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.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.
•An integrative virtual screening tool for risk assessment of EDCs is proposed.•Contemporary estrogen and androgen models show agreement with experimental data.•QSAR consensus modeling presented and evaluated for transparency and reliability.•Approach applied to Carbaryl show usefulness in screening EDCs and further testing.
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. The scheme was applied on two datasets ToxCast_AR_Agonist and ToxCast_AR_Antagonists with 1654 and 1522 compounds, respectively. The compounds are tested to androgen receptor activity (AR) in 11 high throughput screening assays. The carcinogenic dataset was used as the reference 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. Models provide beside the prediction of developmental toxicity additional information on similar compounds from models' training sets. In the report, first, we compare the predictions. Second, the sets of similar compounds have been used to implement the clustering scheme. The Kohonen artificial neural network method has been applied as a clustering method. The clusters obtained have been discussed for both models.
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. The cells were exposed at 10 μg and 100 μg concentrations for 3 and 24 hours to multi-walled carbon nanotubes and TiO
2
nanobelts (TiO
2
-NB). The data were analysed with the hierarchical clustering method and principal components analysis. In all cases, time of exposure is the most important factor in separation and clustering of proteomics patterns. Furthermore, the sets of proteins, which are specific for long (24 hours) exposure, are identified.
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. As a case study we present the results on a set of polychlorinated biphenyls (PCBs) and some of their metabolites. Their mutagenicity and carcinogenic potency were evaluated with CAESAR and T.E.S.T. models, which are freely available over the internet. We discuss the value and reliability of the predictions, the applicability domain of models and the ability to create prioritized groupings of PCBs as a category of chemicals.
A novel characterization of proteins is presented based on selected properties of recently introduced 20 × 20 amino acid adjacency matrix of proteins in which matrix elements count the occurrence of ...all 400 possible pair-wise adjacencies obtained by reading protein primary sequence from the left to the right. In particular we consider the characterization based on the sum and the difference of the rows and the corresponding columns, which characterize proteins by a pair of 20-component vectors. The approach is illustrated on a set of ND6 proteins of eight species.
†
Visitor, Emeritus, Department of Mathematics and Computer Science, Drake University, Des Moines, IA, USA.
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. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds' properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.
The present research investigates the study of a set of 27 (con)azoles and their reproductive toxicity. (Con)azoles are used as fungicides and herbicides in agriculture for treatment of fruits, ...vegetables, cereals, and seeds, or as human antimycotic therapeutics. According to EEC Directive 91/414, active substances used in plant protection products must undergo reproductive toxicity testing. Reproductive toxicity is a complex biological endpoint, which includes many different biological processes and, therefore, it can only to a limited extent be assessed by a single quantitative structure-activity relationship (QSAR) model. The proposed SAR models are built using unsupervised methods, such as hierarchical clustering, principal component analysis and self-organizing maps, with the aim of studying the similarity relationships between structures. The molecular structures are represented with a set of topological and structural descriptors. The models showing clusters, closest neighbours or outliers may support the categorization and the classification of (con)azoles as potential reproductive toxicants.
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 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R
2
= 0.664, F = 13.588, and
(LOO) = 0.542 while it achieves R
2
= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The
is 0.777, reflecting a relatively good predictive power for the external set.
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on ...Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.
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