Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools⊥

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.     

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.     

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure–biodegradation relationship (SBR) and quantitative structure–biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite? includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.     

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.     

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.     

Novel Extraction for Endocrine Disruptors in Atmospheric Particulate Matter

A rapid and efficient sample preparation method, which is called microwave-assisted microsolid phase extraction, was developed for the determination of endocrine disrupting chemicals in atmospheric particulate matter. The endocrine disrupting chemicals included bisphenol A, diethyl phthalate, dibutyl phthalate, and di(2-ethylhexyl) phthalate. The endocrine disrupting chemicals were isolated by microwave-assisted extraction following adsorption by copper(II) isonicotinate using microsolid phase extraction. The endocrine disrupting chemicals were subsequently determined by high performance liquid chromatography with an ultraviolet detector. The extraction was optimized for temperature, time, desorption time, and desorption solvent. Limits of detection (in the range of 2.0–8.5 nanograms per liter), limits of quantification (in the range of 6.6–28.0 nanograms per liter), and repeatability of the procedure (less than 10 percent) were established. Diethyl phthalate, diethyl phthalate, and di(2-ethylhexyl) phthalate were determined at values from 0.57 to 68.8 nanograms per cubic meter in atmospheric particulate matter collected from an urban area, a business center, and an industrial site in Dongguan, China. The concentration of bisphenol A was below the detection limit in these samples.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17beta-estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

Structure-activity approach to the identification of environmental estrogens: the MCASE approach

A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure-activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17beta-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rate, respectively, for compounds not in the learning sets. The good predictive ability of these models and their basis on simple to understand 2-D molecular fragments indicates their potential usefulness in computational screening methods for environmental estrogens.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17 g -estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.     

Fractionation and toxicity evaluation of waste waters

Several toxicity-based procedures have been proposed for waste water risk assessment but the toxic agents could never be identified in these very complex mixtures. A procedure was adopted using disposable solid-phase extraction cartridges to extract organic chemicals and preparative HPLC to fractionate them in relation of their hydrophobicity. Acute toxicity of whole samples and their fractions was measured on Daphnia magna, using a commercially available biokit. The procedure was applied to leachate from an industrial landfill and a textile effluent. In both cases the toxic effects due to xenobiotics were highest in the most hydrophobic HPLC fraction. The compounds responsible for the observed toxicity were identified and quantified by GC-MS. Reconstructed mixtures were analysed to assess their fitting with GC profiles and tested for toxicity to compare the responses of individual chemicals and mixtures.     

Computer‐aided prediction of toxicity with substructure pattern and random forest

Toxicity of chemicals induced by different factors is an important consideration, especially during the drug research and development process. Thus, there is urgent need to develop computationally effective models that can predict the toxicity or adverse effects of chemicals for a specific class of chemicals. In this study, random forest (RF) was used to classify five toxicity data sets from Distributed Structure‐Searchable Toxicity database network, using substructure fingerprints calculated directly from simple molecular structure. Three model validation approaches, out‐of‐bag validation incorporated in RF, fivefold cross‐validation, and an independent validation set, were used for assessing the prediction capability of our models. The chemical space analysis of data sets was explored by multidimensional scaling plots, and outlying molecules were also detected by the proximity measure in RF. At the same time, the important substructure fingerprints, recognized by the RF technique, gave some insights into the structure features related to toxicity of chemicals. The results obtained showed that these in silico classification models with substructure patterns and RF are applicable for potential toxicity prediction of chemical compounds. Copyright © 2012 John Wiley & Sons, Ltd.     

Electrochemistry-mass spectrometry for mechanistic studies and simulation of oxidation processes in the environment

Electrochemistry (EC) coupled to mass spectrometry (MS) has already been successfully applied to metabolism research for pharmaceutical applications, especially for the oxidation behaviour of drug substances. Xenobiotics (chemicals in the environment) also undergo various conversions; some of which are oxidative reactions. Therefore, EC-MS might be a suitable tool for the investigation of oxidative behaviour of xenobiotics. A further evaluation of this approach to environmental research is presented in the present paper using sulfonamide antibiotics. The results with sulfadiazine showed that EC-MS is a powerful tool for the elucidation of the oxidative degradation mechanism within a short time period. In addition, it was demonstrated that EC-MS can be used as a fast and easy method to model the chemical binding of xenobiotics to soil. The reaction of sulfadiazine with catechol, as a model substance for organic matter in soil, led to the expected chemical structure. Finally, by using EC-MS a first indication was obtained of the persistence of a component under chemical oxidation conditions for the comparison of the oxidative stability of different classes of xenobiotics. Overall, using just a few examples, the study demonstrates that EC-MS can be applied as a versatile tool for mechanistic studies of oxidative degradation pathways of xenobiotics and their possible interaction with soil organic matter as well as their oxidative stability in the environment. Further studies are needed to evaluate the full range of possibilities of the application of EC-MS in environmental research.     

卵黄蛋白原的分离测定及其在环境内分泌干扰物质筛选中的应用

环境内分泌干扰物的存在直接威胁野生动物的生存和人类的健康，对其作用机制及筛选方法的研究，已经成为环境科学研究的热点领域。近年来，卵黄蛋白原作为环境内分泌干扰物的“生物标志物”，得到了较深入的研究。本文讨论了卵黄蛋白原的分离测定方法及其在内分泌干扰物筛选中应用的最新进展，为建立更有效的卵黄蛋白分离测定方法及发展新的环境内分泌干扰物筛选技术提供参考。