Dupilumab 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.

Dupilumab 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 dupilumab in detail.

Studies suggesting benefit with dupilumab

Choi et al., Review of COVID-19 Therapeutics by Mechanism: From Discovery to Approval, Journal of Korean Medical Science, doi:10.3346/jkms.2024.39.e134

Loucera et al., Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection, Signal Transduction and Targeted Therapy, doi:10.1038/s41392-020-00417-y

Wang et al., Identification of targets for drug repurposing to treat COVID-19 using a Deep Learning Neural Network, medRxiv, doi:10.1101/2023.05.23.23290403

The COVID-19 pandemic has resulted in a global public health crisis requiring immediate acute therapeutic solutions. To address this challenge, we developed a useful tool deep learning model using the graph-embedding convolution network (GECN) algorithm. Our approach identified COVID-19-related genes and potential druggable targets, including tyrosine kinase ABL1/2, pro-inflammatory cytokine CSF2, and pro-fibrotic cytokines IL-4 and IL-13. These target genes are implicated in critical processes related to COVID-19 pathogenesis, including endosomal membrane fusion, cytokine storm, and tissue fibrosis. Our analysis revealed that ABL kinase inhibitors, lenzilumab (anti-CSF2), and dupilumab (anti-IL4Rα) represent promising therapeutic solutions that can effectively block virus-host membrane fusion or attenuate hyperinflammation in COVID-19 patients. Compared to the traditional drug screening process, our GECN algorithm enables rapid analysis of disease-related human protein interaction networks and prediction of candidate drug targets from a large-scale knowledge graph in a cost-effective and efficient manner. Overall, Overall, our results suggest that the model has the potential to facilitate drug repurposing and aid in the fight against COVID-19.