ABSTRACT
The recent epidemic of COVID-19 caused by SARS-CoV-2 was declared by the World Health Organization as a public health emergency of international concern. The absence of an approved vaccine or a specific antiviral drug has made bioinformatic tools crucial for the identification of potential therapeutic targets and drugs for its control. As in other RNA viruses, the protease 3C-like and the RNA-polymerase are two of the SARS-CoV-2 targets to test drugs that can be analyzed in silico. In the present study, compounds derived from plants, fungi, and nucleoside 5'-triphosphate or uridine nucleotide analogs, with anti-DENV activity in vitro or in vivo, were analyzed by molecular docking as potential anti-SARS-CoV-2 drugs. Anthraquinone, with a DENV NS3 protease inhibitory activity; Balapiravir, Fisetin, Hyperoside, and Sofosbuvir, with a DENV NS5 RNA-polymerase inhibitory activity; and Quercetin, with both anti-NS3-NS5 activities, were tested against 3C-like protease and RNA-polymerase of SARS-CoV-2. All these drugs demonstrated a high affinity for the corresponding SARS-CoV-2 proteins, representing excellent candidates for the treatment of COVID-19. Therefore, in vitro or in vivo studies should be carried out using these compounds on models for SARS-CoV-2 infection.
Subject(s)
COVID-19ABSTRACT
Background: COVID-19 has been recognized as an emerging and rapidly evolving health condition. For this reason, efforts to determine changes in laboratory parameters of COVID-19 patients as biomarkers are urgent. Lipids are essential components of the human body, and their modulation has been observed implicated in some viral infections. Methods: To evaluate the clinical diagnosis utility of the lipid profile changes in Mexican COVID-19 patients, the lipid profile of one hundred two COVID-19 positive patients from three hospitals in Culiacan, Sinaloa in northwest Mexico, was analyzed. ROC curves and binary logistic regression analysis were used as a predictive model to determine their clinical diagnostic utility. Results: Significant changes in the serum lipid profile of patients with COVID-19, such as low levels of cholesterol, LDL, and HDL, while high triglycerides and VLDL were observed. The same abnormalities in the lipid profile among non-critical and critical COVID-19 patients were detected. The predictive model analysis suggests that cholesterol and LDL have AUC values of 0.710 and 0.769, respectively, for COVID-19 (p= 0.0002 and p= <0.0001), and LDL low levels might be a risk factor for critical COVID-19 (OR= 2.07, 95% IC: 1.18 to 3.63; p= 0.01). Conclusion: Our findings suggest that low cholesterol and LDL levels could be considered an acceptable predictor for COVID-19, and low levels of LDL might be a risk factor for critical COVID-19 patients.