Yin X, Chen L, Yuan S, Liu L, Gao Z. A robust high-throughput fluorescent polarization assay for the evaluation and screening of SARS-CoV-2 fusion inhibitors. Bioorg Chem 2021; 116: 105362.
[http://dx.doi.org/10.1016/j.bioorg.2021.105362] [PMID: 34598089]
[136]
Han Y, Duan X, Yang L, et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature 2021; 589(7841): 270-5.
[http://dx.doi.org/10.1038/s41586-020-2901-9] [PMID: 33116299]
[137]
Gorshkov K, Chen CZ, de la Torre JC, Martinez-Sobrido L, Moran T, Zheng W. Development of a high-throughput homogeneous AlphaLISA drug screening assay for the detection of SARS-CoV-2 Nucleocapsid. bioRxiv 2020.
[http://dx.doi.org/10.1101/2020.08.20.258129]
[138]
Gorshkov K, Morales Vasquez D, Chiem K, et al. SARS-CoV-2 nucleocapsid protein TR-FRET assay amenable to high throughput screening. ACS Pharmacol Transl Sci 2022; 5(1): 8-19.
[http://dx.doi.org/10.1021/acsptsci.1c00182] [PMID: 35036857]
[139]
Dasovich M, Zhuo J, Goodman JA, et al. High-throughput activity assay for screening inhibitors of the SARS-CoV-2 Mac1 macrodomain. ACS Chem Biol 2022; 17(1): 17-23.
[http://dx.doi.org/10.1021/acschembio.1c00721] [PMID: 34904435]
[140]
Selvaraj C, Dinesh DC, Panwar U, Boura E, Singh SK. High-throughput screening and quantum mechanics for identifying potent inhibitors against Mac1 domain of SARS-CoV-2 Nsp3. IEEE/ACM Trans Comput Biol Bioinformatics 2021; 18(4): 1262-70.
[http://dx.doi.org/10.1109/TCBB.2020.3037136] [PMID: 33306471]
[141]
Roy A, Alhammad YM, McDonald P, et al. Discovery of compounds that inhibit SARS-CoV-2 Mac1-ADP-ribose binding by high-throughput screening. Antiviral Res 2022; 203: 105344.
[http://dx.doi.org/10.1016/j.antiviral.2022.105344] [PMID: 35598780]
[142]
Classen N, Ulrich D, Hofemeier A, et al. Broadly Applicable, Virus-Free Dual Reporter Assay to Identify Compounds Interfering with Membrane Fusion: Performance for HSV-1 and SARS-CoV-2. Viruses 2022; 14(7): 1354.
[http://dx.doi.org/10.3390/v14071354] [PMID: 35891336]
[143]
de Almeida L, da Silva ALN, Rodrigues TS, et al. Identification of immunomodulatory drugs that inhibit multiple inflammasomes and impair SARS-CoV-2 infection. Sci Adv 2022; 8(37): eabo5400.
[http://dx.doi.org/10.1126/sciadv.abo5400] [PMID: 36103544]
[144]
Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Molecules 2020; 25(6): 1375.
[http://dx.doi.org/10.3390/molecules25061375] [PMID: 32197324]
[145]
Kc GB, Bocci G, Verma S, et al. A machine learning platform to estimate anti-SARS-CoV-2 activities. Nat Mach Intell 2021; 3(6): 527-35.
[http://dx.doi.org/10.1038/s42256-021-00335-w]
[146]
Joshi T, Joshi T, Pundir H, Sharma P, Mathpal S, Chandra S. Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease. J Biomol Struct Dyn 2021; 39(17): 6728-46.
[http://dx.doi.org/10.1080/07391102.2020.1802341] [PMID: 32752947]
[147]
Joshi T, Sharma P, Mathpal S, et al. Computational investigation of drug bank compounds against 3C-like protease (3CL(pro)) of SARS-CoV-2 using deep learning and molecular dynamics simulation. Mol Divers 2022; 26(4): 2243-56.
[PMID: 34637068]
[148]
Srinivasan S, Batra R, Chan H, Kamath G, Cherukara MJ, Sankaranarayanan SKRS. Artificial intelligence-guided de novo molecular design targeting COVID-19. ACS Omega 2021; 6(19): 12557-66.
[http://dx.doi.org/10.1021/acsomega.1c00477] [PMID: 34056406]
[149]
Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep learning algorithms achieved satisfactory predictions when trained on a novel collection of anticoronavirus molecules. Front Genet 2021; 12: 744170.
[http://dx.doi.org/10.3389/fgene.2021.744170] [PMID: 34912370]

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