Prediction of Chemical Function: Model Development and Application

EPA Science Inventory

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

MODELING A MIXTURE: PBPK/PD APPROACHES FOR PREDICTING CHEMICAL INTERACTIONS.

EPA Science Inventory

Since environmental chemical exposures generally involve multiple chemicals, there are both regulatory and scientific drivers to develop methods to predict outcomes of these exposures. Even using efficient statistical and experimental designs, it is not possible to test in vivo a...

Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

PubMed Central

Rusyn, Ivan; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander

2012-01-01

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage. PMID:22387746

Predicting organ toxicity using in vitro bioactivity data and chemical structure

EPA Science Inventory

Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches together with high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a superv...

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

THE TOXCAST PROGRAM FOR PRIORITIZING TOXICITY TESTING OF ENVIRONMENTAL CHEMICALS

EPA Science Inventory

The United States Environmental Protection Agency (EPA) is developing methods for utilizing computational chemistry, high-throughput screening (HTS) and various toxicogenomic technologies to predict potential for toxicity and prioritize limited testing resources towards chemicals...

ToxCast: Developing Predictive Signatures of Chemically Induced Toxicity (S)

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry, bioactivity profiling and toxicogenomic data to predict potential for toxicity and prioritize limited testing resour...

EPA's ToxCast Program for Predicting Hazard and Prioritizing the Toxicity Testing of Environmental Chemicals

EPA Science Inventory

An alternative is to perform a set of relatively inexpensive and rapid high throughput screening (HTS) assays, derive signatures predictive of effects or modes of chemical toxicity from the HTS data, then use these predictions to prioritize chemicals for more detailed analysis. T...

Use of the Chemical Transformation Simulator as a Parameterization Tool for Modeling the Environmental Fate of Organic Chemicals and their Transformation Products

EPA Science Inventory

A Chemical Transformation Simulator is a web-based system for predicting transformation pathways and physicochemical properties of organic chemicals. Role in Environmental Modeling • Screening tool for identifying likely transformation products in the environment • Parameteri...

Environmental Impact on Vascular Development Predicted by High-Throughput Screening

PubMed Central

Judson, Richard S.; Reif, David M.; Sipes, Nisha S.; Singh, Amar V.; Chandler, Kelly J.; DeWoskin, Rob; Dix, David J.; Kavlock, Robert J.; Knudsen, Thomas B.

2011-01-01

Background: Understanding health risks to embryonic development from exposure to environmental chemicals is a significant challenge given the diverse chemical landscape and paucity of data for most of these compounds. High-throughput screening (HTS) in the U.S. Environmental Protection Agency (EPA) ToxCast™ project provides vast data on an expanding chemical library currently consisting of > 1,000 unique compounds across > 500 in vitro assays in phase I (complete) and Phase II (under way). This public data set can be used to evaluate concentration-dependent effects on many diverse biological targets and build predictive models of prototypical toxicity pathways that can aid decision making for assessments of human developmental health and disease. Objective: We mined the ToxCast phase I data set to identify signatures for potential chemical disruption of blood vessel formation and remodeling. Methods: ToxCast phase I screened 309 chemicals using 467 HTS assays across nine assay technology platforms. The assays measured direct interactions between chemicals and molecular targets (receptors, enzymes), as well as downstream effects on reporter gene activity or cellular consequences. We ranked the chemicals according to individual vascular bioactivity score and visualized the ranking using ToxPi (Toxicological Priority Index) profiles. Results: Targets in inflammatory chemokine signaling, the vascular endothelial growth factor pathway, and the plasminogen-activating system were strongly perturbed by some chemicals, and we found positive correlations with developmental effects from the U.S. EPA ToxRefDB (Toxicological Reference Database) in vivo database containing prenatal rat and rabbit guideline studies. We observed distinctly different correlative patterns for chemicals with effects in rabbits versus rats, despite derivation of in vitro signatures based on human cells and cell-free biochemical targets, implying conservation but potentially differential

ToxCast: Developing Predictive Signatures of Chemically Induced Toxicity (Developing Predictive Bioactivity Signatures from ToxCasts HTS Data)

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry, bioactivity profiling and toxicogenomic data to predict potential for toxicity and prioritize limited testing resour...

Toxicokinetic Triage for Environmental Chemicals.

PubMed

Wambaugh, John F; Wetmore, Barbara A; Pearce, Robert; Strope, Cory; Goldsmith, Rocky; Sluka, James P; Sedykh, Alexander; Tropsha, Alex; Bosgra, Sieto; Shah, Imran; Judson, Richard; Thomas, Russell S; Setzer, R Woodrow

2015-09-01

Toxicokinetic (TK) models link administered doses to plasma, blood, and tissue concentrations. High-throughput TK (HTTK) performs in vitro to in vivo extrapolation to predict TK from rapid in vitro measurements and chemical structure-based properties. A significant toxicological application of HTTK has been "reverse dosimetry," in which bioactive concentrations from in vitro screening studies are converted into in vivo doses (mg/kg BW/day). These doses are predicted to produce steady-state plasma concentrations that are equivalent to in vitro bioactive concentrations. In this study, we evaluate the impact of the approximations and assumptions necessary for reverse dosimetry and develop methods to determine whether HTTK tools are appropriate or may lead to false conclusions for a particular chemical. Based on literature in vivo data for 87 chemicals, we identified specific properties (eg, in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. For 271 chemicals we developed a generic HT physiologically based TK (HTPBTK) model that predicts non-steady-state chemical concentration time-courses for a variety of exposure scenarios. We used this HTPBTK model to find that assumptions previously used for reverse dosimetry are usually appropriate, except most notably for highly bioaccumulative compounds. For the thousands of man-made chemicals in the environment that currently have no TK data, we propose a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions. For 349 chemicals with literature HTTK data, we differentiated those chemicals for which HTTK approaches are likely to be sufficient, from those that may require additional data. Published by Oxford University Press on behalf of Society of Toxicology 2015. This work is written by US Government employees and is in the public

Toxicokinetic Triage for Environmental Chemicals

PubMed Central

Wambaugh, John F.; Wetmore, Barbara A.; Pearce, Robert; Strope, Cory; Goldsmith, Rocky; Sluka, James P.; Sedykh, Alexander; Tropsha, Alex; Bosgra, Sieto; Shah, Imran; Judson, Richard; Thomas, Russell S.; Woodrow Setzer, R.

2015-01-01

Toxicokinetic (TK) models link administered doses to plasma, blood, and tissue concentrations. High-throughput TK (HTTK) performs in vitro to in vivo extrapolation to predict TK from rapid in vitro measurements and chemical structure-based properties. A significant toxicological application of HTTK has been “reverse dosimetry,” in which bioactive concentrations from in vitro screening studies are converted into in vivo doses (mg/kg BW/day). These doses are predicted to produce steady-state plasma concentrations that are equivalent to in vitro bioactive concentrations. In this study, we evaluate the impact of the approximations and assumptions necessary for reverse dosimetry and develop methods to determine whether HTTK tools are appropriate or may lead to false conclusions for a particular chemical. Based on literature in vivo data for 87 chemicals, we identified specific properties (eg, in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. For 271 chemicals we developed a generic HT physiologically based TK (HTPBTK) model that predicts non-steady-state chemical concentration time-courses for a variety of exposure scenarios. We used this HTPBTK model to find that assumptions previously used for reverse dosimetry are usually appropriate, except most notably for highly bioaccumulative compounds. For the thousands of man-made chemicals in the environment that currently have no TK data, we propose a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions. For 349 chemicals with literature HTTK data, we differentiated those chemicals for which HTTK approaches are likely to be sufficient, from those that may require additional data. PMID:26085347

Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug-like Molecules

EPA Science Inventory

The human cytochrome P450 (CYP450) enzyme family is involved in the biotransformation of many environmental chemicals. As part of the U.S. Tox21 effort, we profiled the CYP450 activity of ~2800 chemicals predominantly of environmental concern against CYP1A2, CYP2C19, CYP2C9, CYP2...

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

An Online Prediction Platform to Support the Environmental Sciences (American Chemical Society)

EPA Science Inventory

Historical QSAR models are currently utilized across a broad range of applications within the U.S. Environmental Protection Agency (EPA). These models predict basic physicochemical properties (e.g., logP, aqueous solubility, vapor pressure), which are then incorporated into expo...

Environmental Chemicals in Breast Milk

EPA Science Inventory

Most of the information available on environmental chemicals in breast milk is focused on persistent, lipophilic chemicals; the database on levels of these chemicals has expanded substantially since the 1950s. Currently, various types of chemicals are measured in breast milk and ...

Environmental risk assessment of selected organic chemicals based on TOC test and QSAR estimation models.

PubMed

Chi, Yulang; Zhang, Huanteng; Huang, Qiansheng; Lin, Yi; Ye, Guozhu; Zhu, Huimin; Dong, Sijun

2018-02-01

Environmental risks of organic chemicals have been greatly determined by their persistence, bioaccumulation, and toxicity (PBT) and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor (BCF) and octanol-water partition coefficient (Kow), which frequently displays a substantial correlation with the sediment sorption coefficient (Koc). Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship (QSAR) estimation models are indispensable for predicting environmental fate and health effects in the absence of field- or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon (TOC) degradation, and two widely accepted and highly used QSAR estimation models (i.e., EPI Suite and PBT Profiler) were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment. However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants. Copyright © 2016. Published by Elsevier B.V.

Aftereffect Calculation and Prediction of Methanol Tank Leak’s Environmental Risk Accident

NASA Astrophysics Data System (ADS)

Lang, Yueting; Zheng, Lina; Chen, Henan; Wang, Qiushi; Jiang, Hui; Pan, Yiwen

2018-01-01

With the increasing frequency of environmental risk accidents, more emphasis was placed on environmental risk assessment. In this article, the aftermath of an Environmental Risk Accident on Methanol Tank Leakage occurred on a cryogenic unit area in a certain oilfield processing plant have been mainly calculated and predicted. Major hazards were identified through the major hazards identification on dangerous chemicals, which could afterwards analyze maximum credible accident and confirm source item and the source intensity. In the end, the consequence of the accident has been calculated so that the impact on surrounding environment can be predicted after the accident.

High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization.

PubMed

Biryol, Derya; Nicolas, Chantel I; Wambaugh, John; Phillips, Katherine; Isaacs, Kristin

2017-11-01

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C 0 ) and chemical properties. The most predictive variables in the resulting model were C 0 , molecular weight, log K ow , and food type (R 2 =0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C 0 based on the functional role of chemicals in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R 2 =0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations

EPA'S TOXCAST PROGRAM FOR PREDICTING HAZARD AND PRIORITIZING TOXICITY TESTING OF ENVIRONMENTAL CHEMICALS

EPA Science Inventory

EPA is developing methods for utilizing computational chemistry, high-throughput screening (HTS) and various toxicogenomic technologies to predict potential for toxicity and prioritize limited testing resources towards chemicals that likely represent the greatest hazard to human ...

Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

PubMed

Cao, Qi; Leung, K M

2014-09-22

Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

From consumption to harvest: Environmental fate prediction of excreted ionizable trace organic chemicals.

PubMed

Polesel, Fabio; Plósz, Benedek Gy; Trapp, Stefan

2015-11-01

Excreted trace organic chemicals, e.g., pharmaceuticals and biocides, typically undergo incomplete elimination in municipal wastewater treatment plants (WWTPs) and are released to surface water via treated effluents and to agricultural soils through sludge amendment and/or irrigation with freshwater or reclaimed wastewater. Recent research has shown the tendency for these substances to accumulate in food crops. In this study, we developed and applied a simulation tool to predict the fate of three ionizable trace chemicals (triclosan-TCS, furosemide-FUR, ciprofloxacin-CIP) from human consumption/excretion up to the accumulation in soil and plant, following field amendment with sewage sludge or irrigation with river water (assuming dilution of WWTP effluent). The simulation tool combines the SimpleTreat model modified for fate prediction of ionizable chemicals in a generic WWTP and a recently developed dynamic soil-plant uptake model. The simulation tool was tested using country-specific (e.g., consumption/emission rates, precipitation and temperature) input data. A Monte Carlo-based approach was adopted to account for the uncertainty associated to physico-chemical and biokinetic model parameters. Results obtained in this study suggest significant accumulation of TCS and CIP in sewage sludge (1.4-2.8 mg kgDW(-1)) as compared to FUR (0.02-0.11 mg kgDW(-1)). For the latter substance, more than half of the influent load (60.1%-72.5%) was estimated to be discharged via WWTP effluent. Specific emission rates (g ha(-1) a(-1)) of FUR to soil via either sludge application or irrigation were up to 300 times lower than for TCS and CIP. Nevertheless, high translocation potential to wheat was predicted for FUR, reaching concentrations up to 4.3 μg kgDW(-1) in grain. Irrigation was found to enhance the relative translocation of FUR to plant (45.3%-48.9% of emission to soil), as compared to sludge application (21.9%-27.6%). A comparison with peer-reviewed literature showed

Computational Molecular Modeling for Evaluating the Toxicity of Environmental Chemicals: Prioritizing Bioassay Requirements

EPA Science Inventory

This commentary provides an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

PubMed Central

Wildenhain, Jan; Spitzer, Michaela; Dolma, Sonam; Jarvik, Nick; White, Rachel; Roy, Marcia; Griffiths, Emma; Bellows, David S.; Wright, Gerard D.; Tyers, Mike

2016-01-01

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery. PMID:27874849

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.

PubMed

Ng, Hui Wen; Doughty, Stephen W; Luo, Heng; Ye, Hao; Ge, Weigong; Tong, Weida; Hong, Huixiao

2015-12-21

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Assessing risks and preventing disease from environmental chemicals.

PubMed

Dunnette, D A

1989-01-01

In the last 25 years there has been considerable concern expressed about the extent to which chemical agents in the ambient and work environments are contributing to the causation of disease. This concern is a logical extension of our increased knowledge of the real and potential effects of environmental chemicals and the methodological difficulties in applying new knowledge that could help prevent environmentally induced disease. Chemical risk assessment offers an approach to estimating risks and involves consideration of relevant information including identification of chemical hazards, evaluation of the dose-response relationship, estimation of exposure and finally, risk characterization. Particularly significant uncertainties which are inherent in use of this and other risk models include animal-human and low dose-high dose extrapolation and estimation of exposure. Community public health risks from exposure to environmental chemicals appear to be small relative to other public health risks based on information related to cancer trends, dietary intake of synthetic chemicals, assessment data on substances such as DDT and "dioxin," public health effects of hazardous waste sites and contextual considerations. Because of inherent uncertainty in the chemical risk assessment process, however, we need to apply what methods are available in our efforts to prevent disease induced by environmental chemicals. There are a number of societal strategies which can contribute to overall reduction of risk from environmental chemicals. These include acquisition of information on environmental risk including toxicity, intensity and extensity of exposure, biological monitoring, disease surveillance, improvement in epidemiological methods, control of environmental chemical exposures, and dissemination of hazardous chemical information. Responsible environmental risk communication and information transfer appear to be among the most important of the available strategies for

Environmental Chemicals and Aging.

PubMed

Pearson, Brandon L; Ehninger, Dan

2017-03-01

Innovations in agriculture and medicine as well as industrial and domestic technologies are essential for the growing and aging global population. These advances generally require the use of novel natural or synthetic chemical agents with the potential to affect human health. Here, we attempt to highlight environmental chemicals and select drugs with the potential to exacerbate aging by directly affecting molecular aging cascades focusing particular attention on the brain. Finally, we call attention to some potential fruitful areas of research, particularly with advanced molecular profiling that could aid in prevention or mitigation of environmental chemical toxic influences in the periphery and the brain. We briefly summarize new research and highlight a recent study designed to prospectively identify agrochemicals with the potential to induce neurological diseases and place these discoveries into the already rich neurodegeneration and aging literature. Collectively, the research reviewed briefly here highlight chemicals with the true potential to accelerate aging, particularly in the brain, by eliciting elevated free radical stress and mitochondrial dysfunction. We make general recommendations about improved methodological approaches toward identification and regulation of chemicals that are gerontogenic to the brain.

Predicting the bioconcentration factor of highly hydrophobic organic chemicals.

PubMed

Garg, Rajni; Smith, Carr J

2014-07-01

Bioconcentration refers to the process of uptake and buildup of chemicals in living organisms. Experimental measurement of bioconcentration factor (BCF) is time-consuming and expensive, and is not feasible for a large number of chemicals of regulatory concern. Quantitative structure-activity relationship (QSAR) models are used for estimating BCF values to help in risk assessment of a chemical. This paper presents the results of a QSAR study conducted to address an important problem encountered in the prediction of the BCF of highly hydrophobic chemicals. A new QSAR model is derived using a dataset of diverse organic chemicals previously tested in a United States Environmental Protection Agency laboratory. It is noted that the linear relationship between the BCF and hydrophobic parameter, i.e., calculated octanol-water partition coefficient (ClogP), breaks down for highly hydrophobic chemicals. The parabolic QSAR equation, log BCF=3.036 ClogP-0.197 ClogP(2)-0.808 MgVol (n=28, r(2)=0.817, q(2)=0.761, s=0.558) (experimental log BCF range=0.44-5.29, ClogP range=3.16-11.27), suggests that a non-linear relationship between BCF and the hydrophobic parameter, along with inclusion of additional molecular size, weight and/or volume parameters, should be considered while developing a QSAR model for more reliable prediction of the BCF of highly hydrophobic chemicals. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Essential Set of Molecular Descriptors for ADME Prediction in Drug and Environmental Chemical Space

EPA Science Inventory

Historically, the disciplines of pharmacology and toxicology have embraced quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR) to predict ADME properties or biological activities of untested chemicals. The question arises ...

Sea-dumped chemical weapons: environmental risk, occupational hazard.

PubMed

Greenberg, M I; Sexton, K J; Vearrier, D

2016-01-01

Chemical weapons dumped into the ocean for disposal in the twentieth century pose a continuing environmental and human health risk. In this review we discuss locations, quantity, and types of sea-dumped chemical weapons, related environmental concerns, and human encounters with sea-dumped chemical weapons. We utilized the Ovid (http://ovidsp.tx.ovid.com) and PubMed (http://www.pubmed.org) search engines to perform MEDLINE searches for the terms 'sea-dumped chemical weapons', 'chemical warfare agents', and 'chemical munitions'. The searches returned 5863 articles. Irrelevant and non-English articles were excluded. A review of the references for these articles yielded additional relevant sources, with a total of 64 peer-reviewed articles cited in this paper. History and geography of chemical weapons dumping at sea: Hundreds of thousands of tons of chemical munitions were disposed off at sea following World War II. European, Russian, Japanese, and United States coasts are the areas most affected worldwide. Several areas in the Baltic and North Seas suffered concentrated large levels of dumping, and these appear to be the world's most studied chemical warfare agent marine dumping areas. Chemical warfare agents: Sulfur mustard, Lewisite, and the nerve agents appear to be the chemical warfare agents most frequently disposed off at sea. Multiple other type of agents including organoarsenicals, blood agents, choking agents, and lacrimators were dumped at sea, although in lesser volumes. Environmental concerns: Numerous geohydrologic variables contribute to the rate of release of chemical agents from their original casings, leading to difficult and inexact modeling of risk of release into seawater. Sulfur mustard and the organoarsenicals are the most environmentally persistent dumped chemical agents. Sulfur mustard in particular has a propensity to form a solid or semi-solid lump with a polymer coating of breakdown products, and can persist in this state on the ocean floor

WORKSHOP ON ENVIRONMENTALLY CONSCIOUS CHEMICAL PROCESS DESIGN

EPA Science Inventory

To encourage the consideration of environmental issues during chemical process design, the USEPA has developed techniques and software tools to evaluate the relative environmental impact of a chemical process. These techniques and tools aid in the risk management process by focus...

Prioritizing Environmental Chemicals for Obesity and Diabetes ...

EPA Pesticide Factsheets

Background: Diabetes and obesity are major threats to public health in the US and abroad. Understanding the role chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is a challenge. This review is intended to help researchers generate hypotheses about chemicals potentially contributing to diabetes and obesity-related health outcomes by summarizing relevant findings from the US Environmental Protection Agency (EPA) ToxCast high-throughput screening (HTS) program. Objectives: To develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high throughput screening data. Methods: Identify ToxCast assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and beta cell function, adipocyte dierentiation, and feeding behavior) and present chemical screening data against those assay targets to identify chemicals of potential interest. Discussion: Results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated from research papers and reviews published in the peer-reviewed literature. Testing of hypotheses based on ToxCast data will a

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Environmental Prediction Environmental Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP

The US EPAs ToxCast Program for the Prioritization and Prediction of Environmental Chemical Toxicity

EPA Science Inventory

To meet the need for evaluating large numbers of chemicals for potential toxicity, the U.S. Environmental Protection Agency has initiated a research project call ToxCast that makes use of recent advances in molecular biology and high-throughput screening. These technologies have ...

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

EPA Science Inventory

There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the federal Tox21 research progra...

The Toxicity Data Landscape for Environmental Chemicals

PubMed Central

Judson, Richard; Richard, Ann; Dix, David J.; Houck, Keith; Martin, Matthew; Kavlock, Robert; Dellarco, Vicki; Henry, Tala; Holderman, Todd; Sayre, Philip; Tan, Shirlee; Carpenter, Thomas; Smith, Edwin

2009-01-01

Objective Thousands of chemicals are in common use, but only a portion of them have undergone significant toxicologic evaluation, leading to the need to prioritize the remainder for targeted testing. To address this issue, the U.S. Environmental Protection Agency (EPA) and other organizations are developing chemical screening and prioritization programs. As part of these efforts, it is important to catalog, from widely dispersed sources, the toxicology information that is available. The main objective of this analysis is to define a list of environmental chemicals that are candidates for the U.S. EPA screening and prioritization process, and to catalog the available toxicology information. Data sources We are developing ACToR (Aggregated Computational Toxicology Resource), which combines information for hundreds of thousands of chemicals from > 200 public sources, including the U.S. EPA, National Institutes of Health, Food and Drug Administration, corresponding agencies in Canada, Europe, and Japan, and academic sources. Data extraction ACToR contains chemical structure information; physical–chemical properties; in vitro assay data; tabular in vivo data; summary toxicology calls (e.g., a statement that a chemical is considered to be a human carcinogen); and links to online toxicology summaries. Here, we use data from ACToR to assess the toxicity data landscape for environmental chemicals. Data synthesis We show results for a set of 9,912 environmental chemicals being considered for analysis as part of the U.S. EPA ToxCast screening and prioritization program. These include high-and medium-production-volume chemicals, pesticide active and inert ingredients, and drinking water contaminants. Conclusions Approximately two-thirds of these chemicals have at least limited toxicity summaries available. About one-quarter have been assessed in at least one highly curated toxicology evaluation database such as the U.S. EPA Toxicology Reference Database, U.S. EPA Integrated

Predicting ready biodegradability of premanufacture notice chemicals.

PubMed

Boethling, Robert S; Lynch, David G; Thom, Gary C

2003-04-01

Chemical substances other than pesticides, drugs, and food additives are regulated by the U.S. Environmental Protection Agency (U.S. EPA) under the Toxic Substances Control Act (TSCA), but the United States does not require that new substances be tested automatically for such critical properties as biodegradability. The resulting lack of submitted data has fostered the development of estimation methods, and the BioWIN models for predicting biodegradability from chemical structure have played a prominent role in premanufacture notice (PMN) review. Until now, validation efforts have used only the Japanese Ministry of International Trade and Industry (MITI) test data and have not included all models. To assess BioWIN performance with PMN substances, we assembled a database of PMNs for which ready biodegradation data had been submitted over the period 1995 through 2001. The 305 PMN structures are highly varied and pose major challenges to chemical property estimation. Despite the variability of ready biodegradation tests, the use of at least six different test methods, and widely varying quality of submitted data, accuracy of four of six BioWIN models (MITI linear, MITI nonlinear, survey ultimate, survey primary) was in the 80+% range for predicting ready biodegradability. Greater accuracy (>90%) can be achieved by using model estimates only when the four models agree (true for 3/4 of the PMNs). The BioWIN linear and nonlinear probability models did not perform as well even when classification criteria were optimized. The results suggest that the MITI and survey BioWIN models are suitable for use in screening-level applications.

Toxicokinetic Triage for Environmental Chemicals | Science ...

