Two stochastic models are proposed to describe the evolution of the COVID-19 pandemic. In the first model the population is partitioned into four compartments: susceptible S, infected I, removed R and dead people D. In order to have a cross validation, a deterministic version of such a model is also devised which is represented by a system of ordinary differential equations with delays. In the second stochastic model two further compartments are added: the class A of asymptomatic individuals and the class L of isolated infected people. Effects such as social distancing measures are easily included and the consequences are analyzed. Numerical solutions are obtained with Monte Carlo simulations. Quantitative predictions are provided which can be useful for the evaluation of political measures, e.g. the obtained results suggest that strategies based on herd immunity are too risky. Finally, the models are calibrated on data referring to the second wave of infection in Italy.
Background
Sigma (σ) receptors are accepted as a particular receptor class consisting of two subtypes: sigma-1 (σ1) and sigma-2 (σ2). The two receptor subtypes have specific drug actions, pharmacological profiles and molecular characteristics. The σ2 receptor is overexpressed in several tumor cell lines, and its ligands are currently under investigation for their role in tumor diagnosis and treatment. The σ2 receptor structure has not been disclosed, and researchers rely on σ2 receptor radioligand binding assay to understand the receptor’s pharmacological behavior and design new lead compounds.Description
Here we present the sigma-2 Receptor Selective Ligands Database (S2RSLDB) a manually curated database of the σ2 receptor selective ligands containing more than 650 compounds. The database is built with chemical structure information, radioligand binding affinity data, computed physicochemical properties, and experimental radioligand binding procedures. The S2RSLDB is freely available online without account login and having a powerful search engine the user may build complex queries, sort tabulated results, generate color coded 2D and 3D graphs and download the data for additional screening.ConclusionThe collection here reported is extremely useful for the development of new ligands endowed of σ2 receptor affinity, selectivity, and appropriate physicochemical properties. The database will be updated yearly and in the near future, an online submission form will be available to help with keeping the database widely spread in the research community and continually updated. The database is available at http://www.researchdsf.unict.it/S2RSLDB.Graphical abstract
The data have been obtained from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) and refined according to the QSAR requirements. These data provide information about a set of 548 Sigma-2 (σ2) receptor ligands selective over Sigma-1 (σ1) receptor. The development of the QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). Data here reported include the regression for σ2 receptor pKi QSAR models. The QSAR model was also employed to predict the σ2 receptor pKi values of the FDA approved drugs that are herewith included.
Due to increasing interest in the field of heme oxygenases (HOs), we built a ligand database called HemeOxDB that includes the entire set of known HO-1 and HO-2 inhibitors, resulting in more than 400 compounds. The HemeOxDB is available online at http://www.researchdsf.unict.it/hemeoxdb/, and having a robust search engine allows end users to build complex queries, sort tabulated results, and generate color-coded two- and three-dimensional graphs. This database will grow to be a tool for the design of potent and selective HO-1 or HO-2 inhibitors. We were also interested in virtually searching for alternative inhibitors, and, for the first time in the field of HOs, a quantitative structure-activity relationship (QSAR) model was built using half-maximal inhibitory concentration (IC ) values of the whole set of known HO-1 inhibitors, taken from the HemeOxDB and employing the Monte Carlo technique. The statistical quality suggested that the model is robust and possesses desirable predictive potential. The screening of US Food and Drug Administration (FDA)-approved drugs, external to our dataset, suggested new predicted inhibitors, opening the way for replacing imidazole groups. The HemeOxDB and the QSAR model reported herein may help in prospectively identifying or repurposing new drugs with optimal structural attributes for HO enzyme inhibition.
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