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As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing–remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment. [ABSTRACT FROM AUTHOR]

The rapid spread of the virus, the surge in the number of deaths, and the unavailability of specific SARS-CoV-2 drugs thus far necessitate the identification of drugs with anti-COVID-19 activity. SARS-CoV-2 enters the host cell and assembles a multisubunit RNA-dependent RNA polymerase (RdRp) complex of viral nonstructural proteins that plays a substantial role in the transcription and replication of the viral genome. Therefore, RdRp is among the most suitable targets in RNA viruses. Our aim was to investigate the FDA approved antiviral drugs having potential to inhibit the viral replication. The methodology adopted was virtual screening and docking of FDA-approved antiviral drugs into the RdRp protein. Top hits were selected and subjected to molecular dynamics simulations to understand the dynamics of RdRp in complex with these drugs. The antiviral activity of the drugs against SARS-CoV-2 was assessed in Vero E6 cells. Notably, both remdesivir (half-maximal effective concentration (EC50) 6.6 μM, 50% cytotoxicity concentration (CC50) > 100 µM, selectivity index (SI) = 15) and ledipasvir (EC50 34.6 μM, CC50 > 100 µM, SI > 2.9) exerted antiviral action. This study highlights the use of direct-acting antiviral drugs, alone or in combination, for better treatments of COVID-19. [ABSTRACT FROM AUTHOR]