Omalizumab for COVID-19

Abstract Background Omalizumab is an anti-IgE monoclonal antibody used to treat moderate to severe chronic idiopathic urticaria, asthma and nasal polyps. Recent research suggested that omalizumab may enhance the innate antiviral response and have anti-inflammatory properties. Objective We aimed to investigate the efficacy and safety of omalizumab in adults hospitalized for COVID-19 pneumonia. Methods This was a phase II randomized, double blind, placebo-controlled trial comparing omalizumab versus placebo (in addition to standard of care) in hospitalized COVID-19 patients. The primary endpoint was the composite of mechanical ventilation and/or death at day 14. Secondary endpoints included all-cause mortality at day 28, time to clinical improvement, and duration of hospitalization. Results Of 41 patients recruited, 40 were randomized (20 received the study drug and 20 placebo). The median age of the patients was 74 years and 55.0% were male. Omalizumab was associated with a 92.6% posterior probability of a reduction in mechanical ventilation and death on day 14 with an adjusted odds ratio (aOR) of 0.11 (95% Credible Interval (CrI) 0.002-2.05). Omalizumab was also associated with a 79.4% posterior probability of reduced all-cause mortality on day 28 with an aOR of 0.45 (95% CrI 0.06-3.12). No statistically significant differences were found for the time to clinical improvement and duration of hospitalization. Numerically fewer adverse events were reported in the omalizumab group and there were no drug-related serious adverse events. Conclusion These results suggest that omalizumab could prove protective against death and mechanical ventilation in hospitalized COVID-19 patients. This study could also support the development of a phase III trial program investigating the antiviral and anti-inflammatory effect of omalizumab for severe respiratory viral illnesses requiring hospital admission.

<title>Abstract</title> <p>Background Coronavirus disease 2019 (COVID-19) pandemic has led to numerous cases of acute respiratory distress syndrome (ARDS), with a significant number of survivors developing pulmonary fibrosis as a chronic sequela. This condition poses severe long-term health challenges, significantly burdening public health systems. Despite significant research on the acute phase of COVID-19, the mechanisms underlying pulmonary fibrosis following COVID-19 associated ARDS remain poorly understood, and effective therapies are yet to be established. This study aims to elucidate the molecular mechanisms, identify potential biomarkers, and explore therapeutic options for pulmonary fibrosis post-COVID-19-related ARDS through comprehensive transcriptomic and bioinformatic analyses. Methods We collected datasets from Gene Expression Omnibus (GEO) database, including transcriptional profiles of COVID-19, ARDS, and pulmonary fibrosis. Differentially expressed genes (DEGs) common to these conditions were identified, reflecting the transcriptional landscape of pulmonary fibrosis post-COVID-19 ARDS. Functional and pathway enrichment analyses was conducted. Protein-protein interaction (PPI) network was constructed to determine the hub genes and their regulatory networks. Drugs that interact with hub genes were explored and gene-disease associations were analyzed to identify potential therapeutic strategies. Results We identified 116 common DEGs among COVID-19, ARDS, and pulmonary fibrosis datasets. Functional enrichment highlighted critical processes including inflammatory response, apoptosis, transcription regulation, and MAPK cascade. PPI network revealed hub genes which may play crucial roles in the pathogenesis of pulmonary fibrosis post-COVID-19-related ARDS. Notably, FCER1A, associated with immune response and inflammation, GATA2, involved in macrophage function and erythropoiesis, and CLC, indicative of eosinophil activity, emerged as central players. Regulatory network analysis highlighted significant transcription factors (TFs) and microRNAs (miRNAs) associated with hub genes. We found FDA-approved drugs that could interact with these hub genes, including omalizumab, mizolastine, desloratadine, epoetin alfa, and moxidectin. Gene-disease interaction analysis revealed that diseases caused by GATA2 deficiency and immunodeficiency were associated with hub genes. Conclusion Our findings provide valuable insights into the molecular underpinnings of pulmonary fibrosis post-COVID-19 ARDS and highlight potential biomarkers and therapeutic targets. The repurpose of drugs offers a promising avenue for rapid clinical application, potentially improving outcomes. This study provides ideas for improved treatment for pulmonary fibrosis post-COVID-19 ARDS.</p>

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi {\sl et al.} recently developed the \drcov \ model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the \drcov \ model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware --- we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.