ABSTRACT
The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing. More is expected to come in the near future by using such advanced machine learning techniques to combat this pandemic. This review aims to unravel just a small fraction of the large global endeavors by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found to contain COVID-19 or alleviating its symptoms in the absence of specific medication.
Subject(s)
COVID-19 , Deep Learning , Drug Repositioning , Humans , Pandemics , SARS-CoV-2ABSTRACT
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in the respiratory damage that accompanies SARS-CoV-2 infection. Here, flavones isolated from Scutellaria baicalensis (baicalein, baicalin, wogonin, norwogonin, and oroxylin A) were studied as possible compounds in the treatment of SARS-CoV-2 and SARS-CoV-2-induced acute lung injuries. Methods: We used structural bioinformatics and cheminformatics to (i) identify the critical molecular features of flavones for their binding activity at human and SARS-CoV-2 enzymes;(ii) predict their drug-likeness and leader-likeness features;(iii) calculate their pharmacokinetic profile, with an emphasis on toxicology;(iv) predict their pharmacodynamic profiles, with the identification of their human body targets involved in the respiratory system injuries;and (v) dock the ligands to SARS-CoV-2 3CLpro. Results: All flavones presented appropriate drug-like and kinetics features, except for baicalin. Flavones could bind to SARS-CoV-2 3CLpro at a similar site, but interact slightly differently with the protease. Flavones’pharmacodynamic profiles predict that (i) wogonin strongly binds at the cyclooxygenase2 and nitric oxide synthase;(ii) baicalein and norwogonin could modulate lysine-specific demethylase 4D-like and arachidonate 15-lipoxygenase;and (iii) baicalein, wogonin, norwogonin, and oroxylin A bind to SARS-CoV-2 3CLpro. Conclusions: Our results propose these flavones as possible potent drugs against respiratory damage that occurs during SARS-CoV-2 infections, with a strong recommendation for baicalein.
ABSTRACT
The worldwide infection with the new Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) demands urgently new potent treatment(s). In this study we predict, using molecular docking, the binding affinity of 15 phenothiazines (antihistaminic and antipsychotic drugs) when interacting with the main protease (Mpro) of SARS-CoV-2. Additionally, we tested the binding affinity of photoproducts identified after irradiation of phenothiazines with Nd:YAG laser beam at 266 nm respectively 355 nm. Our results reveal that thioridazine and its identified photoproducts (mesoridazine and sulforidazine) have high biological activity on the virus Mpro. This shows that thioridazine and its two photoproducts might represent new potent medicines to be used for treatment in this outbreak. Such results recommend these medicines for further tests on cell cultures infected with SARS-CoV-2 or animal model. The transition to human subjects of the suggested treatment will be smooth due to the fact that the drugs are already available on the market.