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

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

Panantiviral agents: implications for treating cancers associated with oncoviruses

Tweetable abstract Panantiviral agents have emerged as a promising class of drugs for cancer therapy, targeting multiple oncoviruses simultaneously. Challenges include drug resistance, safety and developing specific inhibitors. Future research should focus on viral transcription regulators and new panantivirals. #cancer #oncovirus #panantiviral #drugresistance.

Progress of cGAS-STING signaling in response to SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19) is an epidemic respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can cause infections in millions of individuals, who can develop lung injury, organ failure, and subsequent death. As the first line of host defense, the innate immune system is involved in initiating the immune response to SARS-CoV-2 infection and the hyperinflammatory phenotype of COVID-19. However, the interplay between SARS-CoV-2 and host innate immunity is not yet well understood. It had become known that the cGAS-STING pathway is involved in the detection of cytosolic DNA, which elicits an innate immune response involving a robust type I interferon response against viral and bacterial infections. Nevertheless, several lines of evidence indicate that SARS-CoV-2, a single-stranded positive-sense RNA virus, triggered the cGAS-STING signaling pathway. Therefore, understanding the molecular and cellular details of cGAS-STING signaling upon SARS-CoV-2 infection is of considerable biomedical importance. In this review, we discuss the role of cGAS-STING signaling in SARS-CoV-2 infection and summarize the potential therapeutics of STING agonists as virus vaccine adjuvants.

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.

Treatment of Respiratory Viral Coinfections

With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.