41-O-demethyl rapamycin 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.
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Only three high-profit early treatments are approved in the US.
In reality, many treatments reduce risk,
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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.
41-O-demethyl rapamycin 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 41-O-demethyl rapamycin in detail.
, In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19, PLOS One, doi:10.1371/journal.pone.0325707
The global pandemic, due to the emergence of COVID-19, has created a public health crisis. It has a huge morbidity rate that was never comprehended in the recent decades. Despite numerous efforts, potent antiviral drugs are lacking. Repurposing of drugs presents a low-cost and rapid solution for finding new drugs by exploiting known drugs. In this study, we employed an integrated In-Silico approach using molecular docking and machine learning regression models to explore the potential inhibitors against key proteins of SARS-CoV-2. A library of 5903 drugs from the ZINC database was retrieved and screened against three crucial viral targets: Spike glycoprotein (7LM9), main protease 3CLpro (7JSU), and Nucleocapsid protein (7DE1). Binding affinities were predicted by using molecular docking, and subsequent predictive regression models, Decision Tree Regression (DTR), Gradient Boosting, XGBoost, Extra Trees, KNNR, and MLP, were constructed employing MACCS molecular fingerprints. Among them, the DTR model had better predictive performance, as indicated by the highest R² and lowest RMSE. The highest ranked compounds possessed good binding affinities (−12.6 to −19.7 kcal/mol) and favorable pharmacokinetics. Importantly, five novel candidate compounds, namely ZINC003873365, ZINC085432544, ZINC008214470, ZINC085536956, and ZINC261494640, had multi-target potential and optimal binding interaction. This computational analysis yields useful information for lead prioritization and sets the stage for additional in vitro and in vivo confirmation of these drug candidates to combat COVID-19.