AIN1-43_AINNL0043 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.
-
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.
-
Immune support Effective Vitamins A, C, D, and zinc show reduced risk, as with other viruses.
-
Thermotherapy Effective Methods for increasing internal body temperature, enhancing immune system function.
-
Systemic agents Effective Many systemic agents reduce risk, and may be required when infection progresses.
-
High-profit systemic agents Conditional Effective, but with greater access and cost barriers.
-
Monoclonal antibodies Limited Utility Effective but rarely used—high cost, variant dependence, IV/SC admin.
-
Acetaminophen Harmful Increased risk of severe outcomes and mortality.
-
Remdesivir Harmful Increased mortality with longer followup. Increased kidney and liver injury, cardiac disorders.
AIN1-43_AINNL0043 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 AIN1-43_AINNL0043 in detail.
, AI designed, mutation resistant broad neutralizing antibodies against multiple SARS-CoV-2 strains, Scientific Reports, doi:10.1038/s41598-025-98979-w
Abstract In this study, we developed a digital twin for SARS-CoV-2 by integrating diverse data and metadata with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled us to computationally design neutralizing antibodies against over 1300 historical strains of SARS-CoV-2, encompassing 64 mutations in the receptor binding domain (RBD) region. 70 AI-designed antibodies were experimentally validated through binding assay and real viral neutralization assays against various strains, including later Omicron strains do not present in the initial design database. 14% of these antibodies exhibited strong reactivity against the RBD of multiple strains, achieving triple cross-binding hit rates using ELISA assay. 10 antibodies neutralized the cytopathic effects (CPE) of the Delta strain at IC50 values of < 10 µg/ml, and one antibody neutralized the CPE of Omicron. These findings demonstrate the potential of our approach to influence future therapeutic design for existing virus strains and predict hidden patterns in viral evolution that AI can leverage to develop emerging antiviral treatments.