Mepolizumab for COVID-19

Despite all currently available anti-pandemic monoclonal-antibodies (mAbs) and vaccines, subsequently emerging pandemic-infections will likely become more pan-resistant-, -transmissible and/or -lethal. We have created HEDGES generation-2, a significantly more-combinatorial, -synergistic version of our generation-1 HEDGES DNA vector-based platform. We previously published that one safe intravenous injection of a HEDGES generation-1 DNA vector encoding one of three different FDA-approved mAbs produced durable therapeutic serum mAb levels as well as critical therapeutic endpoints in immunocompetent mice. Here we show one safe, intravenous administration of a 2 nd -generation HEDGES DNA vector co-encoding four different anti-SARS-CoV-2 mAbs rapidly then durably co-produces high anti-SARS-CoV-2 mAb serum levels that effectively block SARS-CoV-2 virus binding to the ACE-2 spike protein in immunocompetent mice. In addition, four weekly intravenous HEDGES generation-2 DNA vector administrations co-encoding a total of ten-different anti-SARS-CoV-2 mAbs, 5J8, plus an anti-1918 pandemic influenza mAb and mepolizumab, an FDA-approved anti-IL-5 mAb, durably co-produce highly-neutralizing 5J8 anti-pandemic influenza mAb serum levels, as well as durably block SARS-CoV-2 virus-ACE-2 receptor binding in mice. Furthermore, unlike vaccines and mAbs, HEDGES does not require an intact cold chain and is readily freeze dried, enabling its prolonged storage at ambient temperatures worldwide, even in equatorial regions. Also, HEDGES can create, then deploy novel, more effective anti-pandemic mAbs ~three weeks after their identification. Conversely, vaccines require ~three months to deploy, recombinant-mAbs ~nine months. By rapidly then durably co-producing many different highly-neutralizing, highly-synergistic anti-pandemic mAbs, HEDGES may effectively co-prevent both SARS-CoV-2 and pandemic-influenza infections. HEDGES may also prevent even more-transmissible, -pan-resistant and/or -lethal pandemic diseases that subsequently-emerge.

Background/Objectives: The COVID-19 pandemic highlighted the critical need for drug repurposing. While computational graph-based approaches show promise, single analytical methods often fail to capture the complex pathological mechanisms needed for clinical translation. We aimed to prioritize COVID-19 repurposing candidates by combining computational prediction with orthogonal lines of evidence. Methods: We developed a Graph Attention Autoencoder (GATE) framework with multilayered evaluation integrating computational prediction, real-world data, and phenome analyses. Network-based embeddings identified candidates through latent-space similarity within biomedical knowledge graphs. Clinical relevance was assessed by disproportionality analysis (DPA) of an adverse event reporting system, and biological plausibility through gene expression and pathway profiling. Results: Our GATE framework identified 16 candidate drug entities, including 12 already used or recognized in the context of COVID-19 or its symptoms, consistent with the model recovering known drug–disease associations. The remaining four had minimal prior COVID-19 associations (cilastatin, megestrol, drotrecogin alfa, and ethacrynic acid). Of these four, the multilayered evaluation prioritized two: DPA showed significant inverse reporting associations for COVID-19-related adverse event terms for cilastatin (reporting odds ratio [ROR]: 0.19, 95% confidence interval [CI]: 0.09–0.43) and megestrol (ROR: 0.63, 95% CI: 0.42–0.94). Pathway profiling indicated that cilastatin shares molecular signatures with drugs investigated in COVID-19 clinical trials and megestrol with compounds reported as COVID-19 therapeutic candidates; gene expression analyses suggest cilastatin may exert antiviral and anti-inflammatory effects through DPEP1 inhibition. Conclusions: By integrating GATE-based prediction, real-world evidence, and phenome-level pathway profiling, this study enabled multidimensional candidate prioritization that single analytical methods do not provide. These findings, including the proposed mechanisms of action, are computational predictions that await experimental confirmation. They nonetheless provide mechanistic working hypotheses that may streamline subsequent preclinical studies. Prioritizing clinically approved drugs with established safety profiles may facilitate subsequent clinical evaluation, although the risks specific to each drug would need to be weighed. The framework offers a generalizable strategy for rapid candidate identification during pandemics and diseases with unmet therapeutic needs.