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Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

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Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA
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Department of Microbiology and Plant Pathology, University of California, 900 University, Riverside, CA 92521, USA
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Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA
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Department of Neurology, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL 32224, USA
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Department of Molecular, Cell and Systems Biology, University of California, 900 University, Riverside, CA 92521, USA
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Department of Molecular Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
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Department of Quantitative Health Science, Division of Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA
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Department of Clinical Genomics, Mayo Clinic, Rochester, MN 55905, USA
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Department of Biochemistry & Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
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Author to whom correspondence should be addressed.
Academic Editor: Alessandro Paiardini
Biomolecules 2021, 11(6), 787; https://doi.org/10.3390/biom11060787
Received: 15 April 2021 / Revised: 19 May 2021 / Accepted: 20 May 2021 / Published: 23 May 2021
COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments. View Full-Text
Keywords: COVID; drug discovery; multi-drug therapy; bioprinting COVID; drug discovery; multi-drug therapy; bioprinting
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MDPI and ACS Style

Coban, M.A.; Morrison, J.; Maharjan, S.; Hernandez Medina, D.H.; Li, W.; Zhang, Y.S.; Freeman, W.D.; Radisky, E.S.; Le Roch, K.G.; Weisend, C.M.; Ebihara, H.; Caulfield, T.R. Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement. Biomolecules 2021, 11, 787. https://doi.org/10.3390/biom11060787

AMA Style

Coban MA, Morrison J, Maharjan S, Hernandez Medina DH, Li W, Zhang YS, Freeman WD, Radisky ES, Le Roch KG, Weisend CM, Ebihara H, Caulfield TR. Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement. Biomolecules. 2021; 11(6):787. https://doi.org/10.3390/biom11060787

Chicago/Turabian Style

Coban, Mathew A., Juliet Morrison, Sushila Maharjan, David H. Hernandez Medina, Wanlu Li, Yu S. Zhang, William D. Freeman, Evette S. Radisky, Karine G. Le Roch, Carla M. Weisend, Hideki Ebihara, and Thomas R. Caulfield. 2021. "Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement" Biomolecules 11, no. 6: 787. https://doi.org/10.3390/biom11060787

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