c19early.org
COVID-19 Treatment Clinical Evidence
COVID-19 involves the interplay of 500+ viral and host proteins and factors, providing many therapeutic targets.
c19early analyzes 6,000+ studies for 220+ 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 26 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.
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Design Trade-Offs
between Target Binding and Self-Association
Revealed by
De Novo
SARS-CoV-2 Binders,
Journal of Chemical Information
and Modeling,
doi:10.1021/acs.jcim.6c02394
Abstract De novo protein binders have emerged as a powerful platform for molecular recognition, yet achieving high affinity while maintaining favorable biophysical properties remains a major challenge. Here, using the SARS-CoV-2 receptor-binding domain (RBD) as a model target, we generated two de novo protein binder design campaigns that deliberately imposed different trade-offs between binding and monomeric behavior, and benchmarked them against a set of experimentally validated binders. Through integrated experimental and computational analyses, we show that binder design is constrained by two distinct failure modes that require different optimization strategies. None of the 22 original designs showed measurable binding to the RBD, with one class remaining monomeric but failing to achieve sufficient target binding, while the other had more competent binding interfaces but exhibited excessive surface hydrophobicity that promoted self-association. By integrating AlphaFold-based predictions, molecular dynamics simulations, docking analyses, and surface aggregation propensity (SAP) calculations, we establish complementary computational signatures that distinguish these failure modes and reveal their underlying structural determinants. Guided by these insights, we prospectively redesigned a representative failed binder by reducing noninterface hydrophobicity while preserving folding and binding interface quality. Experimental characterization by SEC-MALS, surface plasmon resonance, and circular dichroism confirmed that the redesigned variants were stable monomers and acquired low μM binding affinities. Together, our results define the mechanistic trade-offs between binding and self-association in de novo protein binder design and provide a framework for diagnosing and rationally overcoming common design failure modes.