EPA Pesticide Factsheets

Toxicokinetic (TK) models are essential for linking administered doses to blood and tissue concentrations. In vitro-to-in vivo extrapolation (IVIVE) methods have been developed to determine TK from limited in vitro measurements and chemical structure-based property predictions, providing a less resource–intensive alternative to traditional in vivo TK approaches. High throughput TK (HTTK) methods use IVIVE to estimate doses that produce steady-state plasma concentrations equivalent to those producing biological activity in in vitro screening studies (e.g., ToxCast). In this study, the domain of applicability and assumptions of HTTK approaches were evaluated using both in vivo data and simulation analysis. Based on in vivo data for 87 chemicals, specific properties (e.g., in vitro HTTK data, physico-chemical descriptors, chemical structure, and predicted transporter affinities) were identified that correlate with poor HTTK predictive ability. For 350 xenobiotics with literature HTTK data, we then differentiated those xenobiotics for which HTTK approaches are likely to be sufficient, from those that may require additional data. For 272 chemicals we also developed a HT physiologically-based TK (HTPBTK) model that requires somewhat greater information than a steady-state model, but allows non-steady state dynamics and can predict chemical concentration time-courses for a variety of exposure scenarios, tissues, and species. We used this HTPBTK model to show that the

Predictive performance of the Vitrigel‐eye irritancy test method using 118 chemicals

PubMed Central

Yamaguchi, Hiroyuki; Kojima, Hajime

2015-01-01

Abstract We recently developed a novel Vitrigel‐eye irritancy test (EIT) method. The Vitrigel‐EIT method is composed of two parts, i.e., the construction of a human corneal epithelium (HCE) model in a collagen vitrigel membrane chamber and the prediction of eye irritancy by analyzing the time‐dependent profile of transepithelial electrical resistance values for 3 min after exposing a chemical to the HCE model. In this study, we estimated the predictive performance of Vitrigel‐EIT method by testing a total of 118 chemicals. The category determined by the Vitrigel‐EIT method in comparison to the globally harmonized system classification revealed that the sensitivity, specificity and accuracy were 90.1%, 65.9% and 80.5%, respectively. Here, five of seven false‐negative chemicals were acidic chemicals inducing the irregular rising of transepithelial electrical resistance values. In case of eliminating the test chemical solutions showing pH 5 or lower, the sensitivity, specificity and accuracy were improved to 96.8%, 67.4% and 84.4%, respectively. Meanwhile, nine of 16 false‐positive chemicals were classified irritant by the US Environmental Protection Agency. In addition, the disappearance of ZO‐1, a tight junction‐associated protein and MUC1, a cell membrane‐spanning mucin was immunohistologically confirmed in the HCE models after exposing not only eye irritant chemicals but also false‐positive chemicals, suggesting that such false‐positive chemicals have an eye irritant potential. These data demonstrated that the Vitrigel‐EIT method could provide excellent predictive performance to judge the widespread eye irritancy, including very mild irritant chemicals. We hope that the Vitrigel‐EIT method contributes to the development of safe commodity chemicals. Copyright © 2015 The Authors. Journal of Applied Toxicology published by John Wiley & Sons Ltd. PMID:26472347

Predictive performance of the Vitrigel-eye irritancy test method using 118 chemicals.

PubMed

Yamaguchi, Hiroyuki; Kojima, Hajime; Takezawa, Toshiaki

2016-08-01

We recently developed a novel Vitrigel-eye irritancy test (EIT) method. The Vitrigel-EIT method is composed of two parts, i.e., the construction of a human corneal epithelium (HCE) model in a collagen vitrigel membrane chamber and the prediction of eye irritancy by analyzing the time-dependent profile of transepithelial electrical resistance values for 3 min after exposing a chemical to the HCE model. In this study, we estimated the predictive performance of Vitrigel-EIT method by testing a total of 118 chemicals. The category determined by the Vitrigel-EIT method in comparison to the globally harmonized system classification revealed that the sensitivity, specificity and accuracy were 90.1%, 65.9% and 80.5%, respectively. Here, five of seven false-negative chemicals were acidic chemicals inducing the irregular rising of transepithelial electrical resistance values. In case of eliminating the test chemical solutions showing pH 5 or lower, the sensitivity, specificity and accuracy were improved to 96.8%, 67.4% and 84.4%, respectively. Meanwhile, nine of 16 false-positive chemicals were classified irritant by the US Environmental Protection Agency. In addition, the disappearance of ZO-1, a tight junction-associated protein and MUC1, a cell membrane-spanning mucin was immunohistologically confirmed in the HCE models after exposing not only eye irritant chemicals but also false-positive chemicals, suggesting that such false-positive chemicals have an eye irritant potential. These data demonstrated that the Vitrigel-EIT method could provide excellent predictive performance to judge the widespread eye irritancy, including very mild irritant chemicals. We hope that the Vitrigel-EIT method contributes to the development of safe commodity chemicals. Copyright © 2015 The Authors. Journal of Applied Toxicology published by John Wiley & Sons Ltd. Copyright © 2015 The Authors. Journal of Applied Toxicology published by John Wiley & Sons Ltd.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

High throughput heuristics for prioritizing human exposure to environmental chemicals.

PubMed

Wambaugh, John F; Wang, Anran; Dionisio, Kathie L; Frame, Alicia; Egeghy, Peter; Judson, Richard; Setzer, R Woodrow

2014-11-04

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.

Activation of CAR and PXR by Dietary, Environmental and Occupational Chemicals Alters Drug Metabolism, Intermediary Metabolism, and Cell Proliferation

PubMed Central

Hernandez, J.P.; Mota, L.C.; Baldwin, W.S.

2010-01-01

The constitutive androstane receptor (CAR) and the pregnane × receptor (PXR) are activated by a variety of endogenous and exogenous ligands, such as steroid hormones, bile acids, pharmaceuticals, and environmental, dietary, and occupational chemicals. In turn, they induce phase I–III detoxification enzymes and transporters that help eliminate these chemicals. Because many of the chemicals that activate CAR and PXR are environmentally-relevant (dietary and anthropogenic), studies need to address whether these chemicals or mixtures of these chemicals may increase the susceptibility to adverse drug interactions. In addition, CAR and PXR are involved in hepatic proliferation, intermediary metabolism, and protection from cholestasis. Therefore, activation of CAR and PXR may have a wide variety of implications for personalized medicine through physiological effects on metabolism and cell proliferation; some beneficial and others adverse. Identifying the chemicals that activate these promiscuous nuclear receptors and understanding how these chemicals may act in concert will help us predict adverse drug reactions (ADRs), predict cholestasis and steatosis, and regulate intermediary metabolism. This review summarizes the available data on CAR and PXR, including the environmental chemicals that activate these receptors, the genes they control, and the physiological processes that are perturbed or depend on CAR and PXR action. This knowledge contributes to a foundation that will be necessary to discern interindividual differences in the downstream biological pathways regulated by these key nuclear receptors. PMID:20871735

ENVIRONMENTAL ENGINEERING AND ENDOCRINE DISRUPTING CHEMICALS

EPA Science Inventory

Endocrine disruptors are a class of chemicals of growing interest to the environmental community. USEPA's Risk Assessment Forum defined an endocrine disrupting chemical (EDC) as "an exogenous agent that interferes with the synthesis, secretion, transport, binding, action, or elim...

From laboratory to environmental conditions: a new approach for chemical's biodegradability assessment.

PubMed

François, Brillet; Armand, Maul; Marie-José, Durand; Thouand, Gérald

2016-09-01

With thousands of organic chemicals released every day into our environment, Europe and other continents are confronted with increased risk of health and environmental problems. Even if a strict regulation such as REgistration, Authorization and restriction of CHemicals (REACH) is imposed and followed by industry to ensure that they prove the harmlessness of their substances, not all testing procedures are designed to cope with the complexity of the environment. This is especially true for the evaluation of persistence through biodegradability assessment guidelines. Our new approach has been to adapt "in the lab" biodegradability assessment to the environmental conditions and model the probability for a biodegradation test to be positive in the form of a logistic function of both the temperature and the viable cell density. Here, a proof of this new concept is proposed with the establishment of tri-dimensional biodegradability profiles of six chemicals (sodium benzoate, 4-nitrophenol, diethylene glycol, 2,4,5-trichlorophenol, atrazine, and glyphosate) between 4 to 30 °C and 10(4) to 10(8) cells ml(-1) as can be found in environmental compartments in time and space. The results show a significant increase of the predictive power of existing screening lab-scale tests designed for soluble substances. This strategy can be complementary to those current testing strategies with the creation of new indicators to quantify environmental persistence using lab-scale tests.

Chemical stimulation in unconventional hydrocarbons extraction in the USA: a preliminary environmental risk assessment.

NASA Astrophysics Data System (ADS)

Sutra, Emilie; Spada, Matteo; Burgherr, Peter

2016-04-01

While the exploitation of unconventional resources recently shows an extensive development, the stimulation techniques in use in this domain arouse growing public concerns. Often in the shadow of the disputed hydraulic fracturing process, the matrix acidizing is however a complementary or alternative procedure to enhance the reservoir connectivity. Although acidizing processes are widespread within the traditional hydrocarbons sources exploration, the matrix acidizing does not appear to be commonly used in unconventional hydrocarbons formations due to their low permeability. Nonetheless, this process has been recently applied to the Monterey formation, a shale oil play in California. These stimulation fluids are composed by various chemicals, what represents a matter of concern for public as well as for authorities. As a consequence, a risk assessment implying an exposure and toxicity analysis is needed. Focusing on site surface accidents, e.g., leak of a chemical from a storage tank, we develop in this study concentration scenarios for different exposure pathways to estimate the potential environmental risk associated with the use of specific hazardous substances in the matrix acidizing process for unconventional hydrocarbon reservoirs in the USA. Primary, information about the usage of different hazardous substances have been collected in order to extract the most frequently used chemicals. Afterwards, a probabilistic estimation of the environmental risk associated with the use of these chemicals is carried out by comparing the Predicted Environmental Concentrations (PEC) distribution with the Predicted No Effect Concentrations (PNEC) value. The latter is collected from a literature review, whereas the PEC is estimated as probability distribution concentrations in different environmental compartments (e.g., soil) built upon various predefined accident scenarios. By applying a probabilistic methodology for the concentrations, the level at which the used chemicals

Predicting bioconcentration of chemicals into vegetation from soil or air using the molecular connectivity index

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowdy, D.L.; McKone, T.E.; Hsieh, D.P.H.

1995-12-31

Bioconcentration factors (BCFs) are the ratio of chemical concentration found in an exposed organism (in this case a plant) to the concentration in an air or soil exposure medium. The authors examine here the use of molecular connectivity indices (MCIs) as quantitative structure-activity relationships (QSARS) for predicting BCFs for organic chemicals between plants and air or soil. The authors compare the reliability of the octanol-air partition coefficient (K{sub oa}) to the MC based prediction method for predicting plant/air partition coefficients. The authors also compare the reliability of the octanol/water partition coefficient (K{sub ow}) to the MC based prediction method formore » predicting plant/soil partition coefficients. The results here indicate that, relative to the use of K{sub ow} or K{sub oa} as predictors of BCFs the MC can substantially increase the reliability with which BCFs can be estimated. The authors find that the MC provides a relatively precise and accurate method for predicting the potential biotransfer of a chemical from environmental media into plants. In addition, the MC is much faster and more cost effective than direct measurements.« less

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

Chemical Genomics Profiling of Environmental Chemical Modulation of Human Nuclear Receptors

EPA Science Inventory

The large and increasing number of chemicals released into the environment demand more efficient and cost effective approaches for assessing environmental chemical toxicity. The U.S. Tox21 program has responded to this challenge by proposing alternative strategies for toxicity te...

Finding and estimating chemical property data for environmental assessment.

PubMed

Boethling, Robert S; Howard, Philip H; Meylan, William M

2004-10-01

The ability to predict the behavior of a chemical substance in a biological or environmental system largely depends on knowledge of the physicochemical properties and reactivity of that substance. We focus here on properties, with the objective of providing practical guidance for finding measured values and using estimation methods when necessary. Because currently available computer software often makes it more convenient to estimate than to retrieve measured values, we try to discourage irrational exuberance for these tools by including comprehensive lists of Internet and hard-copy data resources. Guidance for assessors is presented in the form of a process to obtain data that includes establishment of chemical identity, identification of data sources, assessment of accuracy and reliability, substructure searching for analogs when experimental data are unavailable, and estimation from chemical structure. Regarding property estimation, we cover estimation from close structural analogs in addition to broadly applicable methods requiring only the chemical structure. For the latter, we list and briefly discuss the most widely used methods. Concluding thoughts are offered concerning appropriate directions for future work on estimation methods, again with an emphasis on practical applications.

Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells.

EPA Science Inventory

Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells S. Hunter, M. Rosen, M. Hoopes, H. Nichols, S. Jeffay, K. Chandler1, Integrated Systems Toxicology Division, National Health and Environmental Effects Research Labor...

Development of an integrated chemical weather prediction system for environmental applications at meso to global scales: NMMB/BSC-CHEM

NASA Astrophysics Data System (ADS)

Jorba, O.; Pérez, C.; Karsten, K.; Janjic, Z.; Dabdub, D.; Baldasano, J. M.

2009-09-01

This contribution presents the ongoing developments of a new fully on-line chemical weather prediction system for meso to global scale applications. The modeling system consists of a mineral dust module and a gas-phase chemistry module coupled on-line to a unified global-regional atmospheric driver. This approach allows solving small scale processes and their interactions at local to global scales. Its unified environment maintains the consistency of all the physico-chemical processes involved. The atmospheric driver is the NCEP/NMMB numerical weather prediction model (Janjic and Black, 2007) developed at National Centers for Environmental Prediction (NCEP). It represents an evolution of the operational WRF-NMME model extending from meso to global scales. Its unified non-hydrostatic dynamical core supports regional and global simulations. The Barcelona Supercomputing Center is currently designing and implementing a chemistry transport model coupled online with the new global/regional NMMB. The new modeling system is intended to be a powerful tool for research and to provide efficient global and regional chemical weather forecasts at sub-synoptic and mesoscale resolutions. The online coupling of the chemistry follows the approach similar to that of the mineral dust module already coupled to the atmospheric driver, NMMB/BSC-DUST (Pérez et al., 2008). Chemical species are advected and mixed at the corresponding time steps of the meteorological tracers using the same numerical scheme. Advection is eulerian, positive definite and monotone. The chemical mechanism and chemistry solver is based on the Kinetic PreProcessor KPP (Damian et al., 2002) package with the main purpose of maintaining a wide flexibility when configuring the model. Such approach will allow using a simplified chemical mechanism for global applications or a more complete mechanism for high-resolution local or regional studies. Moreover, it will permit the implementation of a specific configuration for

The Molecular Recognition Paradigm of Environmental Chemicals with Biomacromolecules.

PubMed

Zhang, Wenjing; Pan, Liumeng; Wang, Haifei; Lv, Xuan; Ding, Keke

2017-01-01

The interactions of ligands with biomacromolecules play a fundamental role in almost all bioprocesses occuring in living organisms. The binding of ligands can cause the conformational changes of biomacromolecules, possibly affecting their physiological functions. The interactions of ligands with biomacromolecules are thus becoming a research hotspot. However, till now, there still lacks a systematic compilation of review with the focus on the interactions between environmental chemicals and biomacromolecules. In this review, we focus on the molecular recognition paradigm of environmental chemicals with biomacromolecules and chemical basis for driving the complex formation. The state-of-the-art review on in vitro and in silico studies on interaction of organic chemicals with transport proteins, nuclear receptors and CYP450 enzymes was provided, and the enantioselective interactions of chiral environmental chemicals was also mentioned.

Predictions of Chemical Species via Diode Laser Spectroscopy

NASA Technical Reports Server (NTRS)

Chen, Shin-Juh; Silver, Joel A.; Dahm, Werner J. A.; Piltch, Nancy D.; Salzman, Jack (Technical Monitor)

2001-01-01

A technique to predict temperature and chemical species in flames from absorbance measurement of one chemical species is presented. Predicted temperature and mole fractions of methane and water agreed well with measured and published results.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post / National Weather Service National Centers for Environmental Prediction Environmental Modeling Center NOAA

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Weather Service National Centers for Environmental Prediction Environmental Modeling Center NOAA Center

Environmental benefits of chemical propulsion

NASA Technical Reports Server (NTRS)

Hayes, Joyce A.; Goldberg, Benjamin E.; Anderson, David M.

1995-01-01

This paper identifies the necessity of chemical propulsion to satellite usage and some of the benefits accrued through monitoring global resources and patterns, including the Global Climate Change Model (GCM). The paper also summarized how the satellite observations are used to affect national and international policies. Chemical propulsion, like all environmentally conscious industries, does provide limited, controlled pollutant sources through its manufacture and usage. However, chemical propulsion is the sole source which enables mankind to launch spacecraft and monitor the Earth. The information provided by remote sensing directly affects national and international policies designed to protect the environment and enhance the overall quality of life on Earth. The resultant of chemical propulsion is the capability to reduce overall pollutant emissions to the benefit of mankind.

Coal Extraction - Environmental Prediction

USGS Publications Warehouse

Cecil, C. Blaine; Tewalt, Susan J.

2002-01-01

Coal from the Appalachian region has supplied energy to the Nation for more than 200 years. Appalachian coal fueled America through a civil war and helped win two world wars. Appalachian coal has also provided fuel for keeping America warm in the winter and cool in the summer and has served as the basis for the steel, automobile, organic chemicals, chlorine, and aluminum industries. These benefits have not come without environmental costs, however. Coal extraction and utilization have had significant environmental impacts.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post / National Weather Service National Centers for Environmental Prediction Environmental Modeling Center NOAA

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Weather Service National Centers for Environmental Prediction Environmental Modeling Center NOAA Center

Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance

EPA Science Inventory

Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrat...

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Centers for Environmental Prediction Environmental Modeling Center NOAA Center for Weather and Climate

EFFECTS OF ENVIRONMENTAL CHEMICALS ON FETAL TESTES TESTOSTERONE PRODUCTION

EPA Science Inventory

Effects of Environmental Chemicals on Fetal Testes Testosterone Production

Lambright, CS , Wilson, VS , Furr, J, Wolf, CJ, Noriega, N, Gray, LE, Jr.
US EPA, ORD/NHEERL/RTD, RTP, NC

Exposure of pregnant rodents to certain environmental chemicals during criti...

In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sirenko, Oksana, E-mail: oksana.sirenko@moldev.com

An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30 min or 24 h and effects on cardiomyocyte beating andmore » cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca{sup 2+} flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of the tested chemicals exhibited effects on cardiomyocyte beating after 30 min of exposure. In contrast, after 24 h, effects on cell beating without concomitant cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes visualized using the Toxicological Prioritization Index (ToxPi) showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential at high exposure levels to alter cardiomyocyte function, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes

HUMAN HEALTH IMPACT OF ENVIRONMENTAL ESTROGENIC CHEMICALS

EPA Science Inventory

HUMAN HEALTH IMPACT OF ENVIRONMENTAL ESTROGENIC CHEMICALS.

Robert J. Kavlock, Reproductive Toxicology Division, NHEERL, ORD, US Environmental Protection Agency, Research Triangle Park, NC USA.

Over the past several decades a hypothesis has been put forth that a numb...

Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing

PubMed Central

Wetmore, Barbara A.; Wambaugh, John F.; Allen, Brittany; Ferguson, Stephen S.; Sochaski, Mark A.; Setzer, R. Woodrow; Houck, Keith A.; Strope, Cory L.; Cantwell, Katherine; Judson, Richard S.; LeCluyse, Edward; Clewell, Harvey J.; Thomas, Russell S.; Andersen, Melvin E.

2015-01-01

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs < 1 or < 100, respectively. Diverse bioactivities across a range of assays and concentrations were also noted across the wider chemical space surveyed. The availability of HT exposure estimation and bioactivity screening tools provides an opportunity to incorporate a risk-based strategy for use in testing prioritization. PMID:26251325

In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model

PubMed Central

Sirenko, Oksana; Grimm, Fabian A.; Ryan, Kristen R.; Iwata, Yasuhiro; Chiu, Weihsueh A.; Parham, Frederick; Wignall, Jessica A.; Anson, Blake; Cromwell, Evan F.; Behl, Mamta; Rusyn, Ivan; Tice, Raymond R.

2017-01-01

An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30 min or 24 hrs and effects on cardiomyocyte beating and cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca2+ flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of tested chemicals exhibited effects on cardiomyocyte rhythm after 30 min of exposure. After 24 hours, the effects on cell rhythm without cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes were visualized using Toxicological Prioritization Index (ToxPi) and showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential to alter cardiomyocyte function at high exposures, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes. PMID:28259702

OPERA models for predicting physicochemical properties and environmental fate endpoints.

PubMed

Mansouri, Kamel; Grulke, Chris M; Judson, Richard S; Williams, Antony J

2018-03-08

-source, command-line application called OPEn structure-activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency's CompTox Chemistry Dashboard.

Evaluating the environmental hazard of industrial chemicals from data collected during the REACH registration process.

PubMed

Gustavsson, Mikael B; Hellohf, Andreas; Backhaus, Thomas

2017-05-15

Registration dossiers for 11,678 industrial chemicals were retrieved from the database of the European Chemicals Agency, of which 3566 provided a numerical entry for the corresponding predicted no effect concentration for the freshwater environment (PNEC). A distribution-based examination of 2244 of these entries reveals that the average PNEC of an industrial chemical in Europe is 238nmol/L, covering a span of 9 orders of magnitude. A comparison with biocides, pesticides, pharmaceuticals and WFD-priority pollutants reveals that, in average, industrial chemicals are least hazardous (hazard ranking: industrial chemicals≪pharmaceuticalschemicals have a lower environmental threshold than the median pesticide and 73 have a lower environmental threshold than even the median biocide. Industrial chemicals produced and/or imported in higher tonnages have, on average, higher PNECs which most likely is due to the lower assessment factors used for the PNEC determination. This pattern indicates that the initial AF of 1000 comprises a measure of conservatism. The vast majority of PNEC values are driven by EC50 and NOEC data from tests with Daphnia magna. Tests with marine species are rarely provided for the hazard characterization of industrial chemicals. Copyright © 2017 Elsevier B.V. All rights reserved.

Environmental signaling: from environmental estrogens to endocrine-disrupting chemicals and beyond.

PubMed

McLachlan, J A

2016-07-01

The landmark report (Herbst et al. 1971) linking prenatal treatment with a synthetic estrogen, diethylstilbestrol (DES), to cancer at puberty in women whose mothers took the drug while pregnant ushered in an era of research on delayed effects of such exposures on functional outcomes in offspring. An animal model developed in our laboratory at the National Institute of Environmental Health Sciences confirmed that DES was the carcinogen and exposure to DES caused, as well, functional alterations in the reproductive, endocrine, and immune systems of male and female mice treated in utero. DES was also being used in agriculture and we discovered, at the first meeting on Estrogens in the Environment in 1979 (Estrogens in the Environment, 1980), that many environmental contaminants were also estrogenic. Many laboratories sought to discern the basis for estrogenicity in environmental chemicals and to discover other hormonally active xenobiotics. Our laboratory elucidated how DES and other estrogenic compounds worked by altering differentiation through epigenetic gene imprinting, helping explain the transgenerational effects found in mice and humans. At the Wingspread Conference on the Human-Wildlife Connection in 1991 (Advances in Modern Environmental Toxicology, 1992), we learned that environmental disruption of the endocrine system occurred in many species and phyla, and the term endocrine disruption was introduced. Further findings of transgenerational effects of environmental agents that mimicked or blocked various reproductive hormones and the ubiquity of environmental signals, such as bisphenol A increased concern for human and ecological health. Scientists began to look at other endocrine system aspects, such as cardiovascular and immune function, and other nuclear receptors, with important observations regarding obesity and metabolism. Laboratories, such as ours, are now using stem cells to try to understand the mechanisms by which various environmental signals

EVALUATING AND DESIGNING CHEMICAL PROCESSES FOR ENVIRONMENTAL SUSTAINABILITY

EPA Science Inventory

Chemicals and chemical processes are at the heart of most environmental problems. This isn't surprising since chemicals make up all of the products we use in our lives. The common use of cjhemicals makes them of high interest for systems analysis, particularly because of environ...

Multiple Classes of Environmental Chemicals are Associated ...

EPA Pesticide Factsheets

Biomonitoring of human tissues and fluids has shown that virtually all individuals, worldwide, carry a “body burden” of synthetic chemicals (Thornton et al. 2002; CDC 2009). Although the measurement of an environmental chemical in a person’s tissues or fluids is an indication of exposure, it does not by itself mean that the chemical or the exposure concentration is sufficient to cause a disease or an adverse effect. However, since humans are exposed to multiple chemicals, there may be a combination effect (e.g., additive, synergistic) on health risks associated with exposure even at low levels (Kortenkamp 2008). Further, biomonitoring studies show that humans carry a body burden of multiple classes of contaminants which are often not studied together. We used the 2003-2006 National Health and Nutrition Examination Survey to evaluate the relationship between alanine aminotransferase (ALT) and 53 environmental contaminants across six classes (metals; perfluorinated compounds [PFCs]; phthalates; phenols; coplanar and non-dioxin-like polychlorinated biphenyls [PCBs]) using a novel method.

Using ToxCast in vitro Assays in the Hierarchical Quantitative Structure-Activity Relationship (QSAR) Modeling for Predicting in vivo Toxicity of Chemicals

EPA Science Inventory

The goal of chemical toxicology research is utilizing short term bioassays and/or robust computational methods to predict in vivo toxicity endpoints for chemicals. The ToxCast program established at the US Environmental Protection Agency (EPA) is addressing this goal by using ca....

Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

PubMed

Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

2018-03-01

Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar conducts a program of research and development in support of the National Centers for Environmental Prediction (NCEP) operational forecasting mission for global prediction. This research and development in

Predicting biological effects of environmental mixtures using exposure:activity ratios (EAR) derived from US EPA’s ToxCast data: Retrospective application to chemical monitoring data

EPA Science Inventory

Chemical monitoring has been widely used in environmental surveillance to assess exposure to environmental contaminants which could represent potential hazards to exposed organisms. However, the ability to detect chemicals in the environment has rapidly outpaced assessment of pot...

Environmental and Chemical Aging of Fatty-Acid-Based Vinyl Ester Composites

DTIC Science & Technology

2011-04-01

Environmental and Chemical Aging of Fatty- Acid -Based Vinyl Ester Composites by Steven E. Boyd and John J. La Scala ARL-TR-5523 April...2011 Environmental and Chemical Aging of Fatty- Acid -Based Vinyl Ester Composites Steven E. Boyd and John J. La Scala Weapons and Materials...COVERED (From - To) October 2009–September 2010 4. TITLE AND SUBTITLE Environmental and Chemical Aging of Fatty- Acid -Based Vinyl Ester Composites

Variation in Chemical Defense Among Natural Populations of Common Toad, Bufo bufo, Tadpoles: the Role of Environmental Factors.

