Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles.

Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha.

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.

Indirect-Acting Pan-Antivirals vs. Respiratory Viruses: A Fresh Perspective on Computational Multi-Target Drug Discovery.

Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [including the current pandemic agent known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and the one causing influenza A (IAV) are highly contagious and deadly. These can evade the immune system defenses while causing a hyperinflammatory response that can damage different tissues/organs. Simultaneously targeting several immunomodulatory proteins is a plausible antiviral strategy since it could lead to the discovery of indirect-acting pan-antiviral (IAPA) agents for the treatment of diseases caused by respiratory viruses. In this context, computational approaches, which are an essential part of the modern drug discovery campaigns, could accelerate the identification of multi-target immunomodulators. This perspective discusses the usefulness of computational multi-target drug discovery for the virtual screening (drug repurposing) of IAPA agents capable of boosting the immune system through the activation of the toll-like receptor 7 (TLR7) and/or the stimulator of interferon genes (STING) while inhibiting key inflammation-related proteins such as caspase-1 and tumor necrosis factor-alpha (TNF-α).