Evaluation of a novel method of predicting estrogen activity of a group of structurally diverse compounds

The number of environmental chemicals found to have some level of endocrine activity has led to concern about the possible effects these compounds could have on human health and the health of other species, populations, and possibly whole ecosystems. The United States Environmental Protection Agency has been charged with testing a large number of these compounds, called endocrine-disrupting chemicals or hormonally active agents for hormonal activity. Limited testing resources have led to a call for alternate methods of screening, possibly for use in prioritizing this list to assist in efficient allocation of resources for further testing. This study describes a computational method, the categorical structure activity relationship (cat-SAR) program, which has demonstrated high predictivity for the estrogen-like activity of a set of diverse chemical structures. The data set for this model was taken from a set of 122 compounds assayed for estrogenicity with the ESCREEN assay, an in vitro assay for estrogenicity. Two endpoints were modeled. The model for relative proliferative potency demonstrated an 82% correct prediction rate, while the relative proliferative effect achieved an 86% correct rate of prediction in model validation. Preliminary evaluation of fragments upon which the models were based suggested a sound mechanistic basis. The models also compared similarly to previous ESCREEN models developed using a different methodology. Based on the results described herein, the cat-SAR method would be a useful approach in screening compounds for estrogen activity as well as for investigating their mechanism of action

Assessment of risks related to agricultural use of sewage sludge, pig and cattle slurry

In April 2017, the Organic Business Development Team released a report with 25 recommendations for the Minister of Environment and Food (Det økologiske erhvervsteam 2017). Among these was a recommendation that organic farmers should have opportunities for utilizing nutrients from treated domestic wastewater for nutrient recycling. A prerequisite for future use of nutrients from treated wastewater is, that quality requirements are met and that application can be explained to (and accepted by) consumers. In partial fulfilment of this, the business team identified a need for a scientific overview of the risks of using nutrients from treated municipal wastewater in relation to other authorized fertilizer sources – e.g. conventional animal manures. Thus, it was assumed that a comparative approach to assess potential risk of using sewage sludge and conventional manures, could usefully inform decision makers in the future regulation of organic farming systems. Dependent on the result of the scientific investigation, the Organic Business Development Team foresaw that Denmark could chose to work to expand Annex 1 of the EU Ecology Regulation, to allow the organic farmers to use nutrients from municipal wastewater or other acceptable derived sludge products. Mobilization of support for this should be done by the Ministry of Environment and Food in collaboration with the Organic Farming Industry. Thus, based on available literature, this report aims at creating an overview of the environmental and human risks associated with application of pig and cattle slurry as well as sewage sludge to agricultural soils. The risk evaluation was performed for the following compound groups: Metals, Chlorophenyls, Dioxins, Furans, Halogenated aliphatic and aromatic hydrocarbons (HAH), Linear alkylbenzenesulfonates (LAS), Polyaromatic hydrocarbons (PAH), Polybrominated diphenyl ethers (PBDE), Polychlorinated biphenyls (PCB), Poly- and perfluorinated alkylated substances (PFAS), Phenols, Phosphate-triesters VII, Phthalates, Polychlorinated naphtalenes (PCN), Polychlorinated alkanes (PCA), Triclosan, Triclocarban, Medicines, Estrogens, Antibiotic resistance genes. Additionally the fertilizer potential of the two nutrient sources was characterized and compared

Identification of \u27structural alerts\u27 and associated mechanisms of action of mammary gland carcinogens in female rodents

A new structure-activity relationship (SAR) approach to modeling was utilized to study mammary gland carcinogens. A set of chemicals tested for mammary tumorigenesis that have been analyzed in the Carcinogenic Potency Database (CPDB) were subjected to several computational analyses in an attempt to predict each chemical’s carcinogenic potential. A total of six learning sets (rat and mouse mammary gland carcinogen, CPDB rat and mouse, and female-specific rodent models) were developed and validated using a SAR modeling algorithm called categorical-SAR (cat-SAR). The predictive cat-SAR program evaluates active and inactive compounds of known biological activity and predicts their biological activity from this categorical data. Overall, this study demonstrates the usefulness of cat-SAR and its successful application in developing ‘structural alerts’ to breast carcinogenicity. The resulting rat and mouse mammary carcinogen models achieved an 82.0% (sensitivity 76.7%; specificity 87.5%) and 80.6% (sensitivity 80%; specificity 81.8%) concordance between experimental and predicted results, respectively. Likewise, the general CPDB mouse and rat models were both 70% predictive. Corresponding sensitivity and specificity values were 74.2 and 66.7% and 70.4 and 68.5%, respectively. The analyses indicate the capability of cat-SAR in identifying molecular fragments that potentially interact with cellular components present only in the targeted cell type (e.g., breast tissue cells). Moreover, this method is expected to help pre-determine structural alerts to carcinogen-induced mammary cancer. Identification of these ‘structural alerts’ can assist in understanding mechanisms involved in making a normal breast cell cancerous. Using the results of these analyses, it is possible to classify and rank structurally diverse chemicals as to their potential to induce mammary gland cancer

Investigation on Quantitative Structure-Activity Relationships of 1,3,4 Oxadiazole Derivatives as Potential Telomerase Inhibitors

The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/164022/article, DOI : 10.2174/1570163815666180724113208. © 2018 Bentham ScienceA series of 1,3,4-oxadiazole derivatives with significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition, was subjected to Quantitative Structure-Activity Relationships (QSAR) analysis. Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds. The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.Peer reviewedFinal Accepted Versio

Structure-activity relationship model for estrogen receptor ligands.

Xenoestrogens are spread throughout the environment affecting our daily lives and may produce potential toxic effects on human health. The purpose of this study was to develop a mechanistically reliable model capable of identifying xenoestrogens. Our hypothesis was that there are identifiable structural characteristics among a diverse set of estrogen receptor ligands that differentiate estrogenic and nonestrogenic compounds. The model\u27s learning set was developed by collecting compounds from the National Center for Toxicological Research Estrogen Receptor Binding database (NCTRER) . The categorical-SAR (cat-SAR) expert system was used to build the models and perform leave-none-out, leave-one-out, leave-many-out and external validations for model analysis. The values of all validations were between 0.80 and 0.97. Based on several analyses of rational subsets of compounds included in the NCTRER based on potency or chemical structure, it was observed that the developed SAR models predictivity varied across sets. This indicates that variability in the SAR models or the in vitro assay results themselves must be considered when applying SAR models for prediction or mechanistic analyses of estrogen receptor ligands. Fragment analysis was carried out to study the mechanism of estrogen receptor binding, and various important fragments were identified that demonstrate potential structural characteristics important for binding. Furthermore, this led to the discovery that the cat-SAR expert system was able to make a higher percentage of correct predictions on specific classes of xenoestrogen expressing these key functional groups. In conclusion, this estrogen receptor ligand model has good predictive performance and is based on model attributes that are mechanistically sound