PubMed

Bókony, Veronika; Móricz, Ágnes M; Tóth, Zsófia; Gál, Zoltán; Kurali, Anikó; Mikó, Zsanett; Pásztor, Katalin; Szederkényi, Márk; Tóth, Zoltán; Ujszegi, János; Üveges, Bálint; Krüzselyi, Dániel; Capon, Robert J; Hoi, Herbert; Hettyey, Attila

2016-04-01

Defensive toxins are widespread in nature, yet we know little about how various environmental factors shape the evolution of chemical defense, especially in vertebrates. In this study we investigated the natural variation in the amount and composition of bufadienolide toxins, and the relative importance of ecological factors in predicting that variation, in larvae of the common toad, Bufo bufo, an amphibian that produces toxins de novo. We found that tadpoles' toxin content varied markedly among populations, and the number of compounds per tadpole also differed between two geographical regions. The most consistent predictor of toxicity was the strength of competition, indicating that tadpoles produced more compounds and larger amounts of toxins when coexisting with more competitors. Additionally, tadpoles tended to contain larger concentrations of bufadienolides in ponds that were less prone to desiccation, suggesting that the costs of toxin production can only be afforded by tadpoles that do not need to drastically speed up their development. Interestingly, this trade-off was not alleviated by higher food abundance, as periphyton biomass had negligible effect on chemical defense. Even more surprisingly, we found no evidence that higher predation risk enhances chemical defenses, suggesting that low predictability of predation risk and high mortality cost of low toxicity might select for constitutive expression of chemical defense irrespective of the actual level of predation risk. Our findings highlight that the variation in chemical defense may be influenced by environmental heterogeneity in both the need for, and constraints on, toxicity as predicted by optimal defense theory.

In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model.

PubMed

Sirenko, Oksana; Grimm, Fabian A; Ryan, Kristen R; Iwata, Yasuhiro; Chiu, Weihsueh A; Parham, Frederick; Wignall, Jessica A; Anson, Blake; Cromwell, Evan F; Behl, Mamta; Rusyn, Ivan; Tice, Raymond R

2017-05-01

An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30min or 24h and effects on cardiomyocyte beating and cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca 2+ flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of the tested chemicals exhibited effects on cardiomyocyte beating after 30min of exposure. In contrast, after 24h, effects on cell beating without concomitant cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes visualized using the Toxicological Prioritization Index (ToxPi) showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential at high exposure levels to alter cardiomyocyte function, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes. Copyright © 2017

REPORTING NEEDS FOR STUDIES OF ENVIRONMENTAL CHEMICALS IN HUMAN MILK

EPA Science Inventory

Studies of environmental chemicals in human milk have been carried out in many countries, but few have been conducted in the U.S. These studies are useful for monitoring populations trends in exposure to chemiclas, for research in the determinants of environmental chemicals in m...

The effects of environmental chemicals on renal function.

PubMed

Kataria, Anglina; Trasande, Leonardo; Trachtman, Howard

2015-10-01

The global incidence of chronic kidney disease (CKD) is increasing among individuals of all ages. Despite advances in proteomics, genomics and metabolomics, there remains a lack of safe and effective drugs to reverse or stabilize renal function in patients with glomerular or tubulointerstitial causes of CKD. Consequently, modifiable risk factors that are associated with a progressive decline in kidney function need to be identified. Numerous reports have documented the adverse effects that occur in response to graded exposure to a wide range of environmental chemicals. This Review summarizes the effects of such chemicals on four aspects of cardiorenal function: albuminuria, glomerular filtration rate, blood pressure and serum uric acid concentration. We focus on compounds that individuals are likely to be exposed to as a consequence of normal consumer activities or medical treatment, namely phthalates, bisphenol A, polyfluorinated alkyl acids, dioxins and furans, polycyclic aromatic hydrocarbons and polychlorinated biphenyls. Environmental exposure to these chemicals during everyday life could have adverse consequences on renal function and might contribute to progressive cumulative renal injury over a lifetime. Regulatory efforts should be made to limit individual exposure to environmental chemicals in an attempt to reduce the incidence of cardiorenal disease.

The effects of environmental chemicals on renal function

PubMed Central

Kataria, Anglina; Trasande, Leonardo; Trachtman, Howard

2015-01-01

The global incidence of chronic kidney disease (CKD) is increasing among individuals of all ages. Despite advances in proteomics, genomics and metabolomics, there remains a lack of safe and effective drugs to reverse or stabilize renal function in patients with glomerular or tubulointerstitial causes of CKD. Consequently, modifiable risk factors that are associated with a progressive decline in kidney function need to be identified. Numerous reports have documented the adverse effects that occur in response to graded exposure to a wide range of environmental chemicals. This Review summarizes the effects of such chemicals on four aspects of cardiorenal function: albuminuria, glomerular filtration rate, blood pressure and serum uric acid concentration. We focus on compounds that individuals are likely to be exposed to as a consequence of normal consumer activities or medical treatment, namely phthalates, bisphenol A, polyfluorinated alkyl acids, dioxins and furans, polycyclic aromatic hydrocarbons and polychlorinated biphenyls. Environmental exposure to these chemicals during everyday life could have adverse consequences on renal function and might contribute to progressive cumulative renal injury over a lifetime. Regulatory efforts should be made to limit individual exposure to environmental chemicals in an attempt to reduce the incidence of cardiorenal disease. PMID:26100504

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

PubMed

Bedoya, David; Manolakos, Elias S; Novotny, Vladimir

2011-03-01

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local

Toxicokinetic Triage for Environmental Chemicals

EPA Science Inventory

Toxicokinetic (TK) models are essential for linking administered doses to blood and tissue concentrations. In vitro-to-in vivo extrapolation (IVIVE) methods have been developed to determine TK from limited in vitro measurements and chemical structure-based property predictions, p...

Environmental fate and exposure models: advances and challenges in 21st century chemical risk assessment.

PubMed

Di Guardo, Antonio; Gouin, Todd; MacLeod, Matthew; Scheringer, Martin

2018-01-24

Environmental fate and exposure models are a powerful means to integrate information on chemicals, their partitioning and degradation behaviour, the environmental scenario and the emissions in order to compile a picture of chemical distribution and fluxes in the multimedia environment. A 1995 pioneering book, resulting from a series of workshops among model developers and users, reported the main advantages and identified needs for research in the field of multimedia fate models. Considerable efforts were devoted to their improvement in the past 25 years and many aspects were refined; notably the inclusion of nanomaterials among the modelled substances, the development of models at different spatial and temporal scales, the estimation of chemical properties and emission data, the incorporation of additional environmental media and processes, the integration of sensitivity and uncertainty analysis in the simulations. However, some challenging issues remain and require research efforts and attention: the need of methods to estimate partition coefficients for polar and ionizable chemical in the environment, a better description of bioavailability in different environments as well as the requirement of injecting more ecological realism in exposure predictions to account for the diversity of ecosystem structures and functions in risk assessment. Finally, to transfer new scientific developments into the realm of regulatory risk assessment, we propose the formation of expert groups that compare, discuss and recommend model modifications and updates and help develop practical tools for risk assessment.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability

EPA Science Inventory

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores th...

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Products People GLOBAL CLIMATE & WEATHER MODELING Personnel Jordan Alpert Email Website Dave Behringer Prediction Environmental Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830

Prioritizing Environmental Chemicals for Obesity and Diabetes Outcomes Research: A Screening Approach Using ToxCast™ High-Throughput Data.

PubMed

Auerbach, Scott; Filer, Dayne; Reif, David; Walker, Vickie; Holloway, Alison C; Schlezinger, Jennifer; Srinivasan, Supriya; Svoboda, Daniel; Judson, Richard; Bucher, John R; Thayer, Kristina A

2016-08-01

Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is challenging. This review is intended to help researchers generate hypotheses about chemicals that may contribute to diabetes and to obesity-related health outcomes by summarizing relevant findings from the U.S. Environmental Protection Agency (EPA) ToxCast™ high-throughput screening (HTS) program. Our aim was to develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high-throughput screening data. We identified ToxCast™ assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and β cell function, adipocyte differentiation, and feeding behavior) and presented chemical screening data against those assay targets to identify chemicals of potential interest. The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature. Testing hypotheses based on ToxCast™ data will also help assess the predictive utility of this HTS platform. More research is required to put these screening-level analyses into context, but the information presented in this review should facilitate the development of new hypotheses. Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. 2016. Prioritizing environmental chemicals for obesity and diabetes outcomes research: a screening approach using ToxCast™ high-throughput data. Environ

CHEMICAL INDUCTION MIXER VERIFICATION - ENVIRONMENTAL TECHNOLOGY VERIFICATION PROGRAM

EPA Science Inventory

The Wet-Weather Flow Technologies Pilot of the Environmental Technology Verification (ETV) Program, which is supported by the U.S. Environmental Protection Agency and facilitated by NSF International, has recently evaluated the performance of chemical induction mixers used for di...

Progress of environmental management and risk assessment of industrial chemicals in China.

PubMed

Wang, Hong; Yan, Zhen-Guang; Li, Hong; Yang, Ni-Yun; Leung, Kenneth M Y; Wang, Yi-Zhe; Yu, Ruo-Zhen; Zhang, Lai; Wang, Wan-Hua; Jiao, Cong-Ying; Liu, Zheng-Tao

2012-06-01

With China's rapid economic growth, chemical-related environmental issues have become increasingly prominent, and the environmental management of chemicals has garnered increased attention from the government. This review focuses on the current situation and the application of risk assessment in China's environmental management of industrial chemicals. The related challenges and research needs of the country are also discussed. The Chinese government promulgated regulations for the import and export of toxic chemicals in 1994. Regulations for new chemical substances came into force in 2003, and were revised in 2010 based on the concept of risk management. In order to support the implementation of new regulations, Guidance for Risk Assessment of Chemicals is under development in an attempt to provide the concepts and techniques of risk assessment. With increasing concern and financial support from Chinese government, China is embarking on the fast track of research and development in environmental management of industrial chemicals. Copyright © 2011 Elsevier Ltd. All rights reserved.

Environmental analysis of the chemical release module. [space shuttle payload

NASA Technical Reports Server (NTRS)

Heppner, J. P.; Dubin, M.

1980-01-01

The environmental analysis of the Chemical Release Module (a free flying spacecraft deployed from the space shuttle to perform chemical release experiments) is reviewed. Considerations of possible effects of the injectants on human health, ionosphere, weather, ground based optical astronomical observations, and satellite operations are included. It is concluded that no deleterious environmental effects of widespread or long lasting nature are anticipated from chemical releases in the upper atmosphere of the type indicated for the program.

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

Chemical Transformation Simulator

EPA Science Inventory

The Chemical Transformation Simulator (CTS) is a web-based, high-throughput screening tool that automates the calculation and collection of physicochemical properties for an organic chemical of interest and its predicted products resulting from transformations in environmental sy...

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors.

PubMed

Huang, Hongtai; Wang, Aolin; Morello-Frosch, Rachel; Lam, Juleen; Sirota, Marina; Padula, Amy; Woodruff, Tracey J

2018-03-01

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Predicting the response of populations to environmental change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ives, A.R.

1995-04-01

When subject to long-term directional environmental perturbations, changes in population densities depend on the positive and negative feedbacks operating through interactions within and among species in a community. This paper develops techniques to predict the long-term responses of population densities to environmental changes using data on short-term population fluctuations driven by short-term environmental variability. In addition to giving quantitative predictions, the techniques also reveal how different qualitative patterns of species interactions either buffer or accentuate population responses to environmental trends. All of the predictions are based on regression coefficients extracted from time series data, and they can therefore be appliedmore » with a minimum of mathematical and statistical gymnastics. 48 refs., 10 figs., 4 tabs.« less

Toxic Environmental Chemicals: The Role of Reproductive Health Professionals In Preventing Harmful Exposures

PubMed Central

SUTTON, Patrice; WOODRUFF, Tracey J.; PERRON, Joanne; STOTLAND, Naomi; CONRY, Jeanne A.; MILLER, Mark D.; GIUDICE, Linda C.

2015-01-01

Every pregnant woman in the U.S. is exposed to many and varied environmental chemicals. Rapidly accumulating scientific evidence documents that widespread exposure to environmental chemicals at levels encountered in daily life can adversely impact reproductive and developmental health. Preconception and prenatal exposure to environmental chemicals are of particular import because they may have a profound and lasting impact on health across the life course. Thus, preventing developmental exposures to environmental chemicals would benefit greatly from the active participation of reproductive health professionals in clinical and policy arenas. PMID:22405527

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar Environmental Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research

Prioritizing chemicals for environmental management in China based on screening of potential risks

NASA Astrophysics Data System (ADS)

Yu, Xiangyi; Mao, Yan; Sun, Jinye; Shen, Yingwa

2014-03-01

The rapid development of China's chemical industry has created increasing pressure to improve the environmental management of chemicals. To bridge the large gap between the use and safe management of chemicals, we performed a comprehensive review of the international methods used to prioritize chemicals for environmental management. By comparing domestic and foreign methods, we confirmed the presence of this gap and identified potential solutions. Based on our literature review, we developed an appropriate screening method that accounts for the unique characteristics of chemical use within China. The proposed method is based on an evaluation using nine indices of the potential hazard posed by a chemical: three environmental hazard indices (persistence, bioaccumulation, and eco-toxicity), four health hazard indices (acute toxicity, carcinogenicity, mutagenicity, and reproductive and developmental toxicity), and two environmental exposure hazard indices (chemical amount and utilization pattern). The results of our screening agree with results of previous efforts from around the world, confirming the validity of the new system. The classification method will help decisionmakers to prioritize and identify the chemicals with the highest environmental risk, thereby providing a basis for improving chemical management in China.

Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins.

PubMed

Karp, Jerome M; Eryilmaz, Ertan; Erylimaz, Ertan; Cowburn, David

2015-01-01

There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.

Validated predictive modelling of the environmental resistome

PubMed Central

Amos, Gregory CA; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

2015-01-01

Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome. PMID:25679532

Validated predictive modelling of the environmental resistome.

PubMed

Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

2015-06-01

Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

Type I and II β-turns prediction using NMR chemical shifts.

PubMed

Wang, Ching-Cheng; Lai, Wen-Chung; Chuang, Woei-Jer

2014-07-01

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of Cο, Cα, H(N), and N(H) chemical shifts were generally (i + 1) > (i + 2). The mean values of Cβ and Hα chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2% with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.

Priority Environmental Chemical Contaminants in Meat

NASA Astrophysics Data System (ADS)

Brambilla, Gianfranco; Iamiceli, Annalaura; di Domenico, Alessandro

Generally, foods of animal origin play an important role in determining the exposure of human beings to contaminants of both biological and chemical origins (Ropkins & Beck, 2002; Lievaart et al., 2005). A potentially large number of chemicals could be considered, several of them deserving a particular attention due to their occurrence (contaminations levels and frequencies) and intake scenarios reflecting the differences existing in the economical, environmental, social and ecological contexts in which the “from-farm-to-fork” activities related to meat production are carried out (FAO - Food and Agriculture Organization, 2008).

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001; Gupta, Shikha

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models wasmore » performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and

A Framework for the Environmental Professional in the Chemical Industry.

ERIC Educational Resources Information Center

Priesing, Charles P.

1982-01-01

Addresses four areas of environmental concern in the chemical industry: (1) needs and responsibilities of environmental protection; (2) organization and distribution of environmental affairs within the corporate structure; (3) functions and operations associated with industrial environmental management; and (4) origins and tasks of the…

Use of questionnaires and an expert panel to judge the environmental consequences of chemical spills for the development of an environment-accident index.

PubMed

Andersson, Asa Scott; Stjernström, Olof; Fängmark, Ingrid

2005-05-01

Assessing the environmental consequences of a chemical accident is a complex task. To date, the methods used to evaluate the environmental effects of an acute release of a chemical have often been based on measurements of chemical and physical variables deemed to be important, such as the concentration of the chemical. However, a broader strategy is needed to predict the environmental consequences of potential accidents during the planning process. An Environment-Accident Index (EAI), a simple tool based on such a strategy, has been developed to facilitate the consideration of a multitude of influential variables. The objectives of this study were to evaluate whether questionnaire-based expert panel's judgements could provide useful data on the environmental consequences of chemical spills, and an effective basis for further development of the EAI. As expected, the judgements did not agree perfectly, but they do give rough indications of the environmental effects, and highlight consistent trends that should be useful inputs for planning, prevention and decontamination processes. The different accidents were also judged to have caused everything from minor to very major effects in the environment, implying that a wide range of accident scenarios were represented in the material and covered by the EAI. Therefore, questionnaires and expert panel judgements can be used to collect useful data for estimating the likely environmental consequences of chemical accidents and for further development of the EAI.

Influences of Environmental Chemicals on Atopic Dermatitis.

PubMed

Kim, Kwangmi

2015-06-01

Atopic dermatitis is a chronic inflammatory skin condition including severe pruritus, xerosis, visible eczematous skin lesions that mainly begin early in life. Atopic dermatitis exerts a profound impact on the quality of life of patients and their families. The estimated lifetime prevalence of atopic dermatitis has increased 2~3 fold during over the past 30 years, especially in urban areas in industrialized countries, emphasizing the importance of life-style and environment in the pathogenesis of atopic diseases. While the interplay of individual genetic predisposition and environmental factors contribute to the development of atopic dermatitis, the recent increase in the prevalence of atopic dermatitis might be attributed to increased exposure to various environmental factors rather than alterations in human genome. In recent decades, there has been an increasing exposure to chemicals from a variety of sources. In this study, the effects of various environmental chemicals we face in everyday life - air pollutants, contact allergens and skin irritants, ingredients in cosmetics and personal care products, and food additives - on the prevalence and severity of atopic dermatitis are reviewed.

Influences of Environmental Chemicals on Atopic Dermatitis

PubMed Central

2015-01-01

Atopic dermatitis is a chronic inflammatory skin condition including severe pruritus, xerosis, visible eczematous skin lesions that mainly begin early in life. Atopic dermatitis exerts a profound impact on the quality of life of patients and their families. The estimated lifetime prevalence of atopic dermatitis has increased 2~3 fold during over the past 30 years, especially in urban areas in industrialized countries, emphasizing the importance of life-style and environment in the pathogenesis of atopic diseases. While the interplay of individual genetic predisposition and environmental factors contribute to the development of atopic dermatitis, the recent increase in the prevalence of atopic dermatitis might be attributed to increased exposure to various environmental factors rather than alterations in human genome. In recent decades, there has been an increasing exposure to chemicals from a variety of sources. In this study, the effects of various environmental chemicals we face in everyday life - air pollutants, contact allergens and skin irritants, ingredients in cosmetics and personal care products, and food additives - on the prevalence and severity of atopic dermatitis are reviewed. PMID:26191377

Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

PubMed Central

Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

2015-01-01

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

Predicting the chemical stability of monatomic chains

NASA Astrophysics Data System (ADS)

Lin, Zheng-Zhe; Chen, Xi

2013-02-01

A simple model for evaluating the thermal atomic transfer rates in nanosystems (Lin Z.-Z. et al., EPL, 94 (2011) 40002) was developed to predict the chemical reaction rates of nanosystems with small gas molecules. The accuracy of the model was verified by MD simulations for molecular adsorption and desorption on a monatomic chain. By the prediction, a monatomic carbon chain should survive for 1.2 × 102 years in the ambient of 1 atm O2 at room temperature, and it is very invulnerable to N2, H2O, NO2, CO and CO2, while a monatomic gold chain quickly ruptures in vacuum. It is worth noting that since the model can be easily applied via common ab initio calculations, it could be widely used in the prediction of chemical stability of nanosystems.

Prioritizing Environmental Chemicals for Obesity and Diabetes Outcomes Research: A Screening Approach Using ToxCast™ High-Throughput Data

PubMed Central

Auerbach, Scott; Filer, Dayne; Reif, David; Walker, Vickie; Holloway, Alison C.; Schlezinger, Jennifer; Srinivasan, Supriya; Svoboda, Daniel; Judson, Richard; Bucher, John R.; Thayer, Kristina A.

2016-01-01

Background: Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is challenging. This review is intended to help researchers generate hypotheses about chemicals that may contribute to diabetes and to obesity-related health outcomes by summarizing relevant findings from the U.S. Environmental Protection Agency (EPA) ToxCast™ high-throughput screening (HTS) program. Objectives: Our aim was to develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high-throughput screening data. Methods: We identified ToxCast™ assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and β cell function, adipocyte differentiation, and feeding behavior) and presented chemical screening data against those assay targets to identify chemicals of potential interest. Discussion: The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature. Testing hypotheses based on ToxCast™ data will also help assess the predictive utility of this HTS platform. Conclusions: More research is required to put these screening-level analyses into context, but the information presented in this review should facilitate the development of new hypotheses. Citation: Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. 2016. Prioritizing environmental chemicals for obesity and diabetes outcomes research

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

Integrated Proteomic Approaches for Understanding Toxicity of Environmental Chemicals

EPA Science Inventory

To apply quantitative proteomic analysis to the evaluation of toxicity of environmental chemicals, we have developed an integrated proteomic technology platform. This platform has been applied to the analysis of the toxic effects and pathways of many important environmental chemi...

Gut Dysbiosis in Animals Due to Environmental Chemical Exposures

PubMed Central

Rosenfeld, Cheryl S.

2017-01-01

The gut microbiome consists of over 103–104 microorganism inhabitants that together possess 150 times more genes that the human genome and thus should be considered an “organ” in of itself. Such communities of bacteria are in dynamic flux and susceptible to changes in host environment and body condition. In turn, gut microbiome disturbances can affect health status of the host. Gut dysbiosis might result in obesity, diabetes, gastrointestinal, immunological, and neurobehavioral disorders. Such host diseases can originate due to shifts in microbiota favoring more pathogenic species that produce various virulence factors, such as lipopolysaccharide. Bacterial virulence factors and metabolites may be transmitted to distal target sites, including the brain. Other potential mechanisms by which gut dysbiosis can affect the host include bacterial-produced metabolites, production of hormones and factors that mimic those produced by the host, and epimutations. All animals, including humans, are exposed daily to various environmental chemicals that can influence the gut microbiome. Exposure to such chemicals might lead to downstream systemic effects that occur secondary to gut microbiome disturbances. Increasing reports have shown that environmental chemical exposures can target both host and the resident gut microbiome. In this review, we will first consider the current knowledge of how endocrine disrupting chemicals (EDCs), heavy metals, air pollution, and nanoparticles can influence the gut microbiome. The second part of the review will consider how potential environmental chemical-induced gut microbiome changes might subsequently induce pathophysiological responses in the host, although definitive evidence for such effects is still lacking. By understanding how these chemicals result in gut dysbiosis, it may open up new remediation strategies in animals, including humans, exposed to such chemicals. PMID:28936425

A Chemical Properties Simulator to Support Integrated Environmental Modeling

EPA Science Inventory

Users of Integrated Environmental Modeling (IEM) systems are responsible for defining individual chemicals and their properties, a process that is time-consuming at best and overwhelming at worst, especially for new chemicals with new structures. A software tool is needed to allo...

Predicted Thermal Responses of Military Working Dog (MWD) to Chemical, Biological, Radiological, Nuclear (CBRN) Protective Kennel Enclosure

DTIC Science & Technology

2011-08-01

meteorological conditions. More specifically, climate chamber studies of the chemical protective kennel cover were conducted over a range of...responses to predict how long the dog could safely remain in the enclosure for various ambient environmental conditions. Climate chamber studies of...Engineering Center (NSRDEC) was tested in a climate - controlled chamber to quantify its insulation and vapor permeability properties. A schematic of

Analysis of baseline gene expression levels from toxicogenomics study control animals to identify sources of variation and predict responses to chemicals

EPA Science Inventory

The use of gene expression profiling to predict chemical mode of action would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control ...

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. �

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Environmental Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research

Are chemicals in articles an obstacle for reaching environmental goals? - Missing links in EU chemical management.

PubMed

Molander, Linda; Breitholtz, Magnus; Andersson, Patrik L; Rybacka, Aleksandra; Rudén, Christina

2012-10-01

It is widely acknowledged that the management of risks associated with chemicals in articles needs to be improved. The EU environmental policy states that environmental damage should be rectified at source. It is therefore motivated that the risk management of substances in articles also takes particular consideration to those substances identified as posing a risk in different environmental compartments. The primary aim of the present study was to empirically analyze to what extent the regulation of chemicals in articles under REACH is coherent with the rules concerning chemicals in the Sewage Sludge Directive (SSD) and the Water Framework Directive (WFD). We also analyzed the chemical variation of the organic substances regulated under these legislations in relation to the most heavily used chemicals. The results show that 16 of 24 substances used in or potentially present in articles and regulated by the SSD or the WFD are also identified under REACH either as a substance of very high concern (SVHC) or subject to some restrictions. However, for these substances we conclude that there is limited coherence between the legislations, since the identification as an SVHC does not in itself encompass any use restrictions, and the restrictions in REACH are in many cases limited to a particular use, and thus all other uses are allowed. Only a minor part of chemicals in commerce is regulated and these show a chemical variation that deviates from classical legacy pollutants. This warrants new tools to identify potentially hazardous chemicals in articles. We also noted that chemicals monitored in the environment under the WFD deviate in their chemistry from the ones regulated by REACH. In summary, we argue that to obtain improved resource efficiency and a sustainable development it is necessary to minimize the input of chemicals identified as hazardous to health or the environment into articles. Copyright © 2012 Elsevier B.V. All rights reserved.

Simulation of Chronic Liver Injury Due to Environmental Chemicals

EPA Science Inventory

US EPA Virtual Liver (v-Liver) is a cellular systems model of hepatic tissues to predict the effects of chronic exposure to chemicals. Tens of thousands of chemicals are currently in commerce and hundreds more are introduced every year. Few of these chemicals have been adequate...

