Computational repurposing of existing drugs for treatment of COVID-19

    $55.00

    The book contains eight chapters. First, we have a chapter describing all non-structural proteins of SARS‑Cov2 and their features that make them possible targets of medications. Then we put the chapter devoted to elucidation of possible inhibitors of ACE2 and Spike Protein of SARS-Cov2. This is the virus entrance route, and its inhibition may play a significant role in prevention or mitigation of the virus penetration to the organism. Following are the chapters devoted to repurposing the drugs for inhibiting of several NSPs: NSP3—papain-like protease PLpro, NSP15— endoribonuclease, and NSP5—main protease Mpro. After that a reader find chapters describing more complex studies devoted to finding of possible simultaneous inhibitors of most of the NSPs, concentrating to the possibility to elucidate the drugs that target more than one NSP. This study was done using information of drug-target interaction (DTI) and predicted several drugs that can simultaneously inhibit several NSPs. Two last chapters are devoted to treatment of cytokine storm, concentrating on possible simultaneous impact on several cytokines-related pathways.

    All the studies were conducted using various machine-learning tools and give a lot of details of the approach that make it possible to use these methods by other scientists and students.

    3 in stock

    Computational repurposing of existing drugs for treatment of COVID-19

    $55.00

      The outbreak of the COVID-19 infection induced by the SARS-CoV-2 virus has spread over the world in a pandemic with over 6 million deaths and over 700 million infected. In the state of emergency, several drug companies explored existing drugs including antivirals, but it was via RNA-based vaccines that the pandemic was contained. As one example which illustrates the urgency, chloroquine a classic antimalarial was initially approved by the FDA, but later withdrawn.

      This series of papers show the potential of using machine learning to define in the SARS-CoV-2 virus potentially specific sites of drug action, including virus entry (ACE2 inhibition) or the virus spike proteins as well as proteases and non-structural proteins. The final papers address the cytokine stress triggered by the infection in so-called long COVID-19, conditions involving lung, gastrointestinal tract, or the CNS. The mechanistic basis of extended COVID-19 is not fully understood.

      The study is extensive, building on a large data base of FDA-approved drugs and illustrates the potential of the approach. Several of the candidates are known as antivirals, particularly active on HIV or hepatitis C. Others are known as anticancer agents, such as Mitomycin C and toxicity may be prohibitive. In the selection process, lead compounds are candidates for further refinement and studies of equipoise investigation is possible.

      Lars Terenius

      Department of Clinical Neuroscience
      Karolinska Institute
      Stockholm, Sweden

       

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