Coronavirus (COVID-19) is an enveloped RNA virus that is diversely found in humans and that has now been declared a global pandemic by the World Health Organization. Thus, there is an urgent need to develop effective therapies and vaccines against this disease. In this context, this study aimed to evaluate in silico the molecular interactions of drugs with therapeutic indications for treatment of COVID-19 (Azithromycin, Baricitinib and Hydroxychloroquine) and drugs with similar structures (Chloroquine, Quinacrine and Ruxolitinib) in docking models from the SARS-CoV-2 main protease (M-pro) protein. The results showed that all inhibitors bound to the same enzyme site, more specifically in domain III of the SARS-CoV-2 main protease. Therefore, this study allows proposing the use of baricitinib and quinacrine, in combination with azithromycin; however, these computer simulations are just an initial step for conceiving new projects for the development of antiviral molecules.

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It is of great importance for the pharmaceutical industry to find therapeutic substances extracted from natural sources, which are abundant, obtained with low costs and presenting the antiviral potential for the treatment of Zika virus (ZIKV) and COVID-19. Tangeretin (TAN) is a citrus polymethoxyflavone from Citrus reticulata peel oil with known antiviral activities, whose physico-chemical properties are not reported. The present study aimed to investigate by a theoretical screening of electronic, structural properties and pharmacodynamic and pharmacokinetic parameters that characterize TAN as a therapeutic drug in the treatment and prevention of zika fever and COVID-19. The molecule reached its minimum energy-forming state of [Formula: see text]795.85747[Formula: see text]kJ/mol and the HOMO and LUMO boundary orbitals reactivity descriptors suggest that the compound is stable and does not tend to be reactive in intermolecular interactions. The ligand connects to the NS1 ZIKV receptor with strong H-bond interactions, also connects with the NS5 ZIKV receptor in a competitive effect with the SAM inhibitor and acts in a supplementary effect with the N3 inhibitor and the BRT drug in the Mpro SARS-CoV-2 receptor. The properties of ADMET shows that the compound suffers few amounts of drug alterations because it inhibits the metabolic enzymes CYP2C9 and CYP3A4 and penetrates the central nervous system, without accumulation of drug residues in the blood or in the lumen in the gastrointestinal tract, without risk of toxicity to the patient. With the results obtained, it is possible to identify TAN as a promising pharmacological tool for the treatment and prevention of Zika fever and COVID-19.

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<abstract><p>About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ &gt; million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.</p></abstract>