Chemical Fingerprinting of Materials Developed Due to Environmental Issues

NASA Technical Reports Server (NTRS)

Smith, Doris A.; McCool, A. (Technical Monitor)

2000-01-01

Instrumental chemical analysis methods are developed and used to chemically fingerprint new and modified External Tank materials made necessary by changing environmental requirements. Chemical fingerprinting can detect and diagnose variations in material composition. To chemically characterize each material, fingerprint methods are selected from an extensive toolbox based on the material's chemistry and the ability of the specific methods to detect the material's critical ingredients. Fingerprint methods have been developed for a variety of materials including Thermal Protection System foams, adhesives, primers, and composites.

Molybdenum Dichalcogenides for Environmental Chemical Sensing

PubMed Central

Zappa, Dario

2017-01-01

2D transition metal dichalcogenides are attracting a strong interest following the popularity of graphene and other carbon-based materials. In the field of chemical sensors, they offer some interesting features that could potentially overcome the limitation of graphene and metal oxides, such as the possibility of operating at room temperature. Molybdenum-based dichalcogenides in particular are among the most studied materials, thanks to their facile preparation techniques and promising performances. The present review summarizes the advances in the exploitation of these MoX2 materials as chemical sensors for the detection of typical environmental pollutants, such as NO2, NH3, CO and volatile organic compounds. PMID:29231879

An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

PubMed

Liu, Tao; Chen, Lei; Pan, Xiaoyong

2018-05-31

Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Computational prediction of chemical reactions: current status and outlook.

PubMed

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals.

PubMed

Herrgård, Markus; Sukumara, Sumesh; Campodonico, Miguel; Zhuang, Kai

2015-12-01

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry. © 2015 Authors; published by Portland Press Limited.

[Application of quantum-chemical methods to prediction of the carcinogenicity of chemical substances].

PubMed

Zholdikova, Z I; Kharchevnikova, N V

2006-01-01

A version of logical-combinatorial JSM type intelligent system was used to predict the presence and the degree of a carcinogenic effect. This version was based on combined description of chemical substances including both structural and numeric parameters. The new version allows for the fact that the toxicity and danger caused by chemical substances often depend on their biological activation in the organism. The authors substantiate classifying chemicals according to their carcinogenic activity, and illustrate the use of the system to predict the carcinogenicity of polycyclic aromatic hydrocarbons using a model of bioactivation via the formation of diolepoxides, and the carcinogenicity of halogenated alkanes using a model of bioactivation via oxidative dehalogenation. The paper defined the boundary level of an energetic parameter, the exceeding of which correlated with the inhibition of halogenated alkanes's metabolism and the absence of carcinogenic activity.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

Predicting drug side-effect profiles: a chemical fragment-based approach

PubMed Central

2011-01-01

Background Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients. Results In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information. Conclusions The proposed method is expected to be useful in various stages of the drug development process. PMID:21586169

PREDICTION OF PHYSICOCHEMICAL PROCESSES FOR ENVIRONMENTAL MODELING BY COMPUTER

EPA Science Inventory

The major differences among behavioral profiles of molecules in the environment are attributable to their physicochemical properties. For most chemicals, only fragmentary knowledge exists about those properties that determine each compound's environmental fate. A chemical-by-ch...

Suspect screening of maternal serum to identify new environmental chemical biomonitoring targets using liquid chromatography-quadrupole time-of-flight mass spectrometry.

PubMed

Gerona, Roy R; Schwartz, Jackie M; Pan, Janet; Friesen, Matthew M; Lin, Thomas; Woodruff, Tracey J

2018-03-01

The use and advantages of high-resolution mass spectrometry (MS) as a discovery tool for environmental chemical monitoring has been demonstrated for environmental samples but not for biological samples. We developed a method using liquid chromatography-quadrupole time-of-flight MS (LC-QTOF/MS) for discovery of previously unmeasured environmental chemicals in human serum. Using non-targeted data acquisition (full scan MS analysis) we were able to screen for environmental organic acids (EOAs) in 20 serum samples from second trimester pregnant women. We define EOAs as environmental organic compounds with at least one dissociable proton which are utilized in commerce. EOAs include environmental phenols, phthalate metabolites, perfluorinated compounds, phenolic metabolites of polybrominated diphenyl ethers and polychlorinated biphenyls, and acidic pesticides and/or predicted acidic pesticide metabolites. Our validated method used solid phase extraction, reversed-phase chromatography in a C18 column with gradient elution, electrospray ionization in negative polarity and automated tandem MS (MS/MS) data acquisition to maximize true positive rates. We identified "suspect EOAs" using Agilent MassHunter Qualitative Analysis software, to match chemical formulas generated from each sample run with molecular formulas in our unique database of 693 EOAs assembled from multiple environmental literature sources. We found potential matches for 282 (41%) of the EOAs in our database. Sixty-five of these suspect EOAs were detected in at least 75% of the samples; only 19 of these compounds are currently biomonitored in National Health and Nutrition Examination Survey. We confirmed two of three suspect EOAs by LC-QTOF/MS using a targeted method developed through LC-MS/MS, reporting the first confirmation of benzophenone-1 and bisphenol S in pregnant women's sera. Our suspect screening workflow provides an approach to comprehensively scan environmental chemical exposures in humans. This

Reporting needs for studies of environmental chemicals in human milk.

PubMed

Bates, Michael N; Selevan, Sherry G; Ellerbee, Susan M; Gartner, Lawrence M

2002-11-22

Studies of environmental chemicals in human milk have been carried out in many countries, but few have been conducted in the United States. These studies are useful for monitoring population trends in exposure to chemicals, for research into the determinants of environmental chemicals in milk and relationships between the levels found and the health status of the women and their infants, and for risk assessment. This article provides practical advice on data and information reporting for such studies. Participation in these studies comes at a difficult time for the breast-feeding mothers, so it is important that the mothers support the study and its goals. A key goal of any study of environmental chemicals in human milk must be to ensure that the breast-feeding process is not disrupted by unwarranted concerns about harm to the infant from chemicals in human milk. Therefore, it is essential that reporting of information be a two-way process. Information needs to be supplied to participating mothers before, during, and after their participation in the study. Information supplied before participation is necessary to satisfy the ethical requirement for informed consent; information supplied during participation includes advice on expressing, collecting, and storing milk samples, and how to avoid sample contamination; and information supplied to each participant at the end of the study includes a report of their individual results and a summary of study results and outcomes generally. The key instrument for obtaining data from the participants is the study questionnaire. This needs to be prepared in accordance with principles of good questionnaire development, and preferably should be interviewer administered. The questionnaire content will vary according to the objectives of the study. Although studies of environmental chemicals in human milk are logistically complex and demanding, they are practicable and, with careful planning and execution, yield important data.

Predicting Chemical Toxicity from Proteomics and Computational Chemistry

DTIC Science & Technology

2008-07-30

similarity spaces, BD Gute and SC Basak, SAR QSAR Environ. Res., 17, 37-51 (2006). Predicting pharmacological and toxicological activity of heterocyclic...affinity of dibenzofurans: a hierarchical QSAR approach, authored jointly by Basak and Mills; Division of Chemical Toxicology iii. Prediction of blood...biodescriptors vis-ä-vis chemodescriptors in predictive toxicology e) Development of integrated QSTR models using the combined set of chemodescriptors and

Application of a predictive Bayesian model to environmental accounting.

PubMed

Anex, R P; Englehardt, J D

2001-03-30

Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar ) of the Environmental Modeling Center (EMC) conducts a program of research and development in support Climate Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 Page Author: EMC

Characterization and Prediction of Chemical Functions and ...

EPA Pesticide Factsheets

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-b

Artificial Neural Network Prediction of Chemical-Disease Relationships using Readily Available Chemical Properties

DTIC Science & Technology

2014-03-27

C15H13N3O4S Potassium Bromide 0119000100 BrK Potassium Permanganate 0158030400 MnO4K Prazosin 0383410801 C19H21N5O4 Propranolol-HCl 0259350302...chemicals and correctly match it to a single disease category. Potassium permanganate and ethylene glycol can both be correctly linked to disease group...chemical is linked to the same disease, the network is unable to predict the same disease for the multiple chemicals. Potassium permanganate and

EPAs ToxCast Research Program: Developing Predictive Bioactivity Signatures for Chemicals

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

The dilemma in prioritizing chemicals for environmental analysis: known versus unknown hazards.

PubMed

Anna, Sobek; Sofia, Bejgarn; Christina, Rudén; Magnus, Breitholtz

2016-08-10

A major challenge for society is to manage the risks posed by the many chemicals continuously emitted to the environment. All chemicals in production and use cannot be monitored and science-based strategies for prioritization are essential. In this study we review available data to investigate which substances are included in environmental monitoring programs and published research studies reporting analyses of chemicals in Baltic Sea fish between 2000 and 2012. Our aim is to contribute to the discussion of priority settings in environmental chemical monitoring and research, which is closely linked to chemical management. In total, 105 different substances or substance groups were analyzed in Baltic Sea fish. Polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) were the most studied substances or substance groups. The majority, 87%, of all analyses comprised 20% of the substances or substance groups, whereas 46 substance groups (44%) were analyzed only once. Almost three quarters of all analyses regarded a POP-substance (persistent organic pollutant). These results demonstrate that the majority of analyses on environmental contaminants in Baltic Sea fish concern a small number of already regulated chemicals. Legacy pollutants such as POPs pose a high risk to the Baltic Sea due to their hazardous properties. Yet, there may be a risk that prioritizations for chemical analyses are biased based on the knowns of the past. Such biases may lead to society failing in identifying risks posed by yet unknown hazardous chemicals. Alternative and complementary ways to identify priority chemicals are needed. More transparent communication between risk assessments performed as part of the risk assessment process within REACH and monitoring programs, and information on chemicals contained in consumer articles, would offer ways to identify chemicals for environmental analysis.

Predictive accuracy of combined genetic and environmental risk scores.

PubMed

Dudbridge, Frank; Pashayan, Nora; Yang, Jian

2018-02-01

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

Predictive accuracy of combined genetic and environmental risk scores

PubMed Central

Pashayan, Nora; Yang, Jian

2017-01-01

ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

Predicting genotypes environmental range from genome-environment associations.

PubMed

Manel, Stéphanie; Andrello, Marco; Henry, Karine; Verdelet, Daphné; Darracq, Aude; Guerin, Pierre-Edouard; Desprez, Bruno; Devaux, Pierre

2018-05-17

Genome-environment association methods aim to detect genetic markers associated with environmental variables. The detected associations are usually analysed separately to identify the genomic regions involved in local adaptation. However, a recent study suggests that single-locus associations can be combined and used in a predictive way to estimate environmental variables for new individuals on the basis of their genotypes. Here, we introduce an original approach to predict the environmental range (values and upper and lower limits) of species genotypes from the genetic markers significantly associated with those environmental variables in an independent set of individuals. We illustrate this approach to predict aridity in a database constituted of 950 individuals of wild beets and 299 individuals of cultivated beets genotyped at 14,409 random Single Nucleotide Polymorphisms (SNPs). We detected 66 alleles associated with aridity and used them to calculate the fraction (I) of aridity-associated alleles in each individual. The fraction I correctly predicted the values of aridity in an independent validation set of wild individuals and was then used to predict aridity in the 299 cultivated individuals. Wild individuals had higher median values and a wider range of values of aridity than the cultivated individuals, suggesting that wild individuals have higher ability to resist to stress-aridity conditions and could be used to improve the resistance of cultivated varieties to aridity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

DEVELOPMENT OF ENVIRONMENTAL INDICES FOR GREEN CHEMICAL PRODUCTION AND USE

EPA Science Inventory

Chemical production, use and disposal cause adverse impacts on the environment. Consequently, much research has been conducted to develop methods for estimating the risk of chemicals and to screen them based on environmental impact. Risk assessment may be subdivide...

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar conducts a program of research and development in support of the National Centers for Environmental Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court College Park

Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study

PubMed Central

Czarnota, Jenna; Gennings, Chris; Colt, Joanne S.; De Roos, Anneclaire J.; Cerhan, James R.; Severson, Richard K.; Hartge, Patricia; Ward, Mary H.

2015-01-01

Background There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one). Objectives We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk. Methods We conducted a population-based case–control study of NHL in four National Cancer Institute–Surveillance, Epidemiology, and End Results centers—Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington—from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL. Results The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa. Conclusions An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk. Citation Czarnota J, Gennings C, Colt JS, De Roos AJ, Cerhan JR, Severson RK, Hartge P, Ward MH

Fate of sessile droplet chemical agents in environmental substrates in the presence of physiochemical processes

NASA Astrophysics Data System (ADS)

Navaz, H. K.; Dang, A. L.; Atkinson, T.; Zand, A.; Nowakowski, A.; Kamensky, K.

2014-05-01

A general-purpose multi-phase and multi-component computer model capable of solving the complex problems encountered in the agent substrate interaction is developed. The model solves the transient and time-accurate mass and momentum governing equations in a three dimensional space. The provisions for considering all the inter-phase activities (solidification, evaporation, condensation, etc.) are included in the model. The chemical reactions among all phases are allowed and the products of the existing chemical reactions in all three phases are possible. The impact of chemical reaction products on the transport properties in porous media such as porosity, capillary pressure, and permeability is considered. Numerous validations for simulants, agents, and pesticides with laboratory and open air data are presented. Results for chemical reactions in the presence of pre-existing water in porous materials such as moisture, or separated agent and water droplets on porous substrates are presented. The model will greatly enhance the capabilities in predicting the level of threat after any chemical such as Toxic Industrial Chemicals (TICs) and Toxic Industrial Materials (TIMs) release on environmental substrates. The model's generality makes it suitable for both defense and pharmaceutical applications.

Human Stem Cell-Derived Cardiomyocytes: An Alternative Model to Evaluate Environmental Chemical Cardiac Safety and Development of Predictive Adverse Outcome Pathways

EPA Science Inventory

Biomonitoring over the last 14 years has shown human exposure to environmental chemicals has increased ~10-fold (1). In addition, mortality and morbidity related cardiovascular disease continues to be the leading national and global public health issue (2, 3). The association bet...

Chemical Risk Assessment: Traditional vs Public Health ...

EPA Pesticide Factsheets

Preventing adverse health impacts from exposures to environmental chemicals is fundamental to protecting individual and public health. When done efficiently and properly, chemical risk assessment enables risk management actions that minimize the incidence and impacts of environmentally-induced diseases related to chemical exposure. However, traditional chemical risk assessment is faced with multiple challenges with respect to predicting and preventing disease in human populations, and epidemiological studies increasingly report observations of adverse health effects at exposure levels predicted from animal studies to be safe for humans. This discordance reinforces concerns about the adequacy of contemporary risk assessment practices (Birnbaum, Burke, & Jones, 2016) for protecting public health. It is becoming clear that to protect public health more effectively, future risk assessments will need to use the full range of available data, draw on innovative methods to integrate diverse data streams, and consider health endpoints that also reflect the range of subtle effects and morbidities observed in human populations. Given these factors, there is a need to reframe chemical risk assessment to be more clearly aligned with the public health goal of minimizing environmental exposures associated with disease. Preventing adverse health impacts from exposures to environmental chemicals is fundamental to protecting individual and public health. Chemical risk assessments

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

PubMed

Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

Consensus models to predict endocrine disruption for all human-exposure chemicals (AAAS Annual Meeting)

EPA Science Inventory

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been te...

DESIGNING ENVIRONMENTALLY FRIENDLY CHEMICAL PROCESSES WITH FUGITIVE AND OPEN EMISSIONS

EPA Science Inventory

Designing a chemical process normally includes aspects of economic and environmental disciplines. In this work we describe methods to quickly and easily evaluate the economics and potential environmental impacts of a process, with the hydrodealkylation of toluene as an example. ...

A Chemical Properties Simulator to Support Integrated Environmental Modeling (proceeding)

EPA Science Inventory

Users of Integrated Environmental Modeling (IEM) systems are responsible for defining individual chemicals and their properties, a process that is time-consuming at best and overwhelming at worst, especially for new chemicals with new structures. A software tool is needed to allo...

RAPID SCREENING OF ENVIRONMENTAL CHEMICALS FOR ESTROGEN RECEPTOR BINDING CAPACITY

EPA Science Inventory

Over the last few years, an increased awareness of endocrine disrupting chemicals (EDCs) and their potential to affect wildlife and humans has produced a demand for practical screening methods to identify endocrine activity in a wide range of environmental and industrial chemical...

Jensen's Inequality Predicts Effects of Environmental Variation

Treesearch

Jonathan J. Ruel; Matthew P. Ayres

1999-01-01

Many biologists now recognize that environmental variance can exert important effects on patterns and processes in nature that are independent of average conditions. Jenson's inequality is a mathematical proof that is seldom mentioned in the ecological literature but which provides a powerful tool for predicting some direct effects of environmental variance in...

Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study.

PubMed

Czarnota, Jenna; Gennings, Chris; Colt, Joanne S; De Roos, Anneclaire J; Cerhan, James R; Severson, Richard K; Hartge, Patricia; Ward, Mary H; Wheeler, David C

2015-10-01

There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one). We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk. We conducted a population-based case-control study of NHL in four National Cancer Institute-Surveillance, Epidemiology, and End Results centers--Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington--from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL. The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa. An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk.

Can Exposure to Environmental Chemicals Increase the Risk of Diabetes Type 1 Development?

PubMed Central

Stene, Lars Christian

2015-01-01

Type 1 diabetes mellitus (T1DM) is an autoimmune disease, where destruction of beta-cells causes insulin deficiency. The incidence of T1DM has increased in the last decades and cannot entirely be explained by genetic predisposition. Several environmental factors are suggested to promote T1DM, like early childhood enteroviral infections and nutritional factors, but the evidence is inconclusive. Prenatal and early life exposure to environmental pollutants like phthalates, bisphenol A, perfluorinated compounds, PCBs, dioxins, toxicants, and air pollutants can have negative effects on the developing immune system, resulting in asthma-like symptoms and increased susceptibility to childhood infections. In this review the associations between environmental chemical exposure and T1DM development is summarized. Although information on environmental chemicals as possible triggers for T1DM is sparse, we conclude that it is plausible that environmental chemicals can contribute to T1DM development via impaired pancreatic beta-cell and immune-cell functions and immunomodulation. Several environmental factors and chemicals could act together to trigger T1DM development in genetically susceptible individuals, possibly via hormonal or epigenetic alterations. Further observational T1DM cohort studies and animal exposure experiments are encouraged. PMID:25883945

Can exposure to environmental chemicals increase the risk of diabetes type 1 development?

PubMed

Bodin, Johanna; Stene, Lars Christian; Nygaard, Unni Cecilie

2015-01-01

Type 1 diabetes mellitus (T1DM) is an autoimmune disease, where destruction of beta-cells causes insulin deficiency. The incidence of T1DM has increased in the last decades and cannot entirely be explained by genetic predisposition. Several environmental factors are suggested to promote T1DM, like early childhood enteroviral infections and nutritional factors, but the evidence is inconclusive. Prenatal and early life exposure to environmental pollutants like phthalates, bisphenol A, perfluorinated compounds, PCBs, dioxins, toxicants, and air pollutants can have negative effects on the developing immune system, resulting in asthma-like symptoms and increased susceptibility to childhood infections. In this review the associations between environmental chemical exposure and T1DM development is summarized. Although information on environmental chemicals as possible triggers for T1DM is sparse, we conclude that it is plausible that environmental chemicals can contribute to T1DM development via impaired pancreatic beta-cell and immune-cell functions and immunomodulation. Several environmental factors and chemicals could act together to trigger T1DM development in genetically susceptible individuals, possibly via hormonal or epigenetic alterations. Further observational T1DM cohort studies and animal exposure experiments are encouraged.

Monitoring, modelling and environmental exposure assessment of industrial chemicals in the aquatic environment.

PubMed

Holt, M S; Fox, K; Griessbach, E; Johnsen, S; Kinnunen, J; Lecloux, A; Murray-Smith, R; Peterson, D R; Schröder, R; Silvani, M; ten Berge, W F; Toy, R J; Feijtel, T C

2000-12-01

Monitoring and laboratory data play integral roles alongside fate and exposure models in comprehensive risk assessments. The principle in the European Union Technical Guidance Documents for risk assessment is that measured data may take precedence over model results but only after they are judged to be of adequate reliability and to be representative of the particular environmental compartments to which they are applied. In practice, laboratory and field data are used to provide parameters for the models, while monitoring data are used to validate the models' predictions. Thus, comprehensive risk assessments require the integration of laboratory and monitoring data with the model predictions. However, this interplay is often overlooked. Discrepancies between the results of models and monitoring should be investigated in terms of the representativeness of both. Certainly, in the context of the EU risk assessment of existing chemicals, the specific requirements for monitoring data have not been adequately addressed. The resources required for environmental monitoring, both in terms of manpower and equipment, can be very significant. The design of monitoring programmes to optimise the use of resources and the use of models as a cost-effective alternative are increasing in importance. Generic considerations and criteria for the design of new monitoring programmes to generate representative quality data for the aquatic compartment are outlined and the criteria for the use of existing data are discussed. In particular, there is a need to improve the accessibility to data sets, to standardise the data sets, to promote communication and harmonisation of programmes and to incorporate the flexibility to change monitoring protocols to amend the chemicals under investigation in line with changing needs and priorities.

National Centers for Environmental Prediction

Science.gov Websites

. Government's official Web portal to all Federal, state and local government Web resources and services. MISSION Web Page [scroll down to "Verification" Section] HRRR Verification at NOAA ESRL HRRR Web Verification Web Page NOAA / National Weather Service National Centers for Environmental Prediction

Environmental sentinel biomonitors: integrated response systems for monitoring toxic chemicals

NASA Astrophysics Data System (ADS)

van der Schalie, William H.; Reuter, Roy; Shedd, Tommy R.; Knechtges, Paul L.

2002-02-01

Operational environments for military forces are becoming potentially more dangerous due to the increased number, use, and misuse of toxic chemicals across the entire range of military missions. Defense personnel may be exposed to harmful chemicals as a result of industrial accidents or intentional or unintentional action of enemy, friendly forces, or indigenous populations. While there has been a significant military effort to enable forces to operate safely and survive and sustain operations in nuclear, biological, chemical warfare agent environments, until recently there has not been a concomitant effort associated with potential adverse health effects from exposures of deployed personnel to toxic industrial chemicals. To provide continuous real-time toxicity assessments across a broad spectrum of individual chemicals or chemical mixtures, an Environmental Sentinel Biomonitor (ESB) system concept is proposed. An ESB system will integrate data from one or more platforms of biologically-based systems and chemical detectors placed in the environment to sense developing toxic conditions and transmit time-relevant data for use in risk assessment, mitigation, and/or management. Issues, challenges, and next steps for the ESB system concept are described, based in part on discussions at a September 2001 workshop sponsored by the U.S. Army Center for Environmental Health Research.

High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization

EPA Science Inventory

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present method...

In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project.

PubMed

Judson, Richard S; Houck, Keith A; Kavlock, Robert J; Knudsen, Thomas B; Martin, Matthew T; Mortensen, Holly M; Reif, David M; Rotroff, Daniel M; Shah, Imran; Richard, Ann M; Dix, David J

2010-04-01

Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use, and the thousands of environmental chemicals lacking toxicity data. The U.S. Environmental Protection Agency's ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. We tested 309 mostly pesticide active chemicals in 467 assays across nine technologies, including high-throughput cell-free assays and cell-based assays, in multiple human primary cells and cell lines plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Chemicals displayed a broad spectrum of activity at the molecular and pathway levels. We saw many expected interactions, including endocrine and xenobiotic metabolism enzyme activity. Chemicals ranged in promiscuity across pathways, from no activity to affecting dozens of pathways. We found a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also found associations between a small set of in vitro assays and rodent liver lesion formation. This approach promises to provide meaningful data on the thousands of untested environmental chemicals and to guide targeted testing of environmental contaminants.

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

ANIMALS AS SENTINELS OF HUMAN HEALTH HAZARDS OF ENVIRONMENTAL CHEMICALS

EPA Science Inventory

A workshop titled "Using Sentinel Species Data to Address the Potential Human Health Effects of Chemicals in the Environmnet," sponsored by the U.S. Army Center for Environmental Health Research, the National Center for Environmental Assessment of the EPA, and the Agency for Toxi...

Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data

PubMed Central

Reif, David M.; Martin, Matthew T.; Tan, Shirlee W.; Houck, Keith A.; Judson, Richard S.; Richard, Ann M.; Knudsen, Thomas B.; Dix, David J.; Kavlock, Robert J.

2010-01-01

Background The prioritization of chemicals for toxicity testing is a primary goal of the U.S. Environmental Protection Agency (EPA) ToxCast™ program. Phase I of ToxCast used a battery of 467 in vitro, high-throughput screening assays to assess 309 environmental chemicals. One important mode of action leading to toxicity is endocrine disruption, and the U.S. EPA’s Endocrine Disruptor Screening Program (EDSP) has been charged with screening pesticide chemicals and environmental contaminants for their potential to affect the endocrine systems of humans and wildlife. Objective The goal of this study was to develop a flexible method to facilitate the rational prioritization of chemicals for further evaluation and demonstrate its application as a candidate decision-support tool for EDSP. Methods Focusing on estrogen, androgen, and thyroid pathways, we defined putative endocrine profiles and derived a relative rank or score for the entire ToxCast library of 309 unique chemicals. Effects on other nuclear receptors and xenobiotic metabolizing enzymes were also considered, as were pertinent chemical descriptors and pathways relevant to endocrine-mediated signaling. Results Combining multiple data sources into an overall, weight-of-evidence Toxicological Priority Index (ToxPi) score for prioritizing further chemical testing resulted in more robust conclusions than any single data source taken alone. Conclusions Incorporating data from in vitro assays, chemical descriptors, and biological pathways in this prioritization schema provided a flexible, comprehensive visualization and ranking of each chemical’s potential endocrine activity. Importantly, ToxPi profiles provide a transparent visualization of the relative contribution of all information sources to an overall priority ranking. The method developed here is readily adaptable to diverse chemical prioritization tasks. PMID:20826373

VHH antibodies: Emerging reagents for the analysis of environmental chemicals

PubMed Central

Bever, Candace S.; Dong, Jie-Xian; Vasylieva, Natalia; Barnych, Bogdan; Cui, Yongliang; Xu, Zhen-Lin; Hammock, Bruce D.; Gee, Shirley J.

