Epinephrine for COVID-19

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for coronavirus disease 2019. It presents one of the most threatening pandemics in the history of humanity. The mortality and morbidity represent an unprecedented challenge to the modern medical era. SARS-CoV-2 results in acute respiratory distress syndrome, high concentrations of proinflammatory mediators, cytokine storm (CS) due to massive release of cytokines, hypercoagulation, and hemoglobin disintegration. Dysregulation of iron homeostasis, iron overload as indicated by high ferritin level, and ferroptosis are major factors in the pathogenesis of the disease. We report a case of SARS-CoV-2 in which the use of epinephrine (Epi) resulted in an unexpected attenuation of CS, decreasing ferritin level and inhibiting ferroptosis. Case presentation A 64-year-old male patient with a history of multiple medical comorbidities had been diagnosed with SARS-CoV-2. Further evaluation showed marked increase in inflammatory markers, severe hyperferritinemia, and lymphopenia in laboratory blood tests. The characteristic score of CS was strongly positive, and in addition to regular treatment, the patient received Epi due to development of acute generalized skin rash, severe itching, and edema of lips and tongue. Epi may have successfully terminated not only the acute cutaneous condition, but also have attenuated CS, decreased ferritin level, and other inflammatory markers in addition to complete patient’s recovery. Conclusion Epinephrine may attenuate CS and inhibit ferroptosis which is an iron-dependent, non-apoptotic mode of cell death. Epi interacts with ferric and/or ferrous iron and built a stable complex that impedes activation of beta-adrenergic receptors. Epi may cause marked decrease of ferritin and other inflammatory markers. Epi may be used to decrease iron overload which is associated with many medical diseases like type 2 diabetes mellitus and cardiometabolic diseases such as coronary heart disease and cerebrovascular disease. As a new clinical indication extensive studies are required for further assessment and possible therapeutic uses.

The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experimentally verified viral–host interaction data (SARS-CoV-2–human interactions published on April 30, 2020) provide an invaluable resource, these datasets include only a limited number of high-confidence interactions. Here, we extend these resources using a deep learning–based multiview graph neural network approach, coupled with optimal transport–based integration. Our comprehensive validation strategy confirms 472 high-confidence predicted interactions between 280 host proteins and 27 SARS-CoV-2 proteins. The proposed model demonstrates robust predictive performance, achieving ROC-AUC scores of 85.9% (PPI network), 83.5% (GO similarity network), and 83.1% (sequence similarity network), with corresponding average precision scores of 86.4%, 82.8%, and 82.3% on independent test sets. Comparative evaluation shows that our multiview approach consistently outperforms conventional single-view and baseline graph learning methods. The model combines features derived from protein sequences, gene ontology terms, and physical interaction information to improve interaction prediction. Furthermore, we systematically map the predicted host factors to FDA-approved drugs and identify several candidates, including lenalidomide and pirfenidone, which have established or emerging roles in COVID-19 therapy. Overall, our framework provides comprehensive and accurate predictions of SARS-CoV-2–host protein interactions and represents a valuable resource for drug repurposing efforts.

AbstractThe recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.