Accelerated artificial intelligence strategy for drug repositioning against COVID-19

  • Funded by Fundação de Amparo à Pesquisa do Estado de São Paulo [São Paulo Research Foundation] (FAPESP)
  • Total publications:5 publications

Grant number: 2020/05369-6

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2022
  • Known Financial Commitments (USD)

    $32,846.07
  • Funder

    Fundação de Amparo à Pesquisa do Estado de São Paulo [São Paulo Research Foundation] (FAPESP)
  • Principle Investigator

    Pending
  • Research Location

    Brazil, Americas
  • Lead Research Institution

    Universidade Estadual de Campinas
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Disease pathogenesis

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

The arrival of COVID-19 in Brazil in March 2020, with its rapid spread, exponential growth of cases worldwide and the high lethality of infected patients, has generated panic in the population, disrupted public and private health systems and impacted negative way in the economy of Brazil and the world. Although the search for an effective vaccine is the first arrival of COVID-19 in Brazil in March 2020, with its rapid spread, exponential growth of cases worldwide and the high lethality of infected patients, it has generated panic in the population, disrupted systems public and private health and negatively impacted the economy of Brazil and the world. Although the search for an effective vaccine is urgent, its development should take at least a year and a half before approval. Thus, the need to discover a treatment that can be quickly approved for use in infected patients becomes evident. Drug repositioning offers a potentially faster approach to identifying drugs already approved for use in humans. Artificial intelligence (AI) is a frontier area of ​​knowledge that allows the identification of potentially active compounds with appropriate pharmacokinetic and toxicological properties, leading to greater speed, greater success rate and lower cost in the discovery of new drugs. In this context, in order to transform drug discovery from a slow, sequential and high-risk process to a fast, integrated model with reduced risk of failure, this project aims to develop an integrated platform based on artificial intelligence for accelerate the repositioning of drugs already approved for use in humans for the treatment of COVID-19 with the potential for rapid clinical development, through the integration of high performance computing with chemical and biological data and the use of emerging biotechnological and experimental technologies. development should require at least a year and a half before approval. Thus, the need to discover a treatment that can be quickly approved for use in infected patients becomes evident. For this, we add different expertise from researchers at the Biology Institute of UNICAMP and other research institutions in Brazil and abroad and we will use a multidisciplinary approach that will involve the development and application of artificial intelligence tools to guide and accelerate the repositioning of drugs in use based on inhibiting virus entry into the host cell by inhibiting the interaction between viral Spike proteins and human ACE-2 and testing antiviral activity in cell culture in a level 3 biological containment laboratory. As preliminary data, in a accepted for publication, researchers involved in this proposal developed computational models and performed a virtual screening based on docking of all drugs approved by the FDA (~ 2,400 drugs) in viral glycoprotein spike complexed with human protein ACE2, with the grid centered on the interface of the two proteins. After docking, drugs were reordered using machine learning models developed using the Bayesian algorithm and ECFP6 fingerprint descriptors for SARS-CoV phenotypic data. At the end of this approach, 25 approved drugs were selected and will be subjected to in vitro tests against SARS-CoV-2 in Vero cells.

Publicationslinked via Europe PMC

Last Updated:37 minutes ago

View all publications at Europe PMC

Unveiling the Antiviral Capabilities of Targeting Human Dihydroorotate Dehydrogenase against SARS-CoV-2.

Computational and Experimental Approaches Identify Beta-Blockers as Potential SARS-CoV-2 Spike Inhibitors.

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

Methylprednisolone as Adjunctive Therapy for Patients Hospitalized With Coronavirus Disease 2019 (COVID-19; Metcovid): A Randomized, Double-blind, Phase IIb, Placebo-controlled Trial.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.