From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling.

Background: Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies. Aim: Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease. Methods: We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics. Results: By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Proteasome Inhibitors as a Possible Therapy for SARS-CoV-2.

The COVID-19 global pandemic is caused by SARS-CoV-2, and represents an urgent medical and social issue. Unfortunately, there is still not a single proven effective drug available, and therefore, current therapeutic guidelines recommend supportive care including oxygen administration and treatment with antibiotics. Recently, patients have been also treated with off-label therapies which comprise antiretrovirals, anti-inflammatory compounds, antiparasitic agents and plasma from convalescent patients, all with controversial results. The ubiquitin-proteasome system (UPS) is important for the maintenance of cellular homeostasis, and plays a pivotal role in viral replication processes. In this review, we discuss several aspects of the UPS and the effects of its inhibition with particular regard to the life cycle of the coronaviruses (CoVs). In fact, proteasome inhibition by various chemical compounds, such as MG132, epoxomycin and bortezomib, may reduce the virus entry into the eucariotic cell, the synthesis of RNA, and the subsequent protein expression necessary for CoVs. Importantly, since UPS inhibitors reduce the cytokine storm associated with various inflammatory conditions, it is reasonable to assume that they might be repurposed for SARS-CoV-2, thus providing an additional tool to counteract both virus replication as well as its most deleterious consequences triggered by abnormal immunological response.