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Using big data to find promising drugs for COVID-19

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • Principal Investigator

    Dr. Derek MacFadden

  • Research Location

    Canada

  • Lead Research Institution

    N/A

  • Research Priority Alignment

    N/A

  • Research Category

    Therapeutics research, development and implementation

  • Research Subcategory

    Prophylactic use of treatments

  • Special Interest Tags

    N/A

  • Study Type

    Unspecified

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Dr. Derek MacFadden and his colleagues plan to identify promising drugs to treat COVID-19 by analyzing past data from 3,000 Ontario patients treated for other kinds of coronavirus infections between 2014 and 2018. Once the team identifies which drugs are associated with the best patient outcomes, they will use the same process to see how effective those drugs have been at treating patients with COVID-19. The drugs they identify in this screening process would then be tested in a lab to confirm their anti-viral activity against COVID-19. Drugs that pass this stage could potentially be used in future clinical trials for patients infected with or at risk of contracting COVID-19. Unlike most lab-based drug screening approaches, this big data approach has the benefit of seeing how drugs work in humans infected with the virus, and what dose is needed to be effective.