Ebselen derivatives inhibit SARS-CoV-2 replication by inhibition of its essential proteins: PLpro and Mpro proteases, and nsp14 guanine N7-methyltransferase.

Proteases encoded by SARS-CoV-2 constitute a promising target for new therapies against COVID-19. SARS-CoV-2 main protease (Mpro, 3CLpro) and papain-like protease (PLpro) are responsible for viral polyprotein cleavage-a process crucial for viral survival and replication. Recently it was shown that 2-phenylbenzisoselenazol-3(2H)-one (ebselen), an organoselenium anti-inflammatory small-molecule drug, is a potent, covalent inhibitor of both the proteases and its potency was evaluated in enzymatic and antiviral assays. In this study, we screened a collection of 34 ebselen and ebselen diselenide derivatives for SARS-CoV-2 PLpro and Mpro inhibitors. Our studies revealed that ebselen derivatives are potent inhibitors of both the proteases. We identified three PLpro and four Mpro inhibitors superior to ebselen. Independently, ebselen was shown to inhibit the N7-methyltransferase activity of SARS-CoV-2 nsp14 protein involved in viral RNA cap modification. Hence, selected compounds were also evaluated as nsp14 inhibitors. In the second part of our work, we employed 11 ebselen analogues-bis(2-carbamoylaryl)phenyl diselenides-in biological assays to evaluate their anti-SARS-CoV-2 activity in Vero E6 cells. We present their antiviral and cytoprotective activity and also low cytotoxicity. Our work shows that ebselen, its derivatives, and diselenide analogues constitute a promising platform for development of new antivirals targeting the SARS-CoV-2 virus.

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland.

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.

Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks.

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.

Modeling the Spatial and Temporal Spread of COVID-19 in Poland Based on a Spatial Interaction Model

This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model's fit with the spatiotemporal distribution of the validation data.

Attacking the Intruder at the Gate: Prospects of Mucosal Anti SARS-CoV-2 Vaccines.

The sudden outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic in December 2019 caused crises and health emergencies worldwide. The rapid spread of the virus created an urgent need for the development of an effective vaccine and mass immunization to achieve herd immunity. Efforts of scientific teams at universities and pharmaceutical companies around the world allowed for the development of various types of preparations and made it possible to start the vaccination process. However, it appears that the developed vaccines are not effective enough and do not guarantee long-lasting immunity, especially for new variants of SARS-CoV-2. Considering this problem, it is promising to focus on developing a Coronavirus Disease 2019 (COVID-19) mucosal vaccine. Such a preparation applied directly to the mucous membranes of the upper respiratory tract might provide an immune barrier at the primary point of virus entry into the human body while inducing systemic immunity. A number of such preparations against SARS-CoV-2 are already in various phases of preclinical and clinical trials, and several of them are very close to being accepted for general use, constituting a milestone toward pandemic containment.

Reynoutria Rhizomes as a Natural Source of SARS-CoV-2 Mpro Inhibitors-Molecular Docking and In Vitro Study.

More than a year has passed since the world began to fight the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the Coronavirus disease 2019 (COVID-19) pandemic, and still it spreads around the world, mutating at the same time. One of the sources of compounds with potential antiviral activity is Traditional Chinese Medicinal (TCM) plants used in China in the supportive treatment of COVID-19. Reynoutria japonica is important part of the Shu Feng Jie Du Granule/Capsule-TCM herbal formula, recommended by China Food and Drug Administration (CFDA) for treatment of patients with H1N1- and H5N9-induced acute lung injury and is also used in China to treat COVID-19, mainly combined with other remedies. In our study, 25 compounds from rhizomes of R. japonica and Reynoutria sachalinensis (related species), were docked into the binding site of SARS-CoV-2 main protease. Next, 11 of them (vanicoside A, vanicoside B, resveratrol, piceid, emodin, epicatechin, epicatechin gallate, epigallocatechin gallate, procyanidin B2, procyanidin C1, procyanidin B2 3,3'-di-O-gallate) as well as extracts and fractions from rhizomes of R. japonica and R. sachalinensis were tested in vitro using a fluorescent peptide substrate. Among the tested phytochemicals the best results were achieved for vanicoside A and vanicoside B with moderate inhibition of SARS-CoV-2 Mpro, IC50 = 23.10 µM and 43.59 µM, respectively. The butanol fractions of plants showed the strongest inhibition of SARS-CoV-2 Mpro (IC50 = 4.031 µg/mL for R. sachalinensis and IC50 = 7.877 µg/mL for R. japonica). As the main constituents of butanol fractions, besides the phenylpropanoid disaccharide esters (e.g., vanicosides), are highly polymerized procyanidins, we suppose that they could be responsible for their strong inhibitory properties. As inhibition of SARS-CoV-2 main protease could prevent the replication of the virus our research provides data that may explain the beneficial effects of R. japonica on COVID-19 and identify the most active compounds worthy of more extensive research.