{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T04:50:32Z","timestamp":1791867032830,"version":"4.3.4"},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union's Horizon 2020 research and innovation programme","award":["Marie Sk\u0142odowska-Curie grant agreement No. 713679"],"award-info":[{"award-number":["Marie Sk\u0142odowska-Curie grant agreement No. 713679"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus\u2019 replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 \u00b5M, respectively.<\/jats:p>","DOI":"10.3390\/ijms21113793","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T10:02:45Z","timestamp":1590660165000},"page":"3793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":141,"title":["Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7654-6689","authenticated-orcid":false,"given":"Aleix","family":"Gimeno","sequence":"first","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00falia","family":"Mestres-Truyol","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-1935","authenticated-orcid":false,"given":"Mar\u00eda Jos\u00e9","family":"Ojeda-Montes","sequence":"additional","affiliation":[{"name":"Escoles Universit\u00e0ries Gimbernat i Tom\u00e0s Cerd\u00e0, 08174 Sant Cugat del Vall\u00e8s, Barcelona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4016-5558","authenticated-orcid":false,"given":"Guillem","family":"Macip","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9667-2818","authenticated-orcid":false,"given":"Bryan","family":"Saldivar-Espinoza","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9319-6377","authenticated-orcid":false,"given":"Adri\u00e0","family":"Cereto-Massagu\u00e9","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2598-8089","authenticated-orcid":false,"given":"Gerard","family":"Pujadas","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"},{"name":"EURECAT, TECNIO, CEICS, Avinguda Universitat 1, 43204 Reus Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0348-7497","authenticated-orcid":false,"given":"Santiago","family":"Garcia-Vallv\u00e9","sequence":"additional","affiliation":[{"name":"Departament de Bioqu\u00edmica i Biotecnologia, Research group in Cheminformatics &amp; Nutrition, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain"},{"name":"EURECAT, TECNIO, CEICS, Avinguda Universitat 1, 43204 Reus Catalonia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1056\/NEJMoa2001282","article-title":"A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19","volume":"382","author":"Cao","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_3","unstructured":"Statista (2020, April 12). Coronavirus (COVID-19) Death Rate in Italy as of April 11, 2020, by Age Group. Available online: https:\/\/www.statista.com\/statistics\/1106372\/coronavirus-death-rate-by-age-group-italy\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mirza, M.U., and Froeyen, M. (2020). Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against Main protease, Nsp12 RNA-dependent RNA polymerase and Nsp13 helicase. Preprints, 2020030085.","DOI":"10.20944\/preprints202003.0085.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"202000028","DOI":"10.1002\/minf.202000028","article-title":"Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds","volume":"39","author":"Ton","year":"2020","journal-title":"Mol. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1038\/s41422-020-0282-0","article-title":"Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) In Vitro","volume":"30","author":"Wang","year":"2020","journal-title":"Cell Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10970","DOI":"10.1073\/pnas.2005615117","article-title":"Effective Treatment of Severe COVID-19 Patients with Tocilizumab","volume":"117","author":"Xu","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1126\/science.abb3405","article-title":"Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved \u03b1-ketoamide inhibitors","volume":"368","author":"Zhang","year":"2020","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., and Peng, C. (2020). Structure of Mpro from COVID-19 virus and discovery of its inhibitors. Nature.","DOI":"10.1038\/s41586-020-2223-y"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kong, R., Yang, G., Xue, R., Liu, M., Wang, F., Hu, J., Guo, X., and Chang, S. (2020). COVID-19 Docking Server: An interactive server for docking small molecules, peptides and antibodies against potential targets of COVID-19. arXiv.","DOI":"10.1093\/bioinformatics\/btaa645"},{"key":"ref_11","unstructured":"Dang, M., and Song, J. (2020). 2019-nCoV 3C-Like Protease carries an activity-enhancing T285\/A variation which may contribute to its high infectivity. Preprints."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tang, B., He, F., Liu, D., Fang, M., Wu, Z., and Xu, D. (2020). AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. bioRxiv.","DOI":"10.1101\/2020.03.03.972133"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gimeno, A., Ojeda-Montes, M., Tom\u00e1s-Hern\u00e1ndez, S., Cereto-Massagu\u00e9, A., Beltr\u00e1n-Deb\u00f3n, R., Mulero, M., Pujadas, G., and Garcia-Vallv\u00e9, S. (2019). The Light and Dark Sides of Virtual Screening: What Is There to Know?. