ABSTRACT
Objectives: Despite great advancements in COVID-19 immunization, the development of therapeutic interventions is urgent to control the ongoing pandemic, especially infected patients. The spike protein (S1) of SARS-Cov-2 virus plays a major role in attachment to the host and further series of events. We aimed to identify natural bioactive compounds (NBC) that act as potential inhibitors of S1 by means of in silico assays. Methods: NBCs with proved biological in vitro activities were obtained from the ZINC database (https://zinc.docking.org) and analyzed through virtual screening and molecular docking to identify those with higher affinity to the S1. Machine learning models of principal component analysis (PCA), artificial neural networks (ANN), discriminant analysis by partial least squares (PLS-DA) and decision tree (DT) were used to validate the Results: Selected NBCs were submitted to drug-likeness analysis using the Lipinsk and Vebber's five rule. The prediction of pharmacokinetic parameters (i.e. absorption, metabolism, distribution, elimination) and toxicity (e.g. hepatotoxicity, cardiotoxicity, carcinogenicity, immunotoxicity) were performed (ADMET). The influence of the NBC’s stereoisomeric, tautomeric and protonation states at physiological pH on the pharmacodynamics, pharmacokinetics and toxicity analyses were also evaluated. Results: A total of 170,906 compounds were analyzed. Of these, only 36 showed greater affinity with the S1 (affinity energy <0.8 kcal/mol). The PCA and PLS-DA models were able to reproduce the results of the virtual screening and docking analyzes with an accuracy of 97.5%. Of these 36 CNBs, only 12 (33.33%) were drug-likeness. The ADMET analysis showed that the natural compound phaselol (7-[[(1R,4aS,6R,8aR)-6-hydroxy-2,5,5,8a-tetramethyl-1,4,4a,6,7,8-hexahydronaphthalen-1-yl]methoxy]chromen-2-one) was the most promising in inhibiting the SARS-COV-2 spike. Conclusions: Machine learning-based research is efficient for retrieving novel approaches to diseases’ treatment. We identified 12 compounds as possible inhibitors of S1;phaselol was the most promising candidate for treating COVID-19. In vitro, preclinical studies and clinical trials are now needed to confirm these findings.
ABSTRACT
Objective: This study aimed to analyze the incidence and epidemiological profile of tuberculosis (TB) cases registered in a region of the State of São Paulo (SP) and to assess the impact of COVID-19 on TB incidence and completeness of notifications. Methods: This is a retrospective cross-sectional study analyzing reports of adult patients with TB, who were notified in the TB-Web from January 2010 to December 2020. Sociodemographic (e.g. sex, race and scholarity) and clinical variables (e.g., clinical form, types of cases and comorbidities) were collected and analyzed. The completeness of TB notifications and the impact of COVID-19 on TB notifications were evaluated, considering the year of 2020. The study was reported following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for cross-sectional studies [CAAE 33166620.0.0000.0102]. Results: A total of 1,509 notifications were included, with a mean incidence of 48.5/100,000 inhabitants. The median age was 42 years, most notification included males (71%), were of white race (42%) and had the pulmonary form of TB (85%). In assessing the impact of the pandemic on notifications in 2020, there was a decrease of 36% in the number of TB notifications, with an emphasis between July and August, which was the peak period of COVID-19 cases in the region. No change in the completeness of TB notifications was observed in this period. Conclusions: Results indicate the clinical and epidemiological profile in a region of SP between 2010 and 2020. The pandemic led to a decrease in the number of TB notifications but did not change the completeness of notifications. © Pontes et al.