MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

To identify natural bioactive compounds (NBCs) as potential inhibitors of 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 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 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.

Neuron-specific Biomarkers Associated With Neurological Manifestations In COVID-19: An Evidence Mapping Systematic Review

Introduction. We aimed to map and synthesize the available evidence on neuron-specific biomarkers related to COVID-19. Methods. A systematic review and qualitative evidence mapping synthesis was performed (PROSPERO-CRD42021266995). Searches were conducted in PubMed and Scopus, and complemented by manual search (July 2021). We included observational studies of any design assessing neurological biomarkers in adult patients (>18 years;with or without neurological comorbidities) diagnosed with COVID-19. Methodological quality of nonrandomized studies (case-control, cohorts) was assessed using the Newcastle-Ottawa Scale. Results. Overall, 14 studies (n=485 patients) conducted in Sweden (n=4 articles, 28.5%), Germany (n=3;21.4%), USA (n=3;21.4%), Canada, France, Italy and Norway (n=1 study each) were included. The most reported neurological symptoms (n=13 studies, 92.8%) were headache, confusion, general weakness, loss of smell/taste, cognitive impairments and behavioral changes. Prevalent neurological conditions included encephalopathies, neuropathies, myopathies, and delirium;most critical cases presented cerebrovascular events (n=4 studies, 28.5%). Hypertension, diabetes, obesity, dyslipidemia, and chronic lung disease were the most reported comorbidities. Eight different neuron-specific biomarkers were found in primary studies: neurofilament-light chain - NfL (n=10 studies;71.4%), glial fibrillary acidic-protein - GFAp (n=5;35.7%), tau protein (n=5;35.7%), neurofilament-heavy chain - NfH, S100B protein, ubiquitin C-terminal hydrolase L1 - UCH-L1, neuronspecific enolase and beta protein-amyloid - Abeta (n=1 study each). These biomarkers were found both in cerebrospinal fluid and blood/ plasma samples even without an evident cytokine storm. In patients with COVID-19, NfL and GFAp can act as sensitive indicators of neuroaxonal and astrocytic damages, respectively. Increased levels of NfL were significantly associated with severe COVID-19, unconsciousness and longer stay in the intensive care unit (p<0.05). Studies had an overall poor to moderate methodological quality. Conclusions. We identified eight neuron-specific biomarkers that should be further studied as prognostic factors of COVID-19. These findings can also guide the development of targeted therapies against SARS-CoV-2. Additional well-designed clinical trials are needed to strengthen this evidence and help understand the mechanisms of neurological symptoms and sequelae after COVID-19 infection.

Development And Validation Of A Machine Learning- Based Prediction Model For COVID-19 Diagnosis Using Patients' Metabolomic Profile Data

Introduction. We aimed to develop and validate machine learning (ML) -based algorithms to predict COVID-19 diagnosis as well as to identify new biomarkers associated with the disease. Methods. Initially, 96 blood samples of patients diagnosed with COVID-19 (Thaizhou Hospital, China) were analyzed through liquid chromatography coupled to mass spectrometry. Samples of patients presenting other pneumonias or severe acute respiratory syndrome, but with negative RT-PCR for SARS-CoV-2, were used as positive controls. Samples from healthy volunteers were used as negative controls. The final database included around 1000 metabolites. Exploratory analyses for the development of ML-based models using principal component analysis (PCA) were performed. Leverage plot versus studentized residuals method was used to detect outliers. Three supervised ML-based models were developed: discriminant analysis by partial least squares (PLS-DA), artificial neural networks discriminant analysis (ANNDA) and k-nearest neighbors (KNN). Samples for the training (70%) and testing sets (30%) were randomly selected using the Kenrad Stone algorithm. Models' performance was evaluated considering accuracy, sensitivity and specificity. Analyses were conducted in SOLO (Eigenvector-Research). Results. The PCA model was able to distinguish the three classes of patients' samples (positive for COVID-19, negative controls, positive controls) with an overall accumulated variance of 94.27 percent. The PLS-DA model presented the best performance (accuracy, sensitivity, and specificity of 93%, 98% and 88%, respectively). Increased levels of the biomarkers uridine (linked to glucose homeostasis, lipid, and amino acid metabolisms), 4-hydroxyphenylacetoylcarnitine (metabolite from the tyrosine metabolism;probably associated with anorexia) and ribothymidine (resulting from oral and fecal microbiota alterations) were significantly associated with COVID-19. Conclusions. Three different and updated ML-based algorithms were developed to predict COVID-19 diagnosis;PLS-DA led to the most accurate results. High levels of some metabolites were found as potentially predictors of the disease. These biomarkers should be further evaluated as potential therapeutic targets in well-designed clinical trials. These ML-based models can help the early diagnosis of COVID-19 and guide the development of tailored interventions.

POSB422 Machine Learning-Based Virtual Screening, Molecular Docking and Drug-Likeness to Discover New Inhibitors of the Glycoprotein Spike (S1) of SARS-CoV-2

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.

Evaluation of the epidemiological aspects and the impact of covid-19 on tuberculosis notifications in são pauloa

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.