Enasidenib 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.
Enasidenib may be beneficial for
COVID-19 according to the studies 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 enasidenib in detail.
, An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease, Molecules, doi:10.3390/molecules30163409
Despite the widespread use of vaccines against SARS-CoV-2, COVID-19 continues to pose global health challenges, requiring efficient drug screening and repurposing strategies. This study presents a novel hybrid framework that integrates deep learning (DL) with molecular docking to accelerate the identification of potential therapeutics. The framework comprises three crucial steps: (1) a previously developed DL model is employed to rapidly screen candidate compounds, selecting those with predicted interaction scores above a cut-off value of 0.8; (2) AutoDock Vina version 1.5.6 and LeDock version 1.0 are used to evaluate binding affinities, with a threshold of <−7.0 kcal·mol−1; and (3) predicted drug–protein binding sites are evaluated to determine their overlap with known active residues of the target protein. We first validated the framework using four experimentally confirmed COVID-19 drug–target pairs and then applied it to identify potential inhibitors of the SARS-CoV-2 main protease (MPro). Among 29 drug candidates selected based on antiviral, anti-inflammatory, or anti-cancer properties, only Enasidenib met all three selection criteria, showing promise as an MPro inhibitor. However, further experimental and clinical studies are required to confirm its efficacy against SARS-CoV-2. This work provides an interpretable strategy for virtual screening and drug repurposing, which can be readily adapted to other DL models and docking tools.