2016-01-01

A VHH antibody (or nanobody) is the antigen binding fragment of heavy chain only antibodies. Discovered nearly 25 years ago, they have been investigated for their use in clinical therapeutics and immunodiagnostics, and more recently for environmental monitoring applications. A new and valuable immunoreagent for the analysis of small molecular weight environmental chemicals, VHH will overcome many pitfalls encountered with conventional reagents. In the work so far, VHH antibodies often perform comparably to conventional antibodies for small molecule analysis, are amenable to numerous genetic engineering techniques, and show ease of adaption to other immunodiagnostic platforms for use in environmental monitoring. Recent reviews cover the structure and production of VHH antibodies as well as their use in clinical settings. However, no report focuses on the use of these VHH antibodies to small environmental chemicals (MW <1,500 Da). This review article summarizes the efforts made to produce VHHs to various environmental targets, compares the VHH-based assays with conventional antibody assays, and discusses the advantages and limitations in developing these new antibody reagents particularly to small molecule targets. PMID:27209591

The effects of environmental chemical carcinogens on the microRNA machinery.

PubMed

Izzotti, A; Pulliero, A

2014-07-01

The first evidence that microRNA expression is early altered by exposure to environmental chemical carcinogens in still healthy organisms was obtained for cigarette smoke. To date, the cumulative experimental data indicate that similar effects are caused by a variety of environmental carcinogens, including polycyclic aromatic hydrocarbons, nitropyrenes, endocrine disruptors, airborne mixtures, carcinogens in food and water, and carcinogenic drugs. Accordingly, the alteration of miRNA expression is a general mechanism that plays an important pathogenic role in linking exposure to environmental toxic agents with their pathological consequences, mainly including cancer development. This review summarizes the existing experimental evidence concerning the effects of chemical carcinogens on the microRNA machinery. For each carcinogen, the specific microRNA alteration signature, as detected in experimental studies, is reported. These data are useful for applying microRNA alterations as early biomarkers of biological effects in healthy organisms exposed to environmental carcinogens. However, microRNA alteration results in carcinogenesis only if accompanied by other molecular damages. As an example, microRNAs altered by chemical carcinogens often inhibits the expression of mutated oncogenes. The long-term exposure to chemical carcinogens causes irreversible suppression of microRNA expression thus allowing the transduction into proteins of mutated oncogenes. This review also analyzes the existing knowledge regarding the mechanisms by which environmental carcinogens alter microRNA expression. The underlying molecular mechanism involves p53-microRNA interconnection, microRNA adduct formation, and alterations of Dicer function. On the whole, reported findings provide evidence that microRNA analysis is a molecular toxicology tool that can elucidate the pathogenic mechanisms activated by environmental carcinogens. Copyright © 2014 Elsevier GmbH. All rights reserved.

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

TSCA Work Plan Chemical Technical Supplement – Physicochemical Properties and Environmental Fate of the Brominated Phthalates Cluster (BPC) Chemicals

EPA Pesticide Factsheets

TSCA Work Plan Chemical Technical Supplement – Physicochemical Properties and Environmental Fate of the Brominated Phthalates Cluster (BPC) Chemicals -- Brominated Phthalates Cluster Flame Retardants.

DESIGNING ENVIRONMENTAL, ECONOMIC AND ENERGY EFFICIENT CHEMICAL PROCESSES

EPA Science Inventory

The design and improvement of chemical processes can be very challenging. The earlier energy conservation, process economics and environmental aspects are incorporated into the process development, the easier and less expensive it is to alter the process design. Process emissio...

Biodiversity in environmental assessment-current practice and tools for prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gapmore » between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment.« less

Bioconcentration potential of organic environmental chemicals in humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geyer, H.; Scheunert, I.; Korte, F.

1986-12-01

A list of environmental chemicals detectable in adipose tissue and/or milk of non-occupationally exposed humans is presented. Besides their physiochemical properties (n-octanol/water partition coefficient and water solubility), their acceptable daily intake (ADI) values, production figures, fate in the environment, concentrations in human adipose tissue, and data from total diet studies from market basket investigations are given. Average bioconcentration factors (BCF) of polychlorinated biphenyls (PCBs), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), DDT, hexachlorobenzene (HCB), dieldrin, hexachlorocyclohexane isomers (alpha-HCH, beta-HCH, gamma-HCH, delta-HCH), pentachlorophenol (PCP), and 3,5-di-tert-butyl-4-hydroxytoluene (BHT) in human adipose tissue are calculated. The bioconcentration factors (wet wt basis) of these compounds are between 3 andmore » 47 times higher in humans than in rats. The environmental chemicals are divided into three groups in respect to their bioconcentration factors in human adipose tissue: group I, high BCF (greater than 100); group II, medium BCF (10-100); and group III, low BCF (less than 10). The bioconcentration factors are useful for hazard assessment of chemicals to humans.« less

SeqAPASS: Predicting chemical susceptibility to threatened/endangered species

EPA Science Inventory

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was devel...

Association of environmental chemicals & estrogen metabolites in children.

PubMed

Ihde, Erin Speiser; Loh, Ji Meng; Rosen, Lawrence

2015-12-17

The prevalence of pediatric hormonal disorders and hormonally-sensitive cancers are rising. Chemicals including bisphenol A (BPA), phthalates, parabens, 4-nonylphenol (4NP) and triclosan have been linked to disruption of endocrine pathways and altered hormonal status in both animal and human studies. Additionally, changes in estrogen metabolism have been associated with pediatric endocrine disorders and linked to estrogen-dependent cancers. The main objective of the study was to measure the presence of these environmental chemicals in prepubescent children and assess the relationship between chemical metabolites and estrogen metabolism. 50 subjects (25 male, 25 female) were recruited from the principal investigator's existing patient population at his pediatric primary care office. The first 5 boys and 5 girls in each age group (4 through 8 years old inclusive) who presented for annual examinations were included, as long as they were Tanner Stage I (prepubertal) on physical exam, without diagnosis of hormonally-related condition and/or cancer and able to give a urine sample. Urine samples were collected in glass containers for analysis of chemical and estrogen metabolites. Study kits and lab analysis were provided by Genova Diagnostics (Duluth, GA). Summary statistics for the concentrations of each chemical metabolite as well as estrogen metabolites were computed (minimum, maximum, median and inter-quartile range) for males only, for females only and for all subjects. Comparisons between groups (e.g. males v. females) were assessed using the nonparametric Wilcoxon test, since the data was skewed. The correlation between concentrations of chemical metabolites and estrogen metabolites in prepubescent children were examined by the Spearman's correlation coefficient (ρ). 100 % of subjects had detectable levels of at least five chemicals [corrected] in their urine, and 74 % had detectable levels of eight or more chemicals. 28 % of subjects had measurable levels of 4NP

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY: AN EXPANDED VIEW OF CHEMICAL TOXICITY

EPA Science Inventory

A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. T...

Integrated Environmental Risk Assessment and Whole-Process Management System in Chemical Industry Parks

PubMed Central

Shao, Chaofeng; Yang, Juan; Tian, Xiaogang; Ju, Meiting; Huang, Lei

2013-01-01

Chemical industry parks in China are considered high-risk areas because they present numerous risks that can damage the environment, such as pollution incidents. In order to identify the environmental risks and the principal risk factors in these areas, we have developed a simple physical model of a regional environmental risk field (ERF) using existing dispersal patterns and migration models. The regional ERF zoning was also conducted and a reference value for diagnostic methods was developed to determine risk-acceptable, risk-warning, and risk-mitigation zones, which can provide a risk source layout for chemical industry parks. In accordance with the environmental risk control requirements, this study focused on the three stages of control and management of environmental risk and established an environmental risk management system including risk source identification and assessment, environmental safety planning, early risk warning, emergency management, assessment of environmental effects, and environmental remediation of pollution accidents. By using this model, the environmental risks in Tianjin Binhai New Area, the largest chemical industry park in China, were assessed and the environmental risk zoning map was drawn, which suggested the existence of many unacceptable environmental risks in this area. Thus, relevant suggestions have been proposed from the perspective of the adjustment of risk source layout, intensified management of environmental risk control and so on. PMID:23603866

Integrated environmental risk assessment and whole-process management system in chemical industry parks.

PubMed

Shao, Chaofeng; Yang, Juan; Tian, Xiaogang; Ju, Meiting; Huang, Lei

2013-04-19

Chemical industry parks in China are considered high-risk areas because they present numerous risks that can damage the environment, such as pollution incidents. In order to identify the environmental risks and the principal risk factors in these areas, we have developed a simple physical model of a regional environmental risk field (ERF) using existing dispersal patterns and migration models. The regional ERF zoning was also conducted and a reference value for diagnostic methods was developed to determine risk-acceptable, risk-warning, and risk-mitigation zones, which can provide a risk source layout for chemical industry parks. In accordance with the environmental risk control requirements, this study focused on the three stages of control and management of environmental risk and established an environmental risk management system including risk source identification and assessment, environmental safety planning, early risk warning, emergency management, assessment of environmental effects, and environmental remediation of pollution accidents. By using this model, the environmental risks in Tianjin Binhai New Area, the largest chemical industry park in China, were assessed and the environmental risk zoning map was drawn, which suggested the existence of many unacceptable environmental risks in this area. Thus, relevant suggestions have been proposed from the perspective of the adjustment of risk source layout, intensified management of environmental risk control and so on.

PREDICTING THE EFFECTIVENESS OF CHEMICAL-PROTECTIVE CLOTHING MODEL AND TEST METHOD DEVELOPMENT

EPA Science Inventory

A predictive model and test method were developed for determining the chemical resistance of protective polymeric gloves exposed to liquid organic chemicals. The prediction of permeation through protective gloves by solvents was based on theories of the solution thermodynamics of...

ENVIRONMENTAL ANDROGENS AND ANTIANDROGENS: AN EXPANDING CHEMICAL UNIVERSE

EPA Science Inventory

Within the last ten years, awareness has grown about environmental chemicals that display antiandrogenic or androgenic activity. While studies in the early 1990s focused on pesticides that acted as androgen receptor (AR) antagonists, it soon became evident that this was not the ...

Forecasting the Environmental Impacts of New Energetic Materials

DTIC Science & Technology

2010-11-30

Quantitative structure- activity relationships for chemical reductions of organic contaminants. Environmental Toxicology and Chemistry 22(8): 1733-1742. QSARs ...activity relationships [ QSARs ]) and the use of these properties to predict the chemical?s fate with multimedia assessment models. SERDP has recently...has several parts, including the prediction of chemical properties (e.g., with quantitative structure-activity relationships [ QSARs ]) and the use of

Cohort profile: the maternal-infant research on environmental chemicals research platform.

PubMed

Arbuckle, Tye E; Fraser, William D; Fisher, Mandy; Davis, Karelyn; Liang, Chun Lei; Lupien, Nicole; Bastien, Stéphanie; Velez, Maria P; von Dadelszen, Peter; Hemmings, Denise G; Wang, Jingwei; Helewa, Michael; Taback, Shayne; Sermer, Mathew; Foster, Warren; Ross, Greg; Fredette, Paul; Smith, Graeme; Walker, Mark; Shear, Roberta; Dodds, Linda; Ettinger, Adrienne S; Weber, Jean-Philippe; D'Amour, Monique; Legrand, Melissa; Kumarathasan, Premkumari; Vincent, Renaud; Luo, Zhong-Cheng; Platt, Robert W; Mitchell, Grant; Hidiroglou, Nick; Cockell, Kevin; Villeneuve, Maya; Rawn, Dorothea F K; Dabeka, Robert; Cao, Xu-Liang; Becalski, Adam; Ratnayake, Nimal; Bondy, Genevieve; Jin, Xiaolei; Wang, Zhongwen; Tittlemier, Sheryl; Julien, Pierre; Avard, Denise; Weiler, Hope; Leblanc, Alain; Muckle, Gina; Boivin, Michel; Dionne, Ginette; Ayotte, Pierre; Lanphear, Bruce; Séguin, Jean R; Saint-Amour, Dave; Dewailly, Eric; Monnier, Patricia; Koren, Gideon; Ouellet, Emmanuel

2013-07-01

The Maternal-Infant Research on Environmental Chemicals (MIREC) Study was established to obtain Canadian biomonitoring data for pregnant women and their infants, and to examine potential adverse health effects of prenatal exposure to priority environmental chemicals on pregnancy and infant health. Women were recruited during the first trimester from 10 sites across Canada and were followed through delivery. Questionnaires were administered during pregnancy and post-delivery to collect information on demographics, occupation, life style, medical history, environmental exposures and diet. Information on the pregnancy and the infant was abstracted from medical charts. Maternal blood, urine, hair and breast milk, as well as cord blood and infant meconium, were collected and analysed for an extensive list of environmental biomarkers and nutrients. Additional biospecimens were stored in the study's Biobank. The MIREC Research Platform encompasses the main cohort study, the Biobank and follow-up studies. Of the 8716 women approached at early prenatal clinics, 5108 were eligible and 2001 agreed to participate (39%). MIREC participants tended to smoke less (5.9% vs. 10.5%), be older (mean 32.2 vs. 29.4 years) and have a higher education (62.3% vs. 35.1% with a university degree) than women giving birth in Canada. The MIREC Study, while smaller in number of participants than several of the international cohort studies, has one of the most comprehensive datasets on prenatal exposure to multiple environmental chemicals. The biomonitoring data and biological specimen bank will make this research platform a significant resource for examining potential adverse health effects of prenatal exposure to environmental chemicals. © 2013 John Wiley & Sons Ltd and Her Majesty the Queen in Right of Canada. Reproduced with the permission of the Minister of Health.

Prediction of chemical speciation in stabilized/solidified wastes using a general chemical equilibrium model. Part 1: Chemical representation of cementitious binders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, J.Y.; Batchelor, B.

1999-03-01

Chemical equilibrium models are useful to evaluate stabilized/solidified waste. A general equilibrium model, SOLTEQ, a modified version of MINTEQA2 for S/S, was applied to predict the chemical speciations in the stabilized/solidified waste form. A method was developed to prepare SOLTEQ input data that can chemically represent various stabilized/solidified binders. Taylor`s empirical model was used to describe partitioning of alkali ions. As a result, SOLTEQ could represent chemical speciation in pure binder systems such as ordinary Portland cement and ordinary Portland cement + fly ash. Moreover, SOLTEQ could reasonably describe the effects on the chemical speciation due to variations in water-to-cement,more » fly ash contents, and hydration times of various binder systems. However, this application of SOLTEQ was not accurate in predicting concentrations of Ca, Si, and SO{sub 4} ions, due to uncertainties in the CSH solubility model and K{sub sp} values of cement hydrates at high pH values.« less

INTEGRATED CHEMICAL INFORMATION TECHNOLOGIES ...

EPA Pesticide Factsheets

A central regulatory mandate of the Environmental Protection Agency, spanning many Program Offices and issues, is to assess the potential health and environmental risks of large numbers of chemicals released into the environment, often in the absence of relevant test data. Models for predicting potential adverse effects of chemicals based primarily on chemical structure play a central role in prioritization and screening strategies yet are highly dependent and conditional upon the data used for developing such models. Hence, limits on data quantity, quality, and availability are considered by many to be the largest hurdles to improving prediction models in diverse areas of toxicology. Generation of new toxicity data for additional chemicals and endpoints, development of new high-throughput, mechanistically relevant bioassays, and increased generation of genomics and proteomics data that can clarify relevant mechanisms will all play important roles in improving future SAR prediction models. The potential for much greater immediate gains, across large domains of chemical and toxicity space, comes from maximizing the ability to mine and model useful information from existing toxicity data, data that represent huge past investment in research and testing expenditures. In addition, the ability to place newer “omics” data, data that potentially span many possible domains of toxicological effects, in the broader context of historical data is the means for opti

The Toxicity Data Landscape for Environmental Chemicals (journal)

EPA Science Inventory

Thousands of chemicals are in common use but only a portion of them have undergone significant toxicological evaluation, leading to the need to prioritize the remainder for targeted testing. To address this issue, the U.S. Environmental Protection Agency (U.S. EPA) and other orga...

Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessments.

PubMed

Comber, Mike H I; Walker, John D; Watts, Chris; Hermens, Joop

2003-08-01

The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed.

Identifying Candidate Chemical-Disease Linkages (Environmental and Epigenetic Determinants of IBD)

EPA Science Inventory

Presentation at meeting on Environmental and Epigenetic Determinants of IBD in New York, NY on identifying candidate chemical-disease linkages by using AOPs to identify molecular initiating events and using relevant high throughput assays to screen for candidate chemicals. This h...

Proteomic analyses of the environmental toxicity of carcinogenic chemicals

EPA Science Inventory

Protein expression and posttranslational modifications consistently change in response to the exposure to environmental chemicals. Recent technological advances in proteomics provide new tools for more efficient characterization of protein expression and posttranslational modific...

Allium-test as a tool for toxicity testing of environmental radioactive-chemical mixtures

NASA Astrophysics Data System (ADS)

Oudalova, A. A.; Geras'kin, S. A.; Dikareva, N. S.; Pyatkova, S. V.

2017-01-01

Bioassay-based approaches have been propagated to assess toxicity of unknown mixtures of environmental contaminants, but it was rarely applied in cases of chemicals with radionuclides combinations. Two Allium-test studies were performed to assess environmental impact from potential sources of combined radioactive-chemical pollution. Study sites were located at nuclear waste storage facilities in European and in Far-Eastern parts of Russia. As environmental media under impact, waters from monitor wells and nearby water bodies were tested. Concentrations of some chemicals and radionuclides in the samples collected enhanced the permitted limits. Cytogenetic and cytotoxic effects were used as biological endpoints, namely, frequency and spectrum of chromosome aberrations and mitotic abnormalities in anatelophase cells as well as mitotic activity in Allium root tips. Sample points were revealed where waters have an enhanced mutagenic potential. The findings obtained could be used to optimize monitoring system and advance decision making on management and rehabilitation of industrial sites. The Allium-test could be recommended and applied as an effective tool for toxicity testing in case of combined contamination of environmental compartments with radionuclides and chemical compounds.

Environmental Impact on Vascular Development Predicted by High Throughput Screening

EPA Science Inventory

Understanding health risks to embryonic development from exposure to environmental chemicals is a significant challenge given the diverse chemical landscape and paucity of data for most of these compounds. High throughput screening (HTS) in EPA’s ToxCastTM project provides vast d...

International Federation of Gynecology and Obstetrics opinion on reproductive health impacts of exposure to toxic environmental chemicals.

PubMed

Di Renzo, Gian Carlo; Conry, Jeanne A; Blake, Jennifer; DeFrancesco, Mark S; DeNicola, Nathaniel; Martin, James N; McCue, Kelly A; Richmond, David; Shah, Abid; Sutton, Patrice; Woodruff, Tracey J; van der Poel, Sheryl Ziemin; Giudice, Linda C

2015-12-01

Exposure to toxic environmental chemicals during pregnancy and breastfeeding is ubiquitous and is a threat to healthy human reproduction. There are tens of thousands of chemicals in global commerce, and even small exposures to toxic chemicals during pregnancy can trigger adverse health consequences. Exposure to toxic environmental chemicals and related health outcomes are inequitably distributed within and between countries; universally, the consequences of exposure are disproportionately borne by people with low incomes. Discrimination, other social factors, economic factors, and occupation impact risk of exposure and harm. Documented links between prenatal exposure to environmental chemicals and adverse health outcomes span the life course and include impacts on fertility and pregnancy, neurodevelopment, and cancer. The global health and economic burden related to toxic environmental chemicals is in excess of millions of deaths and billions of dollars every year. On the basis of accumulating robust evidence of exposures and adverse health impacts related to toxic environmental chemicals, the International Federation of Gynecology and Obstetrics (FIGO) joins other leading reproductive health professional societies in calling for timely action to prevent harm. FIGO recommends that reproductive and other health professionals advocate for policies to prevent exposure to toxic environmental chemicals, work to ensure a healthy food system for all, make environmental health part of health care, and champion environmental justice. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project

EPA Science Inventory

Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use, and the thousands of environmental chemicals lacking toxicity data. The U.S. Environmental Protection Agency’s ToxCast program aims to address these concerns by ...

Influence of heredity on human sensitivity to environmental chemicals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weber, W.W.

1995-12-31

Hereditary peculiarities in individual responses to environmental chemicals are a common occurrence in human populations. Genetic variation in glutathione S-transferase, CYP1A2, N-acetyltransferase, and paraoxonase exemplify the relationship of metabolic variation to individual susceptibility to cancer and other toxicants of environmental origin. Heritable receptor protein variants, a subset of proteins of enormous pharmacogenetic, potential that have not thus far been extensively explored form the pharmacogenetic standpoint, and also considered. Examples of interest that are considered include receptor variants associated with retinoic acid resistance in acute promyelocytic leukemia, with paradoxical responses to antiandrogens in prostate cancer, and with retinitis pigmentosa. Additional heritablemore » protein variants of pharmacogenetic interest that result in antibiotic-induced deafness, glucocorticoid-remediable aldosteronism and hypertension, the long-QT syndrome, and beryllium-induced lung disease are also discussed. These traits demonstrate how knowledge of the molecular basis and mechanism of the variant response may contribute to its prevention in sensitive persons as well as to improved therapy for genetically conditioned disorders that arise form environmental chemicals. 99 refs.« less

Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.

PubMed

Dagan-Wiener, Ayana; Nissim, Ido; Ben Abu, Natalie; Borgonovo, Gigliola; Bassoli, Angela; Niv, Masha Y

2017-09-21

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

Predicting soil formation on the basis of transport-limited chemical weathering

NASA Astrophysics Data System (ADS)

Yu, Fang; Hunt, Allen Gerhard

2018-01-01

Soil production is closely related to chemical weathering. It has been shown that, under the assumption that chemical weathering is limited by solute transport, the process of soil production is predictable. However, solute transport in soil cannot be described by Gaussian transport. In this paper, we propose an approach based on percolation theory describing non-Gaussian transport of solute to predict soil formation (the net production of soil) by considering both soil production from chemical weathering and removal of soil from erosion. Our prediction shows agreement with observed soil depths in the field. Theoretical soil formation rates are also compared with published rates predicted using soil age-profile thickness (SAST) method. Our formulation can be incorporated directly into landscape evolution models on a point-to-point basis as long as such models account for surface water routing associated with overland flow. Further, our treatment can be scaled-up to address complications associated with continental-scale applications, including those from climate change, such as changes in vegetation, or surface flow organization. The ability to predict soil formation rates has implications for understanding Earth's climate system on account of the relationship to chemical weathering of silicate minerals with the associated drawdown of atmospheric carbon, but it is also important in geomorphology for understanding landscape evolution, including for example, the shapes of hillslopes, and the net transport of sediments to sedimentary basins.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

PubMed

Basant, Nikita; Gupta, Shikha

2017-06-01

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Global Environmental Multiscale model - a platform for integrated environmental predictions

NASA Astrophysics Data System (ADS)

Kaminski, Jacek W.; Struzewska, Joanna; Neary, Lori; Dearden, Frank

2017-04-01

The Global Environmental Multiscale model was developed by the Government of Canada as an operational weather prediction model in the mid-1990s. Subsequently, it was used as the host meteorological model for an on-line implementation of air quality chemistry and aerosols from global to the meso-gamma scale. Further model developments led to the vertical extension of the modelling domain to include stratospheric chemistry, aerosols, and formation of polar stratospheric clouds. In parallel, the modelling platform was used for planetary applications where dynamical, radiative transfer and chemical processes in the atmosphere of Mars were successfully simulated. Undoubtedly, the developed modelling platform can be classified as an example capable of the seamless and coupled modelling of the dynamics and chemistry of planetary atmospheres. We will present modelling results for global, regional, and local air quality episodes and the long-term air quality trends. Upper troposphere and lower stratosphere modelling results will be presented in terms of climate change and subsonic aviation emissions modelling. Model results for the atmosphere of Mars will be presented in the context of the 2016 ExoMars mission and the anticipated observations from the NOMAD instrument. Also, we will present plans and the design to extend the GEM model to the F region with further coupling with a magnetospheric model that extends to 15 Re.

Modelling Chemical Reasoning to Predict and Invent Reactions.

PubMed

Segler, Marwin H S; Waller, Mark P

2017-05-02

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemical Risk Assessment: Traditional vs Public Health Perspectives

PubMed Central

Axelrad, Daniel A.; Bahadori, Tina; Bussard, David; Cascio, Wayne E.; Deener, Kacee; Dix, David; Thomas, Russell S.; Kavlock, Robert J.; Burke, Thomas A.