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20061375"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"129","DOI":"10.12688\/f1000research.22457.2","article-title":"Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL pro) structure: Virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates","volume":"9","author":"Chen","year":"2020","journal-title":"F1000Research"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jgg.2020.02.001","article-title":"Potential inhibitors against 2019-nCoV coronavirus M protease from clinically approved medicines","volume":"47","author":"Liu","year":"2020","journal-title":"J. Genet. Genom."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Adem, S., Eyupoglu, V., Sarfraz, I., Rasul, A., and Ali, M. (2020). Identification of potent COVID-19 main protease (Mpro) inhibitors from natural polyphenols: An in silico strategy unveils a hope against CORONA. Preprints, 2020030333.","DOI":"10.20944\/preprints202003.0333.v1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yoshino, R., Yasuo, N., and Sekijima, M. (2020). Identification of key interactions between SARS-CoV-2 Main Protease and inhibitor drug candidates. ChemRxiv.","DOI":"10.26434\/chemrxiv.12009636"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hosseini, F.S., and Amanlou, M. (2020). Simeprevir, potential candidate to repurpose for coronavirus infection: Virtual screening and molecular docking study. Preprints, 2020020438.","DOI":"10.20944\/preprints202002.0438.v1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bzowka, M., Mitusinska, K., Raczynska, A., Samol, A., Tuszynski, J.A., and Gora, A. (2020). Molecular Dynamics Simulations Indicate the SARS-CoV-2 Mpro Is Not a Viable Target for Small-Molecule Inhibitors Design. bioRxiv.","DOI":"10.1101\/2020.02.27.968008"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, Z., Peng, C., Shi, Y., Zhu, Z., Mu, K., Wang, X., and Zhu, W. (2020). Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. bioRxiv.","DOI":"10.1101\/2020.01.27.921627"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khaerunnisa, S., Kurniawan, H., Awaluddin, R., and Suhartati, S. (2020). Potential Inhibitor of COVID-19 Main Protease (M pro) from Several Medicinal Plant Compounds by Molecular Docking Study. Preprints, 2020030226.","DOI":"10.20944\/preprints202003.0226.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.drudis.2012.08.005","article-title":"In silico drug repositioning: What we need to know","volume":"18","author":"Liu","year":"2013","journal-title":"Drug Discov. Today"},{"key":"ref_23","unstructured":"(2020, March 31). GISAID. Available online: https:\/\/www.gisaid.org\/."},{"key":"ref_24","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Maestro, Schr\u00f6dinger, LLC."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Macchiagodena, M., Pagliai, M., and Procacci, P. (2020). Inhibition of the Main Protease 3CL-pro of the Coronavirus Disease 19 via Structure-Based Ligand Design and Molecular Modeling. arXiv.","DOI":"10.1016\/j.cplett.2020.137489"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bouchentouf, S., and Missoum, N. (2020). Identification of Compounds from Nigella Sativa as New PotentialInhibitors of 2019 Novel Coronasvirus (Covid-19): Molecular DockingStudy. ChemRxiv.","DOI":"10.26434\/chemrxiv.12055716"},{"key":"ref_27","unstructured":"(2020, March 26). Structure of COVID-19 Main Protease Bound to Potent Broad-Spectrum Non-Covalent Inhibitor X77. Available online: http:\/\/www.rcsb.org\/structure\/6W63."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Novi\u010d, M., Tibaut, T., Anderluh, M., Bori\u0161ek, J., and Toma\u0161i\u010d, T. (2016). The Comparison of Docking Search Algorithms and Scoring Functions. Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery, Medical Information Science Reference.","DOI":"10.4018\/978-1-5225-0115-2.ch004"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ul-Haq, Z., and Madura, J.D. (2017). Frontiers in Computational Chemistry, Bentham Science Publishers.","DOI":"10.2174\/97816810816701170301"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1021\/ci100436p","article-title":"FRED pose prediction and virtual screening accuracy","volume":"51","author":"McGann","year":"2011","journal-title":"J. Chem. Inf. Model."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1021\/jm030644s","article-title":"Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening","volume":"47","author":"Halgren","year":"2004","journal-title":"J. Med. Chem."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Torres, P.H.M., Sodero, A.C.R., Jofily, P., and Silva, F.P. (2019). Key Topics in Molecular Docking for Drug Design. