In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

Abstract

The recent global COVID-19 outbreak, attributed by the World Health Organization to the rapid spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), underscores the need for an extensive exploration of virological intricacies, fundamental pathophysiology, and immune responses. This investigation is vital to unearth potential therapeutic avenues and preventive strategies. Our study delves into the intricate interaction between SARS-CoV-2 and the immune system, coupled with exploring therapeutic interventions to counteract dysfunctional immune responses like the cytokine storm (CS), a driver of disease progression. Understanding these immunological dimensions informs the design of precise multiepitopetargeted peptide vaccines using advanced immunoinformatics and equips us with tools to confront the cytokine storm. Employing a control theory-based approach, we scrutinize the perturbed behavior of key proteins associated with cytokine storm during COVID-19 infection. Our findings support ACE2 activation as a potential drug target for CS control and confirm AT1R inhibition as an alternative strategy. Leveraging deep learning, we identify potential drugs to individually target ACE2 and AT1R, with Lomefloxacin and Fostamatinib emerging as standout options due to their close interaction with ACE2. Their stability within the protein-drug complex suggests superior efficacy among many drugs from our deep-learning analysis. Moreover, there is a significant scope for optimization in fine-tuning protein-drug interactions. Strong binding alone may not be the sole determining factor for potential drugs; precise adjustments are essential. The application of advanced computational power offers novel solutions, circumventing time-consuming lab work. In scenarios necessitating both ACE2 and AT1R targeting, optimal drug combinations can be derived from our analysis of drug-drug interactions, as detailed in the manuscript.