Exploring BPA alternatives - Environmental levels and toxicity review.

Bisphenol A alternatives are manufactured as potentially less harmful substitutes of bisphenol A (BPA) that offer similar functionality. These alternatives are already in the market, entering the environment and thus raising ecological concerns. However, it can be expected that levels of BPA alternatives will dominate in the future, they are limited information on their environmental safety.

Ecotoxicological Evaluation of Bisphenol A and Alternatives: A Comprehensive In Silico Modelling Approach.

Bisphenol A (BPA), a compound widely used in industrial applications, has raised concerns due to its environmental impact. As a key component in the manufacture of polycarbonate plastics and epoxy resins used in many consumer products, concerns about potential harm to human health and the environment are unavoidable. This study seeks to address these concerns by evaluating a range of potential BPA alternatives, focusing on their ecotoxicological properties.

Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks.

Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the normal function of the human endocrine system. These chemicals can affect specific nuclear receptors, such as androgen receptors (ARs) or estrogen receptors (ER) α and β, which play a crucial role in regulating complex physiological processes in humans. It is now more crucial than ever to identify EDCs and reduce exposure to them.

Development of in silico classification models for binding affinity to the glucocorticoid receptor.

The endocrine disrupting properties of chemicals acting through the glucocorticoid receptor (GR) have attracted considerable interest. Since there are few data for most chemicals on their endocrine properties in silico approaches seem to be the most appropriate tool for screening and prioritizing chemicals for planning further experiments. In this work, we developed classification models for binding affinity to the glucocorticoid receptor using the counterpropagation artificial neural network method.

Comparison of predictions of developmental toxicity for compounds of solvent data set.

We have considered a series of 235 compounds technically classified as solvents. Chemically, they belong to different classes. Their potential developmental toxicity was evaluated using two models available on platform VEGA HUB; model CAESAR and the Developmental/ Reproductive Toxicity library (PG) model.

Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19).

Introduction: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks).

Clustering of Zika Viruses Originating from Different Geographical Regions using Computational Sequence Descriptors.

Background: In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens).

Methods: For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices.

Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus.

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges.

Chemometrical analysis of proteomics data obtained from three cell types treated with multi-walled carbon nanotubes and TiO nanobelts.

Applications of nanomaterials in biomedical, industrial, and consumer goods areas are expanding rapidly because of their unique physicochemical properties. Hazard assessment of nanosubstances is necessary for the protection of human and ecological health. We studied the proteomics patterns of three cell lines: co-culture of Caco-2 and HT29-MTX cells, primary small airway epithelial cells, and THP-1macrophage-like cells.

Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database.

Methods for clustering and measures of similarity of chemical structures have become an important supporting tool in chemoinformatics. They represent the basis for categorization of chemicals and read-across, where a molecular property is estimated from 'similar molecules'. This study proposes a clustering scheme within the given dataset with respect to a reference dataset.

CPANNatNIC software for counter-propagation neural network to assist in read-across.

Background: CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across.

Results: The work presents the details of the program's interface.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors.

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.

Mutagenic and carcinogenic structural alerts and their mechanisms of action.

Knowing the mutagenic and carcinogenic properties of chemicals is very important for their hazard (and risk) assessment. One of the crucial events that trigger genotoxic and sometimes carcinogenic effects is the forming of adducts between chemical compounds and nucleic acids and histones. This review takes a look at the mechanisms related to specific functional groups (structural alerts or toxicophores) that may trigger genotoxic or epigenetic effects in the cells.

Mathematical Nanotoxicoproteomics: Quantitative Characterization of Effects of Multi-walled Carbon Nanotubes (MWCNT) and TiO2 Nanobelts (TiO2-NB) on Protein Expression Patterns in Human Intestinal Cells.

Background: Various applications of nanosubstances in industrial and consumer goods sectors are growing rapidly because of their useful chemical and physical properties.

Objectives: Assessment of hazard posed by exposure to nanosubstances is essential for the protection of human and ecological health.

Methods: We analyzed the proteomics patterns of Caco-2/HT29-MTX cells in co-culture exposed for three and twenty four hours to two kinds of nanoparticles: multi-walled carbon nanotubes (MWCNT) and TiO2 nanobelts (TiO2-NB).

Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm.

Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals.

Prediction of mutagenicity and carcinogenicity using in silico modelling: A case study of polychlorinated biphenyls.

In silico modelling is an important alternative method for the evaluation of properties of chemical compounds. Basically, two concepts are used in its applications: QSAR modelling for endpoint predictions, and grouping (categorization) of large groups of chemicals. In the presented report we address both of these concepts.

Computational study of binding affinity to nuclear receptors for some cosmetic ingredients.

We studied the ingredients of cosmetic products as potential endocrine disruptors (ED) by in silico methods (docking). The structures of 14 human nuclear receptors have been retrieved from the protein data bank (PDB). We only considered the mechanism linked with direct binding to nuclear receptors with well-defined crystal structures.

Evaluation of toxic endpoints for a set of cosmetic ingredients with CAESAR models.

The randomly selected set of 558 chemicals from Cosmetic inventory was studied with internet accessible program package CAESAR. Four toxic endpoints were considered: mutagenicity, carcinogenicity, developmental toxicity and skin sensitization. The CAESAR program provides beside the predictions comprehensive information on applicability domain and the similarity between the considered compound and the compounds from model's training set.

On the centrality of vertices of molecular graphs.

For acyclic systems the center of a graph has been known to be either a single vertex of two adjacent vertices, that is, an edge. It has not been quite clear how to extend the concept of graph center to polycyclic systems. Several approaches to the graph center of molecular graphs of polycyclic graphs have been proposed in the literature.

Prediction of mutagenicity, carcinogenicity, developmental toxicity, and skin sensitisation with Caesar program for a set of conazoles.

This article presents models to predict mutagenicity, carcinogenicity, developmental toxicity, and skin sensitisation for a set of 27 conazoles. The predictions were performed with the program package CAESAR, which is available on the Internet. The CAESAR programs were developed to support the European Community Regulation on chemicals and their safe use (REACH) and follow the OECD principles for (Q)SAR models used for regulatory purposes.

QSTR modelling of the acute toxicity of pharmaceuticals to fish.

Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.