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20184574"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13321-017-0227-x","article-title":"Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds","volume":"9","author":"Chaput","year":"2017","journal-title":"J. Cheminform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s12539-019-00327-w","article-title":"An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking","volume":"11","author":"Li","year":"2019","journal-title":"Interdiscip. Sci."},{"key":"ref_35","unstructured":"(2020). SchrSchrdinger Release 2020-2: Prime, Schr\u00f6dinger, LLC."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: A major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1517\/17425255.2014.856881","article-title":"Assessment of nonsteroidal anti-inflammatory drug-induced cardiotoxicity","volume":"10","author":"Singh","year":"2014","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s00408-016-9973-1","article-title":"Non-steroidal Anti-inflammatory Drugs may Worsen the Course of Community-Acquired Pneumonia: A Cohort Study","volume":"195","author":"Basille","year":"2017","journal-title":"Lung"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hada, M. (2020). Chemotherapeutic Strategy with Synbiotics, Thalidomide and Celecoxib for severe COVID-19 Pneumonia. Association between microbiota, chronic inflammation and pneumonia. Preprint.","DOI":"10.22541\/au.159188529.93357127"},{"key":"ref_40","unstructured":"(2020, April 06). Decision Memo\u2014Analysis and Recommendations for Agency Action-COX-2 Selective and Non-Selective NSAIDs, Available online: https:\/\/www.fda.gov\/media\/74279\/download."},{"key":"ref_41","unstructured":"(2020, April 06). CDER Statement: FDA Approves Labeling Supplement for Celebrex (Celecoxib), Available online: https:\/\/www.fda.gov\/drugs\/drug-safety-and-availability\/cder-statement-fda-approves-labeling-supplement-celebrex-celecoxib."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1021\/acsmedchemlett.7b00462","article-title":"Data Sets Representative of the Structures and Experimental Properties of FDA-Approved Drugs","volume":"9","author":"Douguet","year":"2018","journal-title":"ACS Med. Chem. Lett."},{"key":"ref_43","unstructured":"Elsevier (2020, March 16). Reaxys Database. Available online: https:\/\/www.reaxys.com."},{"key":"ref_44","unstructured":"(2020, March 16). OTAVA Chemicals. Available online: https:\/\/otavachemicals.com\/targets\/sars-cov-2-targeted-libraries."},{"key":"ref_45","unstructured":"(2020, March 16). COVID Moonshot. Available online: https:\/\/covid.postera.ai\/covid."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"ZINC 15\u2014Ligand Discovery for Everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_47","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Glide, Schr\u00f6dinger, LLC."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_49","unstructured":"(2019). OMEGA 3.1.1.2: OpenEye Scientific Software, OpenEye Scientific Software."},{"key":"ref_50","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: LigPrep, Schr\u00f6dinger, LLC."},{"key":"ref_51","unstructured":"(2019). OEDOCKING 3.4.0.2: OpenEye Scientific Software, OpenEye Scientific Software."},{"key":"ref_52","unstructured":"(2019). QUACPAC 2.0.2.2: OpenEye Scientific Software, OpenEye Scientific Software."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/bmb.2006.494034042644","article-title":"Biomolecules in the computer: Jmol to the rescue","volume":"34","year":"2006","journal-title":"Biochem. Mol. Biol. Educ."},{"key":"ref_54","unstructured":"(2020, March 31). Protein Data Bank in Europe. Available online: https:\/\/www.ebi.ac.uk\/pdbe."},{"key":"ref_55","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Protein Preparation Wizard, Schr\u00f6dinger, LLC."},{"key":"ref_56","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Impact, Schr\u00f6dinger, LLC."},{"key":"ref_57","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Epik, Schr\u00f6dinger, LLC."},{"key":"ref_58","unstructured":"(2019). Schr\u00f6dinger Release 2019-3: Prime, Schr\u00f6dinger, LLC."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1021\/ct100578z","article-title":"PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions","volume":"7","author":"Olsson","year":"2011","journal-title":"J. Chem. Theory Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1021\/ci800293n","article-title":"Validation of molecular docking programs for virtual screening against dihydropteroate synthase","volume":"49","author":"Hevener","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"ref_62","unstructured":"(2019). Schr\u00f6dinger Release 2019-3, Schr\u00f6dinger, LLC."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/11\/3793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:09Z","timestamp":1760175189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/11\/3793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,27]]},"references-count":62,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["ijms21113793"],"URL":"https:\/\/doi.org\/10.3390\/ijms21113793","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,27]]}}}