Chemocentric Informatics Analysis: Dexamethasone Versus Combination Therapy for COVID-19.

ABSTRACT

COVID-19 is a biphasic infectious disease with no approved vaccine or pharmacotherapy. The first drug that has shown promise in reducing COVID-19 mortality in severely-ill patients is dexamethasone, a cheap, well-known anti-inflammatory glucocorticoid, approved for the treatment of inflammatory conditions including respiratory diseases such as asthma and tuberculosis. However, about 80% of COVID-19 patients requiring oxygenation, and about 67% of patients on ventilators, are not responsive to dexamethasone therapy mainly. Additionally, using higher doses of dexamethasone for prolonged periods of time can lead to severe side effects and some patients may develop corticosteroid resistance leading to treatment failure. In order to increase the therapeutic efficacy of dexamethasone in COVID-19 patients, while minimizing dexamethasone-related complications that could result from using higher doses of the drug, we applied a chemocentric informatics approach to identify combination therapies. Our results indicated that combining dexamethasone with fast long-acting beta-2 adrenergic agonists (LABAs), such as formoterol and salmeterol, can ease respiratory symptoms hastily, until dexamethasone's anti-inflammatory and immunosuppressant effects kick in. Our studies demonstrated that LABAs and dexamethasone (or other glucocorticoids) exert synergistic effects that will augment both anti-inflammatory and fibronectin-mediated anticoagulant effects. We also propose other alternatives to LABAs that are supported by sound systems biology evidence, such as nitric oxide. Other drugs such as sevoflurane and treprostinil interact with the SARS-CoV-2 interactome and deserve further exploration. Moreover, our chemocentric informatics approach provides systems biology evidence that combination therapies for COVID-19 will have higher chances of perturbing the SARS-CoV-2 human interactome, which may negatively impact COVID-19 disease pathways.