2017-01-01

Preventing adverse health effects of environmental chemical exposure is fundamental to protecting individual and public health. When done efficiently and properly, chemical risk assessment enables risk management actions that minimize the incidence and effects of environmentally induced diseases related to chemical exposure. However, traditional chemical risk assessment is faced with multiple challenges with respect to predicting and preventing disease in human populations, and epidemiological studies increasingly report observations of adverse health effects at exposure levels predicted from animal studies to be safe for humans. This discordance reinforces concerns about the adequacy of contemporary risk assessment practices for protecting public health. It is becoming clear that to protect public health more effectively, future risk assessments will need to use the full range of available data, draw on innovative methods to integrate diverse data streams, and consider health endpoints that also reflect the range of subtle effects and morbidities observed in human populations. Considering these factors, there is a need to reframe chemical risk assessment to be more clearly aligned with the public health goal of minimizing environmental exposures associated with disease. PMID:28520487

Endocrine-Disrupting Chemicals and Oil and Natural Gas Operations: Potential Environmental Contamination and Recommendations to Assess Complex Environmental Mixtures

PubMed Central

Kassotis, Christopher D.; Tillitt, Donald E.; Lin, Chung-Ho; McElroy, Jane A.; Nagel, Susan C.

2015-01-01

Background Hydraulic fracturing technologies, developed over the last 65 years, have only recently been combined with horizontal drilling to unlock oil and gas reserves previously deemed inaccessible. Although these technologies have dramatically increased domestic oil and natural gas production, they have also raised concerns for the potential contamination of local water supplies with the approximately 1,000 chemicals that are used throughout the process, including many known or suspected endocrine-disrupting chemicals. Objectives We discuss the need for an endocrine component to health assessments for drilling-dense regions in the context of hormonal and antihormonal activities for chemicals used. Methods We discuss the literature on a) surface and groundwater contamination by oil and gas extraction operations, and b) potential human exposure, particularly in the context of the total hormonal and antihormonal activities present in surface and groundwater from natural and anthropogenic sources; we also discuss initial analytical results and critical knowledge gaps. Discussion In light of the potential for environmental release of oil and gas chemicals that can disrupt hormone receptor systems, we recommend methods for assessing complex hormonally active environmental mixtures. Conclusions We describe a need for an endocrine-centric component for overall health assessments and provide information supporting the idea that using such a component will help explain reported adverse health trends as well as help develop recommendations for environmental impact assessments and monitoring programs. Citation Kassotis CD, Tillitt DE, Lin CH, McElroy JA, Nagel SC. 2016. Endocrine-disrupting chemicals and oil and natural gas operations: potential environmental contamination and recommendations to assess complex environmental mixtures. Environ Health Perspect 124:256–264; http://dx.doi.org/10.1289/ehp.1409535 PMID:26311476

CHEMICAL HAZARD EVALUATION FOR MANAGEMENT STRATEGIES: A METHOD FOR RANKING AND SCORING CHEMICALS BY POTENTIAL HUMAN HEALTH AND ENVIRONMENTAL IMPACTS

EPA Science Inventory

Between 60,000 and 100,000 of the over than 8,000,000 chemicals listed by the Chemical Abstracts Services Registry are commercially produced and are potential environmental pollutants. Risk-based evaluation for these chemicals is often required to evaluate the potential impacts...

Invertebrates in testing of environmental chemicals: are they alternatives?

PubMed Central

Lagadic, L; Caquet, T

1998-01-01

An enlarged interpretation of alternatives in toxicology testing includes the replacement of one animal species with another, preferably a nonmammalian species. This paper reviews the potential of invertebrates in testing environmental chemicals and provides evidence of their usefulness in alternative testing methodologies. The first part of this review addresses the use of invertebrates in laboratory toxicology testing. Problems in extrapolating results obtained in invertebrates to those obtained from vertebrates are noted, suggesting that invertebrates can essentially be used in addition to rather than as replacements for vertebrates in laboratory toxicity tests. However, evaluation of the ecologic impact of environmental chemicals must include defining end points that may frequently differ from those classically used in biomedical research. In this context, alternative approaches using invertebrates may be more pertinent. The second part of the review therefore focuses on the use of invertebrates in situ to assess the environmental impact of pollutants. Advantages of invertebrates in ecotoxicologic investigation are presented for their usefulness for seeking mechanistic links between effects occurring at the individual level and consequences for higher levels of biologic organization (e.g., population and community). In the end, it is considered that replacement of vertebrates by invertebrates in ecotoxicity testing is likely to become a reality when basic knowledge of metabolic, physiologic, and developmental patterns in the latter will be sufficient to assess the effect of a given chemical through end points that could be different between invertebrates and vertebrates. PMID:9599707

PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

EPA Science Inventory

Spreadsheet and short-cut methods have been developed for predicting evaporation rates and evaporation times for spills (and constrained baths) of chemical mixtures. Steady-state and time-varying predictions of evaporation rates can be made for six-component mixtures, includ...

Why small and medium chemical companies continue to pose severe environmental risks in rural China.

PubMed

He, Guizhen; Zhang, Lei; Mol, Arthur P J; Wang, Tieyu; Lu, Yonglong

2014-02-01

In China, rural chemical SMEs are often believed to still largely operate below the sustainability radar. This paper investigates to what extent and how chemical SMEs are already experiencing pressure to improve their environmental performance, using an in-depth case study in Jasmine County, Hebei province. The results show that local residents had rather low trust in the environmental improvement promises made by the enterprises and the local government, and disagreed with the proposed improvement plans. Although the power of local residents to influence decision making remained limited, the chemical SMEs started to feel increasing pressures to clean up their business, from governments, local communities and civil society, and international value chain stakeholders. Notwithstanding these mounting pressures chemical SME's environmental behavior and performance has not changed radically for the better. The strong economic ties between local county governments and chemical SMEs continue to be a major barrier for stringent environmental regulation. Copyright © 2013 Elsevier Ltd. All rights reserved.

In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Investigating How the Microbiome Interacts With Environmental Chemicals in Zebrafish

EPA Pesticide Factsheets

This internship will use an innovative experimental system comprised of colonized and microbe-free zebrafish to learn how microbial colonization status affects the toxicity of environmental chemicals.

Exposure to environmental chemicals among Korean adults-updates from the second Korean National Environmental Health Survey (2012-2014).

PubMed

Choi, Wookhee; Kim, Suejin; Baek, Yong-Wook; Choi, Kyungho; Lee, Keejae; Kim, Sungkyoon; Yu, Seung Do; Choi, Kyunghee

2017-03-01

National biomonitoring program can offer solid scientific evidence on exposure profiles of environmental chemicals at a national level, and provide a snapshot of changing exposure level over time. Therefore, several countries have maintained such programs for developing environmental health policies. The Korean National Environmental Health Survey (KoNEHS) was designed to understand the level of human exposure to environmental chemicals by time and location, and to identify possible sources of such exposure. The 2nd stage of KoNEHS, which was conducted between 2012 and 2014, examined a total of 6478 adult subjects over 19 years of age, and measured 21 environmental chemicals of major policy concern. Compared to the findings from the first stage monitoring (2009-2011), slightly higher levels of blood lead were observed, while those of mercury remained similar. Blood metal concentrations, however, were higher than those reported from national biomonitoring programs of United States, Germany and Canada. The urinary concentrations of phthalates metabolites were lower, but those of t,t-muconic acid and BPA were higher than those reported in the first stage survey. The urinary cotinine level decreased perhaps reflecting general declining patterns of first- and second-hand smoking. The results of the second stage survey were made available for public use since April 2016. Some policy efforts appear to be at least in part effective on mitigating chemical exposure among people, e.g., urinary phthalate metabolites and cotinine, while further confirmations are warranted. In-depth assessments will be conducted to identify vulnerable groups and important exposure pathways. Copyright © 2016 The Authors. Published by Elsevier GmbH.. All rights reserved.

Development of bovine serum albumin-water partition coefficients predictive models for ionogenic organic chemicals based on chemical form adjusted descriptors.

PubMed

Ding, Feng; Yang, Xianhai; Chen, Guosong; Liu, Jining; Shi, Lili; Chen, Jingwen

2017-10-01

The partition coefficients between bovine serum albumin (BSA) and water (K BSA/w ) for ionogenic organic chemicals (IOCs) were different greatly from those of neutral organic chemicals (NOCs). For NOCs, several excellent models were developed to predict their logK BSA/w . However, it was found that the conventional descriptors are inappropriate for modeling logK BSA/w of IOCs. Thus, alternative approaches are urgently needed to develop predictive models for K BSA/w of IOCs. In this study, molecular descriptors that can be used to characterize the ionization effects (e.g. chemical form adjusted descriptors) were calculated and used to develop predictive models for logK BSA/w of IOCs. The models developed had high goodness-of-fit, robustness, and predictive ability. The predictor variables selected to construct the models included the chemical form adjusted averages of the negative potentials on the molecular surface (V s-adj - ), the chemical form adjusted molecular dipole moment (dipolemoment adj ), the logarithm of the n-octanol/water distribution coefficient (logD). As these molecular descriptors can be calculated from their molecular structures directly, the developed model can be easily used to fill the logK BSA/w data gap for other IOCs within the applicability domain. Furthermore, the chemical form adjusted descriptors calculated in this study also could be used to construct predictive models on other endpoints of IOCs. Copyright © 2017 Elsevier Inc. All rights reserved.

Host-associated microbiota modifies the toxicokinetics of environmental chemicals

EPA Science Inventory

Host-associated microbiota are known to biotransform drugs and some environmental chemicals like arsenic and polycyclic aromatic hydrocarbons. However, the metabolic capacity of microbiota treated with anti-microbial agents has not been assessed. Here, we exposed zebrafish with a...

SIMULATION MODELS FOR ENVIRONMENTAL MULTIMEDIA ANALYSIS OF TOXIC CHEMICALS

EPA Science Inventory

Multimedia understanding of pollutant behavior in the environment is of particular concern for chemicals that are toxic and are subject to accumulation in the environmental media (air, soil, water, vegetation) where biota and human exposure is significant. Multimedia simulation ...

[Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review].

PubMed

Tao, Lin-Li; Yang, Xiu-Juan; Deng, Jun-Ming; Zhang, Xi

2013-11-01

In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular

Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data

EPA Science Inventory

The prioritization of chemicals for toxicity testing is a primary goal of the U.S. EPA’s ToxCast™ program. Phase I of ToxCast utilized a battery of 467 in vitro, high-throughput screening assays to assess 309 environmental chemicals. One important mode of action leading to toxici...

Predicting People's Environmental Behaviour: Theory of Planned Behaviour and Model of Responsible Environmental Behaviour

ERIC Educational Resources Information Center

Chao, Yu-Long

2012-01-01

Using different measures of self-reported and other-reported environmental behaviour (EB), two important theoretical models explaining EB--Hines, Hungerford and Tomera's model of responsible environmental behaviour (REB) and Ajzen's theory of planned behaviour (TPB)--were compared regarding the fit between model and data, predictive ability,…

Environmental Influences on Reproductive Health, the Importance of Chemical Exposures

PubMed Central

Wang, Aolin; Padula, Amy; Sirota, Marina; Woodruff, Tracey J.

2016-01-01

Unstructured Abstract Chemical exposures during pregnancy can have a profound and life-long impact on human health. Due to the omnipresence of chemicals in our daily life, there is continuous contact with chemicals in food, water, air and consumer products. Consequently, human biomonitoring studies show that pregnant women around the globe are exposed to a variety of chemicals. In this review, we provide a summary of current data on maternal and fetal exposure as well as health consequences from these exposures. We review several chemical classes including polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), phenols, phthalates, pesticides, and metals. Additionally, we discuss environmental disparities and vulnerable populations, and future research directions. We conclude by providing some recommendations for prevention of chemical exposure and its adverse reproductive health consequences. PMID:27513554

Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

Biomonitoring of human fetal exposure to environmental chemicals in early pregnancy.

PubMed

Cooke, Gerard M

2014-01-01

The first trimester of human fetal life, a period of extremely rapid development of physiological systems, represents the most rapid growth phase in human life. Interference in the establishment of organ systems may result in abnormal development that may be manifest immediately or programmed for later abnormal function. Exposure to environmental chemicals may be affecting development at these early stages, and yet there is limited knowledge of the quantities and identities of the chemicals to which the fetus is exposed during early pregnancy. Clearly, opportunities for assessing fetal chemical exposure directly are extremely limited. Hence, this review describes indirect means of assessing fetal exposure in early pregnancy to chemicals that are considered disrupters of development. Consideration is given to such matrices as maternal hair, fingernails, urine, saliva, sweat, breast milk, amniotic fluid and blood, and fetal matrices such as cord blood, cord tissue, meconium, placenta, and fetal liver. More than 150 articles that presented data from chemical analysis of human maternal and fetal tissues and fluids were reviewed. Priority was given to articles where chemical analysis was conducted in more than one matrix. Where correlations between maternal and fetal matrices were determined, these articles were included and are highlighted, as these may provide the basis for future investigations of early fetal exposure. The determination of fetal chemical exposure, at the time of rapid human growth and development, will greatly assist regulatory agencies in risk assessments and establishment of advisories for risk management concerning environmental chemicals.

About Using Predictive Models and Tools To Assess Chemicals under TSCA

EPA Pesticide Factsheets

As part of EPA's effort to promote chemical safety, OPPT provides public access to predictive models and tools which can help inform the public on the hazards and risks of substances and improve chemical management decisions.

PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

EPA Science Inventory

Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

Tool for the Reduction and Assessment of Chemical and other Environmental Impacts

EPA Science Inventory

TRACI, the Tool for the Reduction and Assessment of Chemical and other environmental Impacts, has been developed by the US Environmental Protection Agency’s National Risk Management Research Laboratory to facilitate the characterization of stressors that have potential effects, ...

INVERSE QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP ANALYSIS FOR IMPROVING PREDICTIONS OF CHEMICAL TOXICITY

EPA Science Inventory

The toxic outcomes associated with environmental contaminants are often not due to the chemical form that was originally introduced into the environment, but rather to the chemical having undergone a transformation prior to reaching the vulnerable species. More importantly, the c...

Evaluation of measured and predicted environmental concentrations of selected human pharmaceuticals and personal care products.

PubMed

Liebig, Markus; Moltmann, Johann F; Knacker, Thomas

2006-03-01

In the past few years, there was an increasing awareness of the occurrence of pharmaceuticals and personal care products (PPCPs) in surface water and drinking water resources, and measurements in surface water, sediment or waste water were done for a number of PPCPs. In the regulatory context, an environmental risk assessment (ERA) has become essential for new PPCPs. Reliably predicted or measured environmental concentrations (PECs or MECs) of chemicals are essential for the exposure assessment, which is one of the two main pillars of environmental risk assessment (ERA). This paper reports on measured data of selected PPCPs in surface waters and compares the measured values with predicted environmental concentrations from exposure models. Such models have been proposed by the European Agency for the Evaluation of Medicinal Products (EMEA) and the Technical Guidance Document on Risk Assessment for New Notified and Existing Chemical Substances (TGD). Four pharmaceuticals and one personal care product were in the scope of the investigation reported here: 17alpha-ethinylestradiol, carbamazepine, sulfamethoxazole and iopromide as well as tonalide. Measured environmental concentrations in surface waters for these PPCPs were reviewed in the scientific literature. The appropriateness of these data was evaluated according to criteria for monitoring data recommended by the TGD. A total of 38 references were evaluated with emphasis on the adequacy of chemical analysis and the representativeness of sampling. Measurements of concentrations in surface water (MECsw), which were found to be adequate for use in exposure assessment according to the monitoring quality criteria, were averaged and compared with respective PECs in surface water (PECsw) derived from exposure modelling (cf. EMEA and TGD). Measured environmental concentrations adequate for use in exposure assessment were found in 20 out of 38 references. Several of the measurements from Germany could be used for a

Prediction of biodegradability from chemical structure: Modeling or ready biodegradation test data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loonen, H.; Lindgren, F.; Hansen, B.

1999-08-01

Biodegradation data were collected and evaluated for 894 substances with widely varying chemical structures. All data were determined according to the Japanese Ministry of International Trade and Industry (MITI) I test protocol. The MITI I test is a screening test for ready biodegradability and has been described by Organization for Economic Cooperation and Development (OECD) test guideline 301 C and European Union (EU) test guideline C4F. The chemicals were characterized by a set of 127 predefined structural fragments. This data set was used to develop a model for the prediction of the biodegradability of chemicals under standardized OECD and EUmore » ready biodegradation test conditions. Partial least squares (PLS) discriminant analysis was used for the model development. The model was evaluated by means of internal cross-validation and repeated external validation. The importance of various structural fragments and fragment interactions was investigated. The most important fragments include the presence of a long alkyl chain; hydroxy, ester, and acid groups (enhancing biodegradation); and the presence of one or more aromatic rings and halogen substituents (regarding biodegradation). More than 85% of the model predictions were correct for using the complete data set. The not readily biodegradable predictions were slightly better than the readily biodegradable predictions (86 vs 84%). The average percentage of correct predictions from four external validation studies was 83%. Model optimization by including fragment interactions improve the model predicting capabilities to 89%. It can be concluded that the PLS model provides predictions of high reliability for a diverse range of chemical structures. The predictions conform to the concept of readily biodegradable (or not readily biodegradable) as defined by OECD and EU test guidelines.« less

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.

PubMed

Ring, Caroline L; Pearce, Robert G; Setzer, R Woodrow; Wetmore, Barbara A; Wambaugh, John F

2017-09-01

The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with

WAR DSS: A DECISION SUPPORT SYSTEM FOR ENVIRONMENTALLY CONSCIOUS CHEMICAL PROCESS DESIGN

EPA Science Inventory

The second generation of the Waste Reduction (WAR) Algorithm is constructed as a decision support system (DSS) in the design of chemical manufacturing facilities. The WAR DSS is a software tool that can help reduce the potential environmental impacts (PEIs) of industrial chemical...

A Qualitative Comparison of Porcine and Rodent Thyroperoxidase -Effects of Environmental Chemicals.

EPA Science Inventory

A wide variety of environmental chemicals alter the function of the thyroid system in many animal species. Thyroperoxidase (TPO), the enzyme that synthesizes thyroid hormone, is one of the known biochemical targets for thyroid disrupting chemicals (TDC). The majority of the in vi...

Probing the ToxCast Chemical Library for Predictive Signatures of Developmental Toxicity

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals for Risk-Based Prioritization

EPA Science Inventory

Toxicokinetic (TK) models can address an important component of chemical risk assessments by helping bridge the gap between chemical exposure and measured toxicity endpoints. The metabolic clearance rate (CLint) and fraction of a chemical unbound by plasma proteins (Fub) are crit...

INTERSPECIES CORRELATION ESTIMATES PREDICT PROTECTIVE ENVIRONMENTAL CONCENTRATIONS

EPA Science Inventory

Environmental risk assessments often use multiple single species toxicity test results and species sensitivity distributions (SSDs) to derive a predicted no-effect concentration in the environment, typically the 5th percentile of the SSD, termed the HC5. The shape and location of...

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

PubMed

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Leveraging Publically Available Chemical Functional Use Data in Support of Exposure Prediction

EPA Science Inventory

The U.S. EPA Exposure Forecasting (ExpoCast) project aims to provide rapid screening-level exposure predictions for thousands of chemicals, most of which lack detailed exposure data. Chemical functional use - the role a chemical plays in processes or products (e.g. solvent, ant...

Univariate predictors of maternal concentrations of environmental chemicals: The MIREC study.

PubMed

Lewin, Antoine; Arbuckle, Tye E; Fisher, Mandy; Liang, Chun Lei; Marro, Leonora; Davis, Karelyn; Abdelouahab, Nadia; Fraser, William D

2017-03-01

The developing fetus and pregnant woman can be exposed to a variety of environmental chemicals that may adversely affect their health. Moreover, environmental exposure and risk disparities are associated with different social determinants, including socioeconomic status (SES) and demographic indicators. Our aim was to investigate whether and how maternal concentrations of a large panel of persistent and non-persistent environmental chemicals vary according to sociodemographic and lifestyle characteristics in a large pregnancy and birth cohort. Data were analyzed from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a cohort of pregnant women (N=2001) recruited over four years (2008-2011) in 10 cities across Canada. In all, 1890 urine and 1938 blood samples from the first trimester (1st and 3rd trimester for metals) were analysed and six sociodemographic and lifestyle indicators were assessed: maternal age, household income, parity, smoking status, country of birth and pre-pregnancy body mass index (BMI). We found these indicators to be significantly associated with many of the chemicals measured in maternal blood and urine. Women born outside Canada had significantly higher concentrations of di-2-ethylhexyl and diethyl phthalate metabolites, higher levels of all metals except cadmium (Cd), as well as higher levels of polychlorinated biphenyls (PCBs) and legacy organochlorine pesticides (OCPs). Nulliparity was associated with higher concentrations of dialkyl phosphates (DAPs), arsenic, dimethylarsinic acid (DMAA), perfluoroalkyl substances (PFASs) and many of the persistent organic pollutants. Smokers had higher levels of bisphenol A, Cd and perfluorohexane sulfonate, while those women who had never smoked had higher levels of triclosan, DMAA, manganese and some OCPs. Our results demonstrated that inequitable distribution of exposure to chemicals among populations within a country can occur. Sociodemographic and lifestyle factors are an

The NIEHS Predictive-Toxicology Evaluation Project.

PubMed Central

Bristol, D W; Wachsman, J T; Greenwell, A

1996-01-01

The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estimate the true error of models that have been applied to make prospective predictions on previously untested, noncongeneric-chemical substances. The PTE project first identifies a group of standardized NTP chemical bioassays either scheduled to be conducted or are ongoing, but not yet complete. The project then announces and advertises the evaluation experiment, disseminates information about the chemical bioassays, and encourages researchers from a wide variety of disciplines to publish their predictions in peer-reviewed journals, using whatever approaches and methods they feel are best. A collection of such papers is published in this Environmental Health Perspectives Supplement, providing readers the opportunity to compare and contrast PT approaches and models, within the context of their prospective application to an actual-use situation. This introduction to this collection of papers on predictive toxicology summarizes the predictions made and the final results obtained for the 44 chemical carcinogenesis bioassays of the first PTE experiment (PTE-1) and presents information that identifies the 30 chemical carcinogenesis bioassays of PTE-2, along with a table of prediction sets that have been published to date. It also provides background about the origin and goals of the PTE project, outlines the special challenge associated with estimating the true error of models that aspire to predict open-system behavior, and summarizes what has been learned to date. PMID:8933048

An expert system for prediction of chemical toxicity

USGS Publications Warehouse

Hickey, James P.; Aldridge, Andrew J.; Passino-Reader, Dora R.; Frank, Anthony M.

1992-01-01

The National Fisheries Research Center- Great Lakes has developed an interactive computer program that uses the structure of an organic molecule to predict its acute toxicity to four aquatic species. The expert system software, written in the muLISP language, identifies the skeletal structures and substituent groups of an organic molecule from a user-supplied standard chemical notation known as a SMILES string, and then generates values for four solvatochromic parameters. Multiple regression equations relate these parameters to the toxicities (expressed as log10LC50s and log10EC50s, along with 95% confidence intervals) for four species. The system is demonstrated by prediction of toxicity for anilide-type pesticides to the fathead minnow (Pimephales promelas). This software is designed for use on an IBM-compatible personal computer by personnel with minimal toxicology background for rapid estimation of chemical toxicity. The system has numerous applications, with much potential for use in the pharmaceutical industry

Climate-based archetypes for the environmental fate assessment of chemicals.

PubMed

Ciuffo, Biagio; Sala, Serenella

2013-11-15

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits

Environmental Chemical Exposures and Autism Spectrum Disorders: A Review of the Epidemiological Evidence

PubMed Central

Kalkbrenner, Amy E.; Schmidt, Rebecca J.; Penlesky, Annie C.

2016-01-01

In the past decade, the number of epidemiological publications addressing environmental chemical exposures and autism has grown tremendously. These studies are important because it is now understood that environmental factors play a larger role in causing autism than previously thought and because they address modifiable risk factors that may open up avenues for the primary prevention of the disability associated with autism. In this review, we covered studies of autism and estimates of exposure to tobacco, air pollutants, volatile organic compounds and solvents, metals (from air, occupation, diet, dental amalgams, and thimerosal-containing vaccines), pesticides, and organic endocrine-disrupting compounds such as flame retardants, non-stick chemicals, phthalates, and bisphenol A. We included studies that had individual-level data on autism, exposure measures pertaining to pregnancy or the 1st year of life, valid comparison groups, control for confounders, and adequate sample sizes. Despite the inherent error in the measurement of many of these environmental exposures, which is likely to attenuate observed associations, some environmental exposures showed associations with autism, especially traffic-related air pollutants, some metals, and several pesticides, with suggestive trends for some volatile organic compounds (e.g., methylene chloride, trichloroethylene, and styrene) and phthalates. Whether any of these play a causal role requires further study. Given the limited scope of these publications, other environmental chemicals cannot be ruled out, but have not yet been adequately studied. Future research that addresses these and additional environmental chemicals, including their most common routes of exposures, with accurate exposure measurement pertaining to several developmental windows, is essential to guide efforts for the prevention of the neurodevelopmental damage that manifests in autism symptoms. PMID:25199954

Environmental Chemical Assessment in Clinical Practice: Unveiling the Elephant in the Room

PubMed Central

Bijlsma, Nicole; Cohen, Marc M.

2016-01-01

A growing body of evidence suggests chemicals present in air, water, soil, food, building materials and household products are toxicants that contribute to the many chronic diseases typically seen in routine medical practice. Yet, despite calls from numerous organisations to provide clinicians with more training and awareness in environmental health, there are multiple barriers to the clinical assessment of toxic environmental exposures. Recent developments in the fields of systems biology, innovative breakthroughs in biomedical research encompassing the “-omics” fields, and advances in mobile sensing, peer-to-peer networks and big data, provide tools that future clinicians can use to assess environmental chemical exposures in their patients. There is also a need for concerted action at all levels, including actions by individual patients, clinicians, medical educators, regulators, government and non-government organisations, corporations and the wider civil society, to understand the “exposome” and minimise the extent of toxic exposures on current and future generations. Clinical environmental chemical risk assessment may provide a bridge between multiple disciplines that uses new technologies to herald in a new era in personalised medicine that unites clinicians, patients and civil society in the quest to understand and master the links between the environment and human health. PMID:26848668

Toward the definition of specific protection goals for the environmental risk assessment of chemicals: A perspective on environmental regulation in Europe.

