Browsing by Author "Stremel, Dile"
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- Machine learning-based virtual screening, molecular docking, drug-likeness, pharmacokinetics and toxicity analyses to identify new natural inhibitors of the glycoprotein spike (S1) od SARS-CoV-2Publication . Cobre, Alexandre; Böger, Beatriz; Fachi, Mariana; Ehrenfried, Carlos; Stremel, Dile; De Melo, Eduardo; Tonin, Fernanda; Pontarolo, RobertoTo identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed a greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15, and NBC27 had better results in ADMET predictions. These had a similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.
- Use of biochemical tests and machine learning in the search for potential diagnostic biomarkers of COVID-19, HIV/AIDS, and pulmonary tuberculosisPublication . Cobre, Alexandre; Morais, Amiel; Selege, Fosfato; Stremel, Dile; Wiens, Astrid; Ferreira, Luana; Tonin, Fernanda; Pontarolo, RobertoThis study aims to develop, validate, and evaluate machine learning algorithms for predicting the diagnosis of coronavirus disease (COVID-19), human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), pulmonary tuberculosis (TB), and HIV/TB co-infection. We also investigated potential biomarkers associated with the diagnosis. Data from biochemical and hematological tests of infected and controls were collected in a single general hospital, totalizing 6,418 patients. The discriminant analysis by partial least squares (PLS-DA) model had the highest performance in predicting the diagnosis of COVID-19, HIV/AIDS, TB, and HIV/TB co-infection with an accuracy of 94, 97, 95, and 96%, respectively. The biomarkers calcium, lactate dehydrogenase, red blood cells (RBC), white blood cells, neutrophils, basophils, eosinophils, hemoglobin, and hematocrit were associated with COVID-19. HIV infection was associated with mean corpuscular volume, platelets, neutrophils, and mean platelet volume. Red blood cell distribution width and urea were associated with infection by Mycobacterium tuberculosis. The following biomarkers were associated with HIV/TB co-infection: lymphocytes, RBC, hematocrit, hemoglobin, aspartate transaminase, alanine transaminase, and glycemia. The PLS-DA model can optimize COVID-19, HIV/AIDS, TB, and HIV/TB co-infection diagnostics. Some biomarkers were potential diagnostic indicators and could be evaluated during the screening of these diseases.