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example which illustrates the urgency, chloroquine a classic antimalarial was initially approved by the FDA, but later withdrawn.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.
Department of Clinical Neuroscience