Neq1148 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.
Neq1148 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 Neq1148 in detail.
, Machine Learning‐Guided Repositioning of a SARS‐CoV‐2‐Targeting Molecular Series as Cruzain Inhibitors, ChemMedChem, doi:10.1002/cmdc.202500630
Drug repurposing and repositioning are concepts that involve identifying alternative therapeutic uses for existing drug candidates or molecular series. During the COVID‐19 pandemic, hundreds of antivirals were developed, many of which remain unexplored for other diseases. Concurrently, machine learning (ML) has become a valuable tool in early drug discovery for screening the most promising compounds for a target. In this work, an ExtraTrees ML model is developed to predict inhibitory activity against cruzain, the main cysteine protease of Trypanosoma cruzi , the causative agent of Chagas disease. The model is used to screen a proprietary library of peptidomimetic compounds originally designed to target SARS‐CoV‐2 M pro and human cathepsin L. High‐affinity cruzain inhibitors are identified, some containing P1 moieties not previously reported in cruzain inhibitors, expanding the known chemical space for this target. Selected hits are validated using isothermal titration calorimetry and some compounds display more favorable enthalpic and entropic contributions to binding than similar peptidomimetic nitrile‐based inhibitors. Notably, this is achieved without highly lipophilic R‐groups, preserving drug‐like properties. This work also highlights how compound libraries derived from global health efforts can be effectively repurposed for neglected tropical diseases with ML models.