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. The EU PARC project highlights BPA alternatives as priority chemicals and consolidates information on BPA alternatives, with a focus on environmental relevance and on the identification of the research gaps. The review highlighted aspects and future perspectives. In brief, an extension of environmental monitoring is crucial, extending it to cover BPA alternatives to track their levels and facilitate the timely implementation of mitigation measures. The biological activity has been studied for BPA alternatives, but in a non-systematic way and prioritized a limited number of chemicals. For several BPA alternatives, the data has already provided substantial evidence regarding their potential harm to the environment. We stress the importance of conducting more comprehensive assessments that go beyond the traditional reproductive studies and focus on overlooked relevant endpoints. Future research should also consider mixture effects, realistic environmental concentrations, and the long-term consequences on biota and ecosystems.

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. In most cases alternative approaches, however, while being apparently equally plausible, gave the same results for many molecules, but occasionally they differ in their characterization of molecular center. In order to reduce the number of vertices that would qualify as forming the center of the graph, a hierarchy of rules have been considered in the search for graph centers. We reconsidered the problem of "the center of a graph" by using a novel concept of graph theory, the vertex "weights," defined by counting the number of pairs of vertices at the same distance from the vertex considered. This approach gives often the same results for graph centers of acyclic graphs as the standard definition of graph center based on vertex eccentricities. However, in some cases when two nonequivalent vertices have been found as graph center, the novel approach can discriminate between the two. The same approach applies to cyclic graphs without additional rules to locate the vertex or vertices forming the center of polycyclic graphs, vertices referred to as central vertices of a graph. In addition, the novel vertex "weights," in the case of acyclic, cyclic, and polycyclic graphs can be interpreted as vertex centralities, a measure for how close or distant vertices are from the center or central vertices of the graph. Besides illustrating the centralities of a number of smaller polycyclic graphs, we also report on several acyclic graphs showing the same centrality values of their vertices.

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. The research examines 76 bisphenols, including BPA derivatives, using a variety of in silico ecotoxicological models, although it should be noted that these models were not developed exclusively for this particular class of compounds. Consequently, interpretations should be made with caution. The results of this study highlight specific compounds of potential environmental concern and underscore the need to develop more specific models for BPA alternatives that will allow for more accurate and reliable assessment.

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. We considered two series of 142 and 182 compounds and their binding affinity to the glucocorticoid receptor as agonists and antagonists, respectively. The compounds belong to different chemical classes. The compounds were represented by a set of descriptors calculated with the DRAGON program. The clustering structure of sets was studied with standard principal component method. A weak separation between binders and non-binders was found. Another classification model was developed using the counterpropagation artificial neural network method (CPANN). The final classification models developed were well balanced and showed a high level of accuracy, with 85.7% of GR agonist and 78.9% of GR antagonist correctly assigned in leave-one-out cross-validation.

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 ß, 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. For screening and prioritizing chemicals for further experimentation, the use of artificial neural networks (ANN), which allow the modeling of complicated, nonlinear relationships, is most appropriate. We developed six models that predict the binding of a compound to ARs, ERα, or ERß as agonists or antagonists, using counter-propagation artificial neural networks (CPANN). Models were trained on a dataset of structurally diverse compounds, and activity data were obtained from the CompTox Chemicals Dashboard. Leave-one-out (LOO) tests were performed to validate the models. The results showed that the models had excellent performance with prediction accuracy ranging from 94% to 100%. Therefore, the models can predict the binding affinity of an unknown compound to the selected nuclear receptor based solely on its chemical structure. As such, they represent important alternatives for the safety prioritization of chemicals.

Conjugated circuits currents in hexabenzocoronene and its derivatives formed by joining proximal carbons.