PubMed

Brown, A Ross; Whale, Graham; Jackson, Mathew; Marshall, Stuart; Hamer, Mick; Solga, Andreas; Kabouw, Patrick; Galay-Burgos, Malyka; Woods, Richard; Nadzialek, Stephanie; Maltby, Lorraine

2017-01-01

This critical review examines the definition and implementation of environmental protection goals for chemicals in current European Union (EU) legislation, guidelines, and international agreements to which EU countries are party. The European chemical industry is highly regulated, and prospective environmental risk assessments (ERAs) are tailored for different classes of chemical, according to their specific hazards, uses, and environmental exposure profiles. However, environmental protection goals are often highly generic, requiring the prevention of "unacceptable" or "adverse" impacts on "biodiversity" and "ecosystems" or the "environment as a whole." This review aims to highlight working examples, challenges, solutions, and best practices for defining specific protection goals (SPGs), which are seen to be essential for refining and improving ERA. Specific protection goals hinge on discerning acceptable versus unacceptable adverse effects on the key attributes of relevant, sensitive ecological entities (ranging from organisms to ecosystems). Some isolated examples of SPGs for terrestrial and aquatic biota can be found in prospective ERA guidance for plant protection products (PPPs). However, SPGs are generally limited to environmental or nature legislation that requires environmental monitoring and retrospective ERA. This limitation is due mainly to the availability of baselines, which define acceptable versus unacceptable environmental effects on the key attributes of sentinel species, populations and/or communities, such as reproductive status, abundance, or diversity. Nevertheless, very few regulatory case examples exist in which SPGs incorporate effect magnitude, spatial extent, and temporal duration. We conclude that more holistic approaches are needed for defining SPGs, particularly with respect to protecting population sustainability, ecosystem function, and integrity, which are implicit in generic protection goals and explicit in the International

Mechanisms of environmental chemicals that enable the cancer hallmark of evasion of growth suppression

PubMed Central

Nahta, Rita; Al-Mulla, Fahd; Al-Temaimi, Rabeah; Amedei, Amedeo; Andrade-Vieira, Rafaela; Bay, Sarah; G. Brown, Dustin; Calaf, Gloria M.; Castellino, Robert C.; Cohen-Solal, Karine A.; Colacci, Annamaria; Cruickshanks, Nichola; Dent, Paul; Di Fiore, Riccardo; Forte, Stefano; Goldberg, Gary S.; Hamid, Roslida A.; Krishnan, Harini; Laird, Dale W.; Lasfar, Ahmed; Marignani, Paola A.; Memeo, Lorenzo; Mondello, Chiara; Naus, Christian C.; Ponce-Cusi, Richard; Raju, Jayadev; Roy, Debasish; Roy, Rabindra; P. Ryan, Elizabeth; Salem, Hosni K.; Scovassi, A. Ivana; Singh, Neetu; Vaccari, Monica; Vento, Renza; Vondráček, Jan; Wade, Mark; Woodrick, Jordan; Bisson, William H.

2015-01-01

As part of the Halifax Project, this review brings attention to the potential effects of environmental chemicals on important molecular and cellular regulators of the cancer hallmark of evading growth suppression. Specifically, we review the mechanisms by which cancer cells escape the growth-inhibitory signals of p53, retinoblastoma protein, transforming growth factor-beta, gap junctions and contact inhibition. We discuss the effects of selected environmental chemicals on these mechanisms of growth inhibition and cross-reference the effects of these chemicals in other classical cancer hallmarks. PMID:26106139

Traits and causes of environmental loss-related chemical accidents in China based on co-word analysis.

PubMed

Wu, Desheng; Song, Yu; Xie, Kefan; Zhang, Baofeng

2018-04-25

Chemical accidents are major causes of environmental losses and have been debated due to the potential threat to human beings and environment. Compared with the single statistical analysis, co-word analysis of chemical accidents illustrates significant traits at various levels and presents data into a visual network. This study utilizes a co-word analysis of the keywords extracted from the Web crawling texts of environmental loss-related chemical accidents and uses the Pearson's correlation coefficient to examine the internal attributes. To visualize the keywords of the accidents, this study carries out a multidimensional scaling analysis applying PROXSCAL and centrality identification. The research results show that an enormous environmental cost is exacted, especially given the expected environmental loss-related chemical accidents with geographical features. Meanwhile, each event often brings more than one environmental impact. Large number of chemical substances are released in the form of solid, liquid, and gas, leading to serious results. Eight clusters that represent the traits of these accidents are formed, including "leakage," "poisoning," "explosion," "pipeline crack," "river pollution," "dust pollution," "emission," and "industrial effluent." "Explosion" and "gas" possess a strong correlation with "poisoning," located at the center of visualization map.

Prediction of novel synthetic pathways for the production of desired chemicals.

PubMed

Cho, Ayoun; Yun, Hongseok; Park, Jin Hwan; Lee, Sang Yup; Park, Sunwon

2010-03-28

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

(Q)SARs to predict environmental toxicities: current status and future needs.

PubMed

Cronin, Mark T D

2017-03-22

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

Prediction Metrics for Chemical Detection in Long-Wave Infrared Hyperspectral Imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chilton, Marie C.; Walsh, Stephen J.; Daly, Don S.

2009-01-29

A natural or anthropogenic process often generates a signature gas plume whose chemical constituents may be identified using hyperspectral imagery. A hyperspectral image is a pixel-indexed set of spectra where each spectrum reflects the chemical constituents of the plume, the atmosphere, the bounding background surface, and instrument noise. This study explored the relationship between gas absorbance and background emissivity across the long-wave infrared (LWIR) spectrum and how they affect relative gas detection sensitivity. The physics-based model for the observed radiance shows that high gas absorbance coupled with low background emissivity at a single wavenumber results in a stronger recorded radiance.more » Two sensitivity measures were developed to predict relative probability of detection using chemical absorbance and background emissivity: one focused on a single wavenumber while another accounted for the entire spectrum. The predictive abilities of these measures were compared to synthetic image analysis. This study simulated images with 499 distinct gases at each of 6 concentrations over 6 different background surfaces with the atmosphere and level of instrument noise held constant. The Whitened Matched Filter was used to define gas detection from an image spectrum. The estimate of a chemical’s probability of detection at a given concentration over a specific background was the proportion of detections in 500 trials. Of the 499 chemicals used in the images, 276 had estimated probabilities of detection below 0.2 across all backgrounds and concentrations; these chemicals were removed from the study. For 92.8 percent of the remaining chemicals, the single channel measure correctly predicted the background over which the chemical had the largest relative probability of detection. Further, the measure which accounted for information across all wavenumbers predicted the background over which the chemical had the largest relative probability of detection for

The Matthew effect in environmental science publication: A bibliometric analysis of chemical substances in journal articles

PubMed Central

2011-01-01

Background While environmental research addresses scientific questions of possible societal relevance, it is unclear to what degree research focuses on environmental chemicals in need of documentation for risk assessment purposes. Methods In a bibliometric analysis, we used SciFinder to extract Chemical Abstract Service (CAS) numbers for chemicals addressed by publications in the 78 major environmental science journals during 2000-2009. The Web of Science was used to conduct title searches to determine long-term trends for prominent substances and substances considered in need of research attention. Results The 119,636 journal articles found had 760,056 CAS number links during 2000-2009. The top-20 environmental chemicals consisted of metals, (chlorinated) biphenyls, polyaromatic hydrocarbons, benzene, and ethanol and contributed 12% toward the total number of links- Each of the top-20 substances was covered by 2,000-10,000 articles during the decade. The numbers for the 10-year period were similar to the total numbers of pre-2000 articles on the same chemicals. However, substances considered a high priority from a regulatory viewpoint, due to lack of documentation, showed very low publication rates. The persistence in the scientific literature of the top-20 chemicals was only weakly related to their publication in journals with a high impact factor, but some substances achieved high citation rates. Conclusions The persistence of some environmental chemicals in the scientific literature may be due to a 'Matthew' principle of maintaining prominence for the very reason of having been well researched. Such bias detracts from the societal needs for documentation on less well known environmental hazards, and it may also impact negatively on the potentials for innovation and discovery in research. PMID:22074398

The Matthew effect in environmental science publication: a bibliometric analysis of chemical substances in journal articles.

PubMed

Grandjean, Philippe; Eriksen, Mette L; Ellegaard, Ole; Wallin, Johan A

2011-11-10

While environmental research addresses scientific questions of possible societal relevance, it is unclear to what degree research focuses on environmental chemicals in need of documentation for risk assessment purposes. In a bibliometric analysis, we used SciFinder to extract Chemical Abstract Service (CAS) numbers for chemicals addressed by publications in the 78 major environmental science journals during 2000-2009. The Web of Science was used to conduct title searches to determine long-term trends for prominent substances and substances considered in need of research attention. The 119,636 journal articles found had 760,056 CAS number links during 2000-2009. The top-20 environmental chemicals consisted of metals, (chlorinated) biphenyls, polyaromatic hydrocarbons, benzene, and ethanol and contributed 12% toward the total number of links- Each of the top-20 substances was covered by 2,000-10,000 articles during the decade. The numbers for the 10-year period were similar to the total numbers of pre-2000 articles on the same chemicals. However, substances considered a high priority from a regulatory viewpoint, due to lack of documentation, showed very low publication rates. The persistence in the scientific literature of the top-20 chemicals was only weakly related to their publication in journals with a high impact factor, but some substances achieved high citation rates. The persistence of some environmental chemicals in the scientific literature may be due to a 'Matthew' principle of maintaining prominence for the very reason of having been well researched. Such bias detracts from the societal needs for documentation on less well known environmental hazards, and it may also impact negatively on the potentials for innovation and discovery in research.

Predicting Formation Damage in Aquifer Thermal Energy Storage Systems Utilizing a Coupled Hydraulic-Thermal-Chemical Reservoir Model

NASA Astrophysics Data System (ADS)

Müller, Daniel; Regenspurg, Simona; Milsch, Harald; Blöcher, Guido; Kranz, Stefan; Saadat, Ali

2014-05-01

In aquifer thermal energy storage (ATES) systems, large amounts of energy can be stored by injecting hot water into deep or intermediate aquifers. In a seasonal production-injection cycle, water is circulated through a system comprising the porous aquifer, a production well, a heat exchanger and an injection well. This process involves large temperature and pressure differences, which shift chemical equilibria and introduce or amplify mechanical processes. Rock-fluid interaction such as dissolution and precipitation or migration and deposition of fine particles will affect the hydraulic properties of the porous medium and may lead to irreversible formation damage. In consequence, these processes determine the long-term performance of the ATES system and need to be predicted to ensure the reliability of the system. However, high temperature and pressure gradients and dynamic feedback cycles pose challenges on predicting the influence of the relevant processes. Within this study, a reservoir model comprising a coupled hydraulic-thermal-chemical simulation was developed based on an ATES demonstration project located in the city of Berlin, Germany. The structural model was created with Petrel, based on data available from seismic cross-sections and wellbores. The reservoir simulation was realized by combining the capabilities of multiple simulation tools. For the reactive transport model, COMSOL Multiphysics (hydraulic-thermal) and PHREEQC (chemical) were combined using the novel interface COMSOL_PHREEQC, developed by Wissmeier & Barry (2011). It provides a MATLAB-based coupling interface between both programs. Compared to using COMSOL's built-in reactive transport simulator, PHREEQC additionally calculates adsorption and reaction kinetics and allows the selection of different activity coefficient models in the database. The presented simulation tool will be able to predict the most important aspects of hydraulic, thermal and chemical transport processes relevant to

The UKC2 regional coupled environmental prediction system

NASA Astrophysics Data System (ADS)

Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

2018-01-01

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries.

PubMed

Zhu, Zhengqiu; Chen, Bin; Qiu, Sihang; Wang, Rongxiao; Chen, Feiran; Wang, Yiping; Qiu, Xiaogang

2018-03-27

Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker's parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker's observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants.

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries

PubMed Central

Wang, Rongxiao; Chen, Feiran; Wang, Yiping; Qiu, Xiaogang

2018-01-01

Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker’s observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants. PMID:29584679

Revolution In Toxicity Testing And Risk Prediction For Chemicals In The Environment (ASA)

EPA Science Inventory

Addressing safety aspects of drugs and environmental chemicals relies extensively on animal testing; however, the quantity of chemicals needing assessment and challenges of species extrapolation require alternative approaches to traditional animal studies. Newer in vitro and in s...

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200

Chemical Fingerprinting of Materials Developed Due To Environmental Issues

NASA Technical Reports Server (NTRS)

Smith, Doris A.; McCool, A. (Technical Monitor)

2000-01-01

This paper presents viewgraphs on chemical fingerprinting of materials developed due to environmental issues. Some of the topics include: 1) Aerospace Materials; 2) Building Blocks of Capabilities; 3) Spectroscopic Techniques; 4) Chromatographic Techniques; 5) Factors that Determine Fingerprinting Approach; and 6) Fingerprinting: Combination of instrumental analysis methods that diagnostically characterize a material.

TOXCAST: A PROGRAM FOR PRIORTITIZING TOXICITY TESTING OF ENVIRONMENTAL CHEMICALS

EPA Science Inventory

Evaluating the potential of tens of thousands of chemicals for risk to human health and the environment is beyond the resource limits of the Environmental Protection Agency. The EPA's ToxCast program will explore alternative methods comprising computational chemistry, high-throug...

Molecular building blocks and their architecture in biologically/environmentally compatible soft matter chemical machinery.

PubMed

Toyota, Taro; Banno, Taisuke; Nitta, Sachiko; Takinoue, Masahiro; Nomoto, Tomonori; Natsume, Yuno; Matsumura, Shuichi; Fujinami, Masanori

2014-01-01

This review briefly summarizes recent developments in the construction of biologically/environmentally compatible chemical machinery composed of soft matter. Since environmental and living systems are open systems, chemical machinery must continuously fulfill its functions not only through the influx and generation of molecules but also via the degradation and dissipation of molecules. If the degradation or dissipation of soft matter molecular building blocks and biomaterial molecules/polymers can be achieved, soft matter particles composed of them can be used to realize chemical machinery such as selfpropelled droplets, drug delivery carriers, tissue regeneration scaffolds, protocell models, cell-/tissuemarkers, and molecular computing systems.

Novel High-Fidelity Screening of Environmental Chemicals and Carcinogens and Mechanisms in Colorectal Cancer.

DTIC Science & Technology

2016-09-01

Chemical Promiscuity, Pharmacokinetics, Colorectal Cancer, N , N ’-disalicylidene-1,2-diaminopropane, Pyraclostrobin, Paclobutrazol, Vitamin D Receptor, Wnt...Environmental Chemicals, TOX-TMFS, CPTM, Cancer Cellular Network Model, Chemical Reactivity, Chemical Promiscuity, Pharmacokinetics, Colorectal Cancer, N , N ...network models were further enriched with oncologic disease OMIM profiles to create cancer-specific networks. The ECs N , N ’-disalicylidene- 1,2

Are there other persistent organic pollutants? A challenge for environmental chemists.

PubMed

Muir, Derek C G; Howard, Philip H

2006-12-01

The past 5 years have seen some major successes in terms of global measurement and regulation of persistent, bioaccumulative, and toxic (PB&T) chemicals and persistent organic pollutants (POPs). The Stockholm Convention, a global agreement on POPs, came into force in 2004. There has been a major expansion of measurements and risk assessments of new chemical contaminants in the global environment, particularly brominated diphenyl ethers and perfluorinated alkyl acids. However, the list of chemicals measured represents only a small fraction of the approximately 30,000 chemicals widely used in commerce (>1 t/y). The vast majority of existing and new chemical substances in commerce are not monitored in environmental media. Assessment and screening of thousands of existing chemicals in commerce in the United States, Europe, and Canada have yielded lists of potentially persistent and bioaccumulative chemicals. Here we review recent screening and categorization studies of chemicals in commerce and address the question of whether there is now sufficient information to permit a broader array of chemicals to be determined in environmental matrices. For example, Environment Canada's recent categorization of the Domestic (existing) Substances list, using a wide array of quantitative structure activity relationships for PB&T characteristics, has identified about 5.5% of 11,317 substances as meeting P & B criteria. Using data from the Environment Canada categorization, we have listed, for discussion purposes, 30 chemicals with high predicted bioconcentration and low rate of biodegradation and 28 with long range atmospheric transport potential based on predicted atmospheric oxidation half-lives >2 days and log air-water partition coefficients > or =5 and < or =1. These chemicals are a diverse group including halogenated organics, cyclic siloxanes, and substituted aromatics. Some of these chemicals and their transformation products may be candidates for future environmental

Endocrine-disrupting chemicals and oil and natural gas operations: Potential environmental contamination and recommendations to assess complex environmental mixtures

USGS Publications Warehouse

Kassotis, Christopher D.; Tillitt, Donald E.; Lin, Chung-Ho; McElroy, Jane A.; Nagel, Susan C.

2016-01-01

Background: Hydraulic fracturing technologies, developed over the last 65 years, have only recently been combined with horizontal drilling to unlock oil and gas reserves previously deemed inaccessible. While these technologies have dramatically increased domestic oil and natural gas production, they have also raised concerns for the potential contamination of local water supplies with the approximately 1,000 chemicals used throughout the process, including many known or suspected endocrine-disrupting chemicals.Objectives: We discuss the need for an endocrine component to health assessments for drilling-dense regions in the context of hormonal and anti-hormonal activities for chemicals used.Methods: We discuss the literature on 1) surface and ground water contamination by oil and gas extraction operations, and 2) potential human exposure, particularly in context of the total hormonal and anti-hormonal activities present in surface and ground water from natural and anthropogenic sources, with initial analytical results and critical knowledge gaps discussed.Discussion: In light of the potential for environmental release of oil and gas chemicals that can disrupt hormone receptor systems, we recommend methods for assessing complex hormonally active environmental mixtures.Conclusions: We describe a need for an endocrine-centric component for overall health assessments and provide supporting information that using this may help explain reported adverse health trends as well as help develop recommendations for environmental impact assessments and monitoring programs.

Endocrine-Disrupting Chemicals and Oil and Natural Gas Operations: Potential Environmental Contamination and Recommendations to Assess Complex Environmental Mixtures.

PubMed

Kassotis, Christopher D; Tillitt, Donald E; Lin, Chung-Ho; McElroy, Jane A; Nagel, Susan C

2016-03-01

Hydraulic fracturing technologies, developed over the last 65 years, have only recently been combined with horizontal drilling to unlock oil and gas reserves previously deemed inaccessible. Although these technologies have dramatically increased domestic oil and natural gas production, they have also raised concerns for the potential contamination of local water supplies with the approximately 1,000 chemicals that are used throughout the process, including many known or suspected endocrine-disrupting chemicals. We discuss the need for an endocrine component to health assessments for drilling-dense regions in the context of hormonal and antihormonal activities for chemicals used. We discuss the literature on a) surface and groundwater contamination by oil and gas extraction operations, and b) potential human exposure, particularly in the context of the total hormonal and antihormonal activities present in surface and groundwater from natural and anthropogenic sources; we also discuss initial analytical results and critical knowledge gaps. In light of the potential for environmental release of oil and gas chemicals that can disrupt hormone receptor systems, we recommend methods for assessing complex hormonally active environmental mixtures. We describe a need for an endocrine-centric component for overall health assessments and provide information supporting the idea that using such a component will help explain reported adverse health trends as well as help develop recommendations for environmental impact assessments and monitoring programs.

AI AND SAR APPROACHES FOR PREDICTING CHEMICAL CARCINOGENICITY: SURVEY AND STATUS REPORT

EPA Science Inventory

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoin...

A generic, cross-chemical predictive PBTK model with multiple entry routes running as application in MS Excel; design of the model and comparison of predictions with experimental results.

PubMed

Jongeneelen, Frans J; Berge, Wil F Ten

2011-10-01

Physiologically based toxicokinetic (PBTK) models are computational tools, which simulate the absorption, distribution, metabolism, and excretion of chemicals. The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model with a high level of transparency. The model should be able to predict blood and urine concentrations of environmental chemicals and metabolites, given a certain environmental or occupational exposure scenario. The model refers to a reference human of 70 kg. The partition coefficients of the parent compound and its metabolites (blood:air and tissue:blood partition coefficients of 11 organs) are estimated by means of quantitative structure-property relationship, in which five easily available physicochemical properties of the compound are the independent parameters. The model gives a prediction of the fate of the compound, based on easily available chemical properties; therefore, it can be applied as a generic model applicable to multiple compounds. Three routes of uptake are considered (inhalation, dermal, and/or oral) as well as two built-in exercise levels (at rest and at light work). Dermal uptake is estimated by the use of a dermal diffusion-based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Saturable metabolism according to Michaelis-Menten kinetics can be modelled in any of 11 organs/tissues or in liver only. Renal tubular resorption is based on a built-in algorithm, dependent on the (log) octanol:water partition coefficient. Enterohepatic circulation is optional at a user-defined rate. The generic PBTK model is available as a spreadsheet application in MS Excel. The differential equations of the model are programmed in Visual Basic. Output is presented as numerical listing over time in tabular form and in graphs. The MS Excel application of the PBTK model is available as

Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions.

PubMed

Bennett, Erin R; Clausen, Jay; Linkov, Eugene; Linkov, Igor

2009-11-01

Reliable, up-front information on physical and biological properties of emerging materials is essential before making a decision and investment to formulate, synthesize, scale-up, test, and manufacture a new material for use in both military and civilian applications. Multiple quantitative structure-activity relationships (QSARs) software tools are available for predicting a material's physical/chemical properties and environmental effects. Even though information on emerging materials is often limited, QSAR software output is treated without sufficient uncertainty analysis. We hypothesize that uncertainty and variability in material properties and uncertainty in model prediction can be too large to provide meaningful results. To test this hypothesis, we predicted octanol water partitioning coefficients (logP) for multiple, similar compounds with limited physical-chemical properties using six different commercial logP calculators (KOWWIN, MarvinSketch, ACD/Labs, ALogP, CLogP, SPARC). Analysis was done for materials with largely uncertain properties that were similar, based on molecular formula, to military compounds (RDX, BTTN, TNT) and pharmaceuticals (Carbamazepine, Gemfibrizol). We have also compared QSAR modeling results for a well-studied pesticide and pesticide breakdown product (Atrazine, DDE). Our analysis shows variability due to structural variations of the emerging chemicals may be several orders of magnitude. The model uncertainty across six software packages was very high (10 orders of magnitude) for emerging materials while it was low for traditional chemicals (e.g. Atrazine). Thus the use of QSAR models for emerging materials screening requires extensive model validation and coupling QSAR output with available empirical data and other relevant information.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

PubMed Central

van Zanten, Martijn

2015-01-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation. PMID:26496492

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PubMed

van Heerwaarden, Joost; van Zanten, Martijn; Kruijer, Willem

2015-10-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

High-Throughput Models for Exposure-Based Chemical ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlie

Revolutionizing Toxicity Testing For Predicting Developmental Outcomes (DNT4)

EPA Science Inventory

Characterizing risk from environmental chemical exposure currently requires extensive animal testing; however, alternative approaches are being researched to increase throughput of chemicals screened, decrease reliance on animal testing, and improve accuracy in predicting adverse...

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

Mechanisms of environmental chemicals that enable the cancer hallmark of evasion of growth suppression.

PubMed

Nahta, Rita; Al-Mulla, Fahd; Al-Temaimi, Rabeah; Amedei, Amedeo; Andrade-Vieira, Rafaela; Bay, Sarah N; Brown, Dustin G; Calaf, Gloria M; Castellino, Robert C; Cohen-Solal, Karine A; Colacci, Annamaria; Cruickshanks, Nichola; Dent, Paul; Di Fiore, Riccardo; Forte, Stefano; Goldberg, Gary S; Hamid, Roslida A; Krishnan, Harini; Laird, Dale W; Lasfar, Ahmed; Marignani, Paola A; Memeo, Lorenzo; Mondello, Chiara; Naus, Christian C; Ponce-Cusi, Richard; Raju, Jayadev; Roy, Debasish; Roy, Rabindra; Ryan, Elizabeth P; Salem, Hosni K; Scovassi, A Ivana; Singh, Neetu; Vaccari, Monica; Vento, Renza; Vondráček, Jan; Wade, Mark; Woodrick, Jordan; Bisson, William H

2015-06-01

As part of the Halifax Project, this review brings attention to the potential effects of environmental chemicals on important molecular and cellular regulators of the cancer hallmark of evading growth suppression. Specifically, we review the mechanisms by which cancer cells escape the growth-inhibitory signals of p53, retinoblastoma protein, transforming growth factor-beta, gap junctions and contact inhibition. We discuss the effects of selected environmental chemicals on these mechanisms of growth inhibition and cross-reference the effects of these chemicals in other classical cancer hallmarks. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. For example, high impact weather is typically manifested through various interactions and feedbacks between different components of the Earth System. Damaging high winds can lead to significant damage from the large waves and storm surge along coastlines. The impact of intense rainfall can be translated through saturated soils and land surface processes, high river flows and flooding inland. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system and discuss progress and initial results from further development to integrate wave interactions. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.

PubMed

Chen, Lei; Lu, Jing; Zhang, Ning; Huang, Tao; Cai, Yu-Dong

2014-04-01

In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.

Overview of the ToxCast Research Program: Applications to Predictive Toxicology and Chemical Prioritization (SETAC)

EPA Science Inventory

Understanding the potential health risks posed by environmental chemicals is a significant challenge driven by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms and toxicities. The U.S. EPA’s ToxCast chemical prioritization research projec...

