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

  • Author(s): Coban, Mathew A
  • Morrison, Juliet
  • Maharjan, Sushila
  • Hernandez Medina, David Hyram
  • Li, Wanlu
  • Zhang, Yu Shrike
  • Freeman, William D
  • Radisky, Evette S
  • Le Roch, Karine G
  • Weisend, Carla M
  • Ebihara, Hideki
  • Caulfield, Thomas R
  • et al.
Abstract

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.

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