We report on calculated CC bond currents for a dozen derivatives of hexabenzocoroenene in which one or more proximal carbon atoms at the molecular periphery have been bridged. The approach that we use is graph-theoretical in nature, following our outline of this method in 2003, which is based on finding all conjugated circuits in all Kekulé valence structures of these molecules. To the π-electrons having 4n + 2 π-electrons are assigned anticlockwise π-electron currents and to conjugated circuits having 4n π-electrons are assigned π-electron currents. One may summarize the results reported in this work by stating that CC bond currents in the compounds considered decrease on going from peripheral rings to the central ring of the molecule, and also that CC bond currents decrease by insertion of bridges to proximal peripheral benzenoid rings.

Quantitative structure-activity relationship study of antitubercular fluoroquinolones.

Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (µg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.

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). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type. BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses. OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world. METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters. RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers. CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster.

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. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods. RESULTS: Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods. CONCLUSION: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Study of proteome maps using partial ordering.

This paper describes numerical characterization of proteome maps based on partial ordering of protein spots with respect to the mass and the charge. The partial ordering diagram is embedded directly over the 2D map and the corresponding adjacency matrix is constructed. The adjacency matrix is augmented by including the information on the abundance of proteins in a gel as suitably scaled diagonal entries of the matrix. The approach is illustrated on proteome maps of Anderson et al. (1996) based on experimental results from liver cells of rats exposed to four peroxisome proliferators. We used the leading eigenvectors of the adjacency matrices as maps descriptors in order to determine the degree of similarity between proteome maps.

Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

QSAR models for reproductive toxicity and endocrine disruption activity.

Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides). The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.

Ranking of QSAR Models to Predict Minimal Inhibitory Concentrations Toward Mycobacterium tuberculosis for a Set of Fluoroquinolones.

CP-ANN technique was used to build 54 different QSAR models. The models were built for three sets (assays) of fluoroquinolones considering their antituberculosis activity and using different technical parameters (dimension of network and number of learning epochs). The models served as a reliable basis for ranking by a new powerful method based on sum of ranking differences (SRD). With the applied SRD procedure we can find the optimal ones. The best model can be selected easily for the first assay. Two models can be recommended for the second assay, and no recommended model was found for the assay3.

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. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

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. Parts of the interface are presented and how they can be used. The examples provided show how the user can build a new model and view the results of predictions using the interface. Examples are given to show how the software may be used in read-across. CONCLUSIONS: CPANNatNIC provides a simple user interface for model development and visualisation. The interface implements options which may simplify read-across procedure. Statistical results show better prediction accuracy of read-across predictions than model predictions where similar compounds could be identified, which indicates the importance of using read-across and usefulness of the program.

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). For each nanosubstance cells were exposed to two concentrations of the material before carrying out proteomics analyses: 10 µg and 100 µg. In each case over 3000 proteins were identified. A mathematically based similarity index, which measures the changes in abundances of cellular proteins that are highly affected by exposure to the nanosubstances, was used to characterize toxic effects of the nanomaterials. RESULTS: We identified 8 and 25 proteins, which are most highly affected by MWCNT and TiO2-NB, respectively. These proteins may be responsible for specific response of cells to the nanoparticles. Further 14 reported proteins are affected by either of the two nanoparticles and they are probably related to nonspecific toxic response of the cells. CONCLUSION: The similarity methods proposed in this paper may be useful in the management and visualization of the large amount of data generated by proteomics technologies.

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. We present up-to-date information about defined structural alerts with their mechanisms and the software based on this knowledge (QSAR models and classification schemes).

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. This information was used to implement for clustering and classification of chemicals. As the technique the Self Organizing Maps was applied. This technique also enables us to define to each cluster the cluster indicator, i.e., the characteristic compound, which is considered as a representative for a cluster.

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. Predictions were performed using the Endocrine Disruptome docking program http://endocrinedisruptome.ki.si/ (Kolsek et al., 2013). 122 compounds were estimated to be possible endocrine disruptors bind to at least one of the receptors, 21 of them which are predicted to be probable toxicants for endocrine disruption as they bind to more than five receptors simultaneously. According to the literature survey and lack of experimental data it remains a challenge to prove or disprove the in silico results experimentally also for other potential endocrine disruptors.