Characterizing the Estrogenic Potential of 1060 Environmental Chemicals by Assessing Growth Kinetics in T47D Cells

EPA Science Inventory

In order to detect environmental chemicals that pose a risk of endocrine disruption, high-throughput screening (HTS) tests capable of testing thousands of environmental chemicals are needed. Alteration of estrogen signaling has been implicated in a variety of adverse health effec...

Role of Environmental Chemicals in Diabetes and Obesity: A National Toxicology Program Workshop Review

PubMed Central

Heindel, Jerrold J.; Bucher, John R.; Gallo, Michael A.

2012-01-01

Background: There has been increasing interest in the concept that exposures to environmental chemicals may be contributing factors to the epidemics of diabetes and obesity. On 11–13 January 2011, the National Institute of Environmental Health Sciences (NIEHS) Division of the National Toxicology Program (NTP) organized a workshop to evaluate the current state of the science on these topics of increasing public health concern. Objective: The main objective of the workshop was to develop recommendations for a research agenda after completing a critical analysis of the literature for humans and experimental animals exposed to certain environmental chemicals. The environmental exposures considered at the workshop were arsenic, persistent organic pollutants, maternal smoking/nicotine, organotins, phthalates, bisphenol A, and pesticides. High-throughput screening data from Toxicology in the 21st Century (Tox21) were also considered as a way to evaluate potential cellular pathways and generate -hypotheses for testing which and how certain chemicals might perturb biological processes related to diabetes and obesity. Conclusions: Overall, the review of the existing literature identified linkages between several of the environmental exposures and type 2 diabetes. There was also support for the “developmental obesogen” hypothesis, which suggests that chemical exposures may increase the risk of obesity by altering the differentiation of adipocytes or the development of neural circuits that regulate feeding behavior. The effects may be most apparent when the developmental exposure is combined with consumption of a high-calorie, high-carbohydrate, or high-fat diet later in life. Research on environmental chemical exposures and type 1 diabetes was very limited. This lack of research was considered a critical data gap. In this workshop review, we outline the major themes that emerged from the workshop and discuss activities that NIEHS/NTP is undertaking to address research

The effect of environmental chemicals on the tumor microenvironment

PubMed Central

Casey, Stephanie C.; Vaccari, Monica; Al-Mulla, Fahd; Al-Temaimi, Rabeah; Amedei, Amedeo; Barcellos-Hoff, Mary Helen; Brown, Dustin G.; Chapellier, Marion; Christopher, Joseph; Curran, Colleen S.; Forte, Stefano; Hamid, Roslida A.; Heneberg, Petr; Koch, Daniel C.; Krishnakumar, P.K.; Laconi, Ezio; Maguer-Satta, Veronique; Marongiu, Fabio; Memeo, Lorenzo; Mondello, Chiara; Raju, Jayadev; Roman, Jesse; Roy, Rabindra; Ryan, Elizabeth P.; Ryeom, Sandra; Salem, Hosni K.; Scovassi, A.Ivana; Singh, Neetu; Soucek, Laura; Vermeulen, Louis; Whitfield, Jonathan R.; Woodrick, Jordan; Colacci, Anna Maria; Bisson, William H.; Felsher, Dean W.

2015-01-01

Potentially carcinogenic compounds may cause cancer through direct DNA damage or through indirect cellular or physiological effects. To study possible carcinogens, the fields of endocrinology, genetics, epigenetics, medicine, environmental health, toxicology, pharmacology and oncology must be considered. Disruptive chemicals may also contribute to multiple stages of tumor development through effects on the tumor microenvironment. In turn, the tumor microenvironment consists of a complex interaction among blood vessels that feed the tumor, the extracellular matrix that provides structural and biochemical support, signaling molecules that send messages and soluble factors such as cytokines. The tumor microenvironment also consists of many host cellular effectors including multipotent stromal cells/mesenchymal stem cells, fibroblasts, endothelial cell precursors, antigen-presenting cells, lymphocytes and innate immune cells. Carcinogens can influence the tumor microenvironment through effects on epithelial cells, the most common origin of cancer, as well as on stromal cells, extracellular matrix components and immune cells. Here, we review how environmental exposures can perturb the tumor microenvironment. We suggest a role for disrupting chemicals such as nickel chloride, Bisphenol A, butyltins, methylmercury and paraquat as well as more traditional carcinogens, such as radiation, and pharmaceuticals, such as diabetes medications, in the disruption of the tumor microenvironment. Further studies interrogating the role of chemicals and their mixtures in dose-dependent effects on the tumor microenvironment could have important general mechanistic implications for the etiology and prevention of tumorigenesis. PMID:26106136

The environmental injustice of beauty: framing chemical exposures from beauty products as a health disparities concern.

PubMed

Zota, Ami R; Shamasunder, Bhavna

2017-10-01

The obstetrics-gynecology community has issued a call to action to prevent toxic environmental chemical exposures and their threats to healthy human reproduction. Recent committee opinions recognize that vulnerable and underserved women may be impacted disproportionately by environmental chemical exposures and recommend that reproductive health professionals champion policies that secure environmental justice. Beauty product use is an understudied source of environmental chemical exposures. Beauty products can include reproductive and developmental toxicants such as phthalates and heavy metals; however, disclosure requirements are limited and inconsistent. Compared with white women, women of color have higher levels of beauty product-related environmental chemicals in their bodies, independent of socioeconomic status. Even small exposures to toxic chemicals during critical periods of development (such as pregnancy) can trigger adverse health consequences (such as impacts on fertility and pregnancy, neurodevelopment, and cancer). In this commentary, we seek to highlight the connections between environmental justice and beauty product-related chemical exposures. We describe racial/ethnic differences in beauty product use (such as skin lighteners, hair straighteners, and feminine hygiene products) and the potential chemical exposures and health risks that are associated with these products. We also discuss how targeted advertising can take advantage of mainstream beauty norms to influence the use of these products. Reproductive health professionals can use this information to advance environmental justice by being prepared to counsel patients who have questions about toxic environmental exposures from beauty care products and other sources. Researchers and healthcare providers can also promote health-protective policies such as improved ingredient testing and disclosure for the beauty product industry. Future clinical and public health research should consider beauty

Environmental Decontamination of a Chemical Warfare Simulant Utilizing a Membrane Vesicle-Encapsulated Phosphotriesterase.

PubMed

Alves, Nathan J; Moore, Martin; Johnson, Brandy J; Dean, Scott N; Turner, Kendrick B; Medintz, Igor L; Walper, Scott A

2018-05-09

While technologies for the remediation of chemical contaminants continue to emerge, growing interest in green technologies has led researchers to explore natural catalytic mechanisms derived from microbial species. One such method, enzymatic degradation, offers an alternative to harsh chemical catalysts and resins. Recombinant enzymes, however, are often too labile or show limited activity when challenged with nonideal environmental conditions that may vary in salinity, pH, or other physical properties. Here, we demonstrate how phosphotriesterase encapsulated in a bacterial outer membrane vesicle can be used to degrade the organophosphate chemical warfare agent (CWA) simulant paraoxon in environmental water samples. We also carried out remediation assays on solid surfaces, including glass, painted metal, and fabric, that were selected as representative materials, which could potentially be contaminated with a CWA.

Local environmental quality positively predicts breastfeeding in the UK's Millennium Cohort Study.

PubMed

Brown, Laura J; Sear, Rebecca

2017-01-01

Background and Objectives: Breastfeeding is an important form of parental investment with clear health benefits. Despite this, rates remain low in the UK; understanding variation can therefore help improve interventions. Life history theory suggests that environmental quality may pattern maternal investment, including breastfeeding. We analyse a nationally representative dataset to test two predictions: (i) higher local environmental quality predicts higher likelihood of breastfeeding initiation and longer duration; (ii) higher socioeconomic status (SES) provides a buffer against the adverse influences of low local environmental quality. Methodology: We ran factor analysis on a wide range of local-level environmental variables. Two summary measures of local environmental quality were generated by this analysis-one 'objective' (based on an independent assessor's neighbourhood scores) and one 'subjective' (based on respondent's scores). We used mixed-effects regression techniques to test our hypotheses. Results: Higher objective, but not subjective, local environmental quality predicts higher likelihood of starting and maintaining breastfeeding over and above individual SES and area-level measures of environmental quality. Higher individual SES is protective, with women from high-income households having relatively high breastfeeding initiation rates and those with high status jobs being more likely to maintain breastfeeding, even in poor environmental conditions. Conclusions and Implications: Environmental quality is often vaguely measured; here we present a thorough investigation of environmental quality at the local level, controlling for individual- and area-level measures. Our findings support a shift in focus away from individual factors and towards altering the landscape of women's decision making contexts when considering behaviours relevant to public health.

77 FR 12867 - Accreditation of ALTOL Chemical and Environmental Lab Inc., as a Commercial Laboratory

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-02

... accredited to test petroleum, petroleum products, organic chemicals and vegetable oils for customs purposes... DEPARTMENT OF HOMELAND SECURITY U.S. Customs and Border Protection Accreditation of ALTOL Chemical..., Department of Homeland Security. ACTION: Notice of accreditation of Altol Chemical and Environmental Lab Inc...

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

Chemical, biochemical, and environmental fiber sensors III; Proceedings of the Meeting, Boston, MA, Sept. 4, 5, 1991

NASA Astrophysics Data System (ADS)

Lieberman, Robert A.

Various papers on chemical, biochemical, and environmental fiber sensors are presented. Individual topics addressed include: fiber optic pressure sensor for combustion monitoring and control, viologen-based fiber optic oxygen sensors, renewable-reagent fiber optic sensor for ocean pCO2, transition metal complexes as indicators for a fiber optic oxygen sensor, fiber optic pH measurements using azo indicators, simple reversible fiber optic chemical sensors using solvatochromic dyes, totally integrated optical measuring sensors, integrated optic biosensor for environmental monitoring, radiation dosimetry using planar waveguide sensors, optical and piezoelectric analysis of polymer films for chemical sensor characterization, source polarization effects in an optical fiber fluorosensor, lens-type refractometer for on-line chemical analysis, fiber optic hydrocarbon sensor system, chemical sensors for environmental monitoring, optical fibers for liquid-crystal sensing and logic devices, suitability of single-mode fluoride fibers for evanescent-wave sensing, integrated modules for fiber optic sensors, optoelectronic sensors based on narrowband A3B5 alloys, fiber Bragg grating chemical sensor.

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions.

PubMed

Hartman, Joshua D; Day, Graeme M; Beran, Gregory J O

2016-11-02

Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13 C and 15 N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study.

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions

PubMed Central

2016-01-01

Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13C and 15N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study. PMID:27829821

Partition of environmental chemicals between maternal and fetal blood and tissues.

PubMed

Needham, Larry L; Grandjean, Philippe; Heinzow, Birger; Jørgensen, Poul J; Nielsen, Flemming; Patterson, Donald G; Sjödin, Andreas; Turner, Wayman E; Weihe, Pal

2011-02-01

Passage of environmental chemicals across the placenta has important toxicological consequences, as well as for choosing samples for analysis and for interpreting the results. To obtain systematic data, we collected in 2000 maternal and cord blood, cord tissue, placenta, and milk in connection with births in the Faroe Islands, where exposures to marine contaminants is increased. In 15 sample sets, we measured a total of 87 environmental chemicals, almost all of which were detected both in maternal and fetal tissues. The maternal serum lipid-based concentrations of organohalogen compounds averaged 1.7 times those of cord serum, 2.8 times those of cord tissue and placenta, and 0.7 those of milk. For organohalogen compounds detectable in all matrices, a high degree of correlation between concentrations in maternal serum and the other tissues investigated was generally observed (r(2) > 0.5). Greater degree of chlorination resulted in lower transfer from maternal serum into milk. Concentrations of pentachlorbenzene, γ-hexachlorocyclohexane, and several polychlorinated biphenyl congeners with low chlorination were higher in fetal samples and showed poor correlation with maternal levels. Perfluorinated compounds occurred in lower concentrations in cord serum than in maternal serum. Cadmium, lead, mercury, and selenium were all detected in fetal samples, but only mercury showed close correlations among concentrations in different matrices. Although the environmental chemicals examined pass through the placenta and are excreted into milk, partitions between maternal and fetal samples are not uniform.

Disruptive environmental chemicals and cellular mechanisms that confer resistance to cell death

PubMed Central

Narayanan, Kannan Badri; Ali, Manaf; Barclay, Barry J.; Cheng, Qiang (Shawn); D’Abronzo, Leandro; Dornetshuber-Fleiss, Rita; Ghosh, Paramita M.; Gonzalez Guzman, Michael J.; Lee, Tae-Jin; Leung, Po Sing; Li, Lin; Luanpitpong, Suidjit; Ratovitski, Edward; Rojanasakul, Yon; Romano, Maria Fiammetta; Romano, Simona; Sinha, Ranjeet K.; Yedjou, Clement; Al-Mulla, Fahd; Al-Temaimi, Rabeah; Amedei, Amedeo; Brown, Dustin G.; Ryan, Elizabeth P.; Colacci, Anna Maria; Hamid, Roslida A.; Mondello, Chiara; Raju, Jayadev; Salem, Hosni K.; Woodrick, Jordan; Scovassi, A.Ivana; Singh, Neetu; Vaccari, Monica; Roy, Rabindra; Forte, Stefano; Memeo, Lorenzo; Kim, Seo Yun; Bisson, William H.; Lowe, Leroy; Park, Hyun Ho

2015-01-01

Cell death is a process of dying within biological cells that are ceasing to function. This process is essential in regulating organism development, tissue homeostasis, and to eliminate cells in the body that are irreparably damaged. In general, dysfunction in normal cellular death is tightly linked to cancer progression. Specifically, the up-regulation of pro-survival factors, including oncogenic factors and antiapoptotic signaling pathways, and the down-regulation of pro-apoptotic factors, including tumor suppressive factors, confers resistance to cell death in tumor cells, which supports the emergence of a fully immortalized cellular phenotype. This review considers the potential relevance of ubiquitous environmental chemical exposures that have been shown to disrupt key pathways and mechanisms associated with this sort of dysfunction. Specifically, bisphenol A, chlorothalonil, dibutyl phthalate, dichlorvos, lindane, linuron, methoxychlor and oxyfluorfen are discussed as prototypical chemical disruptors; as their effects relate to resistance to cell death, as constituents within environmental mixtures and as potential contributors to environmental carcinogenesis. PMID:26106145

Chemical combination effects predict connectivity in biological systems

PubMed Central

Lehár, Joseph; Zimmermann, Grant R; Krueger, Andrew S; Molnar, Raymond A; Ledell, Jebediah T; Heilbut, Adrian M; Short, Glenn F; Giusti, Leanne C; Nolan, Garry P; Magid, Omar A; Lee, Margaret S; Borisy, Alexis A; Stockwell, Brent R; Keith, Curtis T

2007-01-01

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured. PMID:17332758

Research review. Interactions between environmental chemicals and drug biotransformation in man.

PubMed

Alvares, A P

1978-01-01

Many factors influence the metabolism of drugs in man. Besides genetic factors, environmental factors may play a significant role in explaining the variation observed in the rates of drug metabolism between different individuals. Intentional or unintentional exposure to environmental chemicals could enhance or inhibit the activity of hepatic mixed function oxidases that metabolise drugs and other foreign chemicals, as well as endogenous substrates such as steroid hormones. A major source of such exposure may be occupational. Exposure to the heavy metal, lead, has been shown to inhibit drug metabolism; whereas intensive exposure to chlorinated insecticides, and other halogenated hydrocarbons such as polychlorinated biphenyls, has been shown to enhance the metabolism of test drugs such as antipyrine and phenylbutazone. An intentional source of exposure to foreign chemicals is cigarette smoke. Cigarette smoke contains polycyclic hydrocarbons, which are known inducers of hepatic mixed function oxidases. A number of studies have shown that cigarette smoking can alter the pharmacological action and/or the metabolism of some drugs. Pharmacokinetic studies have shown that cigarette smoking decreases the bioavailability of phenacetin and increases dosage requirements of theophylline by enhancing their rate of metabolism. Data, which are not very conclusive, indicate that heavy marijuana use may have an inhibitory effect on metabolism of some drugs and an inducing effect on others such as theophylline. Dietary factors may also play a significant role in the regulation of drug metabolism. Charcoal broiling which introduces polycyclic hydrocarbons into foods has been shown to enhance the metabolism of the test drug, antipyrine, and of such commonly used drugs as phenacetin and theophylline. Such intentional or unintentional exposure to environmental chemicals which may alter the rates of drug metabolism in man indicates the importance of individualisation of drug therapy.

THYROID DISRUPTING CHEMICALS: CHALLENGES IN ASSESSING NEUROTOXIC RISK FROM ENVIRONMENTAL MIXTURES.

EPA Science Inventory

Environmental contaminants are known to act as thyroid disrupting chemicals (TDCs). Broadly defined, TDCs are xenobiotics that alter the structure or function of the thyroid gland, alter regulatory enzymes associated with thyroid hormone (TH) homeostasis, or change circulating o...

Chemical structure-based predictive model for the oxidation of trace organic contaminants by sulfate radical.

PubMed

Ye, Tiantian; Wei, Zongsu; Spinney, Richard; Tang, Chong-Jian; Luo, Shuang; Xiao, Ruiyang; Dionysiou, Dionysios D

2017-06-01

Second-order rate constants [Formula: see text] for the reaction of sulfate radical anion (SO 4 •- ) with trace organic contaminants (TrOCs) are of scientific and practical importance for assessing their environmental fate and removal efficiency in water treatment systems. Here, we developed a chemical structure-based model for predicting [Formula: see text] using 32 molecular fragment descriptors, as this type of model provides a quick estimate at low computational cost. The model was constructed using the multiple linear regression (MLR) and artificial neural network (ANN) methods. The MLR method yielded adequate fit for the training set (R training 2 =0.88,n=75) and reasonable predictability for the validation set (R validation 2 =0.62,n=38). In contrast, the ANN method produced a more statistical robustness but rather poor predictability (R training 2 =0.99andR validation 2 =0.42). The reaction mechanisms of SO 4 •- reactivity with TrOCs were elucidated. Our result shows that the coefficients of functional groups reflect their electron donating/withdrawing characters. For example, electron donating groups typically exhibit positive coefficients, indicating enhanced SO 4 •- reactivity. Electron withdrawing groups exhibit negative values, indicating reduced reactivity. With its quick and accurate features, we applied this structure-based model to 55 discrete TrOCs culled from the Contaminant Candidate List 4, and quantitatively compared their removal efficiency with SO 4 •- and OH in the presence of environmental matrices. This high-throughput model helps prioritize TrOCs that are persistent to SO 4 •- based oxidation technologies at the screening level, and provide diagnostics of SO 4 •- reaction mechanisms. Copyright © 2017 Elsevier Ltd. All rights reserved.

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay.

PubMed

Maeda, Yosuke; Hirosaki, Haruka; Yamanaka, Hidenori; Takeyoshi, Masahiro

2018-05-23

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three-step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non-UV irradiation (UV-). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV- and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens. Copyright © 2018 John Wiley & Sons, Ltd.

STRUCTURE-ACTIVITY RELATIONSHIP STUIDES AND THEIR ROLE IN PREDICTING AND INVESTIGATING CHEMICAL TOXICITY

EPA Science Inventory

Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity

Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

PubMed

Bhhatarai, Barun; Teetz, Wolfram; Liu, Tao; Öberg, Tomas; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor V; Kovarich, Simona; Papa, Ester; Gramatica, Paola

2011-03-14

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

PubMed

Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

2004-10-07

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

Environmental chemicals and DNA methylation in adults: a systematic review of the epidemiologic evidence

USDA-ARS?s Scientific Manuscript database

Current evidence supports the notion that environmental exposures are associated with DNA-methylation and expression changes that can impact human health. Our objective was to conduct a systematic review of epidemiologic studies evaluating the association between environmental chemicals with DNA met...

Parental Concern about Environmental Chemical Exposures and Children's Urinary Concentrations of Phthalates and Phenols.

PubMed

Pell, Tripler; Eliot, Melissa; Chen, Aimin; Lanphear, Bruce P; Yolton, Kimberly; Sathyanarayana, Sheela; Braun, Joseph M

2017-07-01

To examine whether parents' concerns about environmental chemical exposures were associated with urinary phthalate and phenol concentrations in their school-age children. In a prospective cohort of 218 mother-child pairs from Cincinnati, Ohio (2010-2014), we measured 11 phthalate metabolites and 5 phenols in urine samples when children were age 8 years and used questionnaire data from caregivers. We estimated the covariate-adjusted percent difference in phthalates and phenols among children of parents who expressed concern about environmental chemical exposures compared with children whose parents did not. Concentrations of 4 phthalates, bisphenol S, and bisphenol A were lower among children whose parents expressed concern about environmental chemicals (n = 122) compared with those who did not (n = 96). Di-2-ethylhexyl phthalate metabolites, bisphenol S, and bisphenol A concentrations were 23% (95% CI -38, -5), 37% (95% CI -49, -21), and 13% (95% CI -26, 3) lower, respectively, among children whose parents expressed concern compared with those whose parents did not. Triclosan concentrations were 35% greater (95% CI -2, 87) among children whose parents expressed concern compared with children whose parents did not. Parental concern about environmental chemicals was associated with lower childhood urine concentrations of several phthalates and phenols; unexpectedly, parental concern was associated with greater triclosan concentrations. These results suggest that parental concern may be an important factor in mitigating children's phthalate and phenol exposures. Copyright © 2017 Elsevier Inc. All rights reserved.

Family Environmental and Genetic Influences on Children's Future Chemical Dependency.

ERIC Educational Resources Information Center

Kumpfer, Karol L.; DeMarsh, Joseph

1985-01-01

Discusses the following in relation to their predictability to future drug abuse in youth: (1) susceptibility of children of chemically dependent parents; (2) genetic transmutation; (3) family structure and management; (4) socialization; and (5) cognitive family characteristics. (Author/LHW)

Progress in High Throughput Exposure Assessment for Prioritizing Human Exposure to Environmental Chemicals (SRA)

EPA Science Inventory

For thousands of chemicals in commerce, there is little or no information about exposure or health and ecological effects. The US Environmental Protection Agency (USEPA) has ongoing research programs to develop and evaluate models that use the often minimal chemical information a...

Chemical Safety Alert: First Responders’ Environmental Liability Due To Mass Decontamination Runoff

EPA Pesticide Factsheets

CERCLA's good Samaritan provisions protect responders such as the Chemical Weapons Improved Response Team during lifesaving actions. Once imminent threats are addressed, responders should contain contamination and avoid/mitigate environmental consequences.

National differences in environmental concern and performance are predicted by country age.

PubMed

Hershfield, Hal E; Bang, H Min; Weber, Elke U

2014-01-01

There are obvious economic predictors of ability and willingness to invest in environmental sustainability. Yet, given that environmental decisions represent trade-offs between present sacrifices and uncertain future benefits, psychological factors may also play a role in country-level environmental behavior. Gott's principle suggests that citizens may use perceptions of their country's age to predict its future continuation, with longer pasts predicting longer futures. Using country- and individual-level analyses, we examined whether longer perceived pasts result in longer perceived futures, which in turn motivate concern for continued environmental quality. Study 1 found that older countries scored higher on an environmental performance index, even when the analysis controlled for country-level differences in gross domestic product and governance. Study 2 showed that when the United States was framed as an old country (vs. a young one), participants were willing to donate more money to an environmental organization. The findings suggest that framing a country as a long-standing entity may effectively prompt proenvironmental behavior.

Autoantibodies associated with prenatal and childhood exposure to environmental chemicals in Faroese children.

PubMed

Osuna, Christa E; Grandjean, Philippe; Weihe, Pál; El-Fawal, Hassan A N

2014-11-01

Methylmercury, polychlorinated biphenyls (PCBs), and perfluorinated compounds (PFCs) are ubiquitous and persistent environmental chemicals with known or suspected toxic effects on the nervous system and the immune system. Animal studies have shown that tissue damage can elicit production of autoantibodies. However, it is not known if autoantibodies similarly will be generated and detectable in humans following toxicant exposures. Therefore, we conducted a pilot study to investigate if autoantibodies specific for neural and non-neural antigens could be detected in children at age 7 years who have been exposed to environmental chemicals. Both prenatal and age-7 exposures to mercury, PCBs, and PFCs were measured in 38 children in the Faroe Islands who were exposed to widely different levels of these chemicals due to their seafood-based diet. Concentrations of IgM and IgG autoantibodies specific to both neural (neurofilaments, cholineacetyltransferase, astrocyte glial fibrillary acidic protein, and myelin basic protein) and non-neural (actin, desmin, and keratin) antigens were measured and the associations of these autoantibody concentrations with chemical exposures were assessed using linear regression. Age-7 blood-mercury concentrations were positively associated with titers of multiple neural- and non-neural-specific antibodies, mostly of the IgM isotype. Additionally, prenatal blood-mercury and -PCBs were negatively associated with anti-keratin IgG and prenatal PFOS was negatively associated with anti-actin IgG. These exploratory findings demonstrate that autoantibodies can be detected in the peripheral blood following exposure to environmental chemicals. The unexpected association of exposures with antibodies specific for non-neural antigens suggests that these chemicals may have toxicities that have not yet been recognized. © The Author 2014. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please

Children's environmental chemical exposures in the USA, NHANES 2003-2012.

PubMed

Hendryx, Michael; Luo, Juhua

2018-02-01

Children are vulnerable to environmental chemical exposures, but little is known about the extent of multiple chemical exposures among children. We analyzed biomonitoring data from five cycles (2003-2012) of the National Health and Nutrition Examination Survey (NHANