ZINC000003022621 for COVID-19
c19early.org
COVID-19 Treatment Clinical Evidence
COVID-19 involves the interplay of 400+ viral and host proteins and factors, providing many therapeutic targets.
c19early analyzes 6,000+ studies for 210+ treatments—over 17 million hours of research.
Only three high-profit early treatments are approved in the US.
In reality, many treatments reduce risk,
with 25 low-cost treatments approved across 163 countries.
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Naso/
oropharyngeal treatment Effective Treatment directly to the primary source of initial infection. -
Healthy lifestyles Protective Exercise, sunlight, a healthy diet, and good sleep all reduce risk.
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Immune support Effective Vitamins A, C, D, and zinc show reduced risk, as with other viruses.
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Thermotherapy Effective Methods for increasing internal body temperature, enhancing immune system function.
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Systemic agents Effective Many systemic agents reduce risk, and may be required when infection progresses.
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High-profit systemic agents Conditional Effective, but with greater access and cost barriers.
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Monoclonal antibodies Limited Utility Effective but rarely used—high cost, variant dependence, IV/SC admin.
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Acetaminophen Harmful Increased risk of severe outcomes and mortality.
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Remdesivir Harmful Increased mortality with longer followup. Increased kidney and liver injury, cardiac disorders.
ZINC000003022621 may be beneficial for
COVID-19 according to the study below.
COVID-19 involves the interplay of 400+ viral and host proteins and factors providing many therapeutic targets.
Scientists have proposed 11,000+ potential treatments.
c19early.org analyzes
210+ treatments.
We have not reviewed ZINC000003022621 in detail.
, 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, Química Nova, doi:10.21577/0100-4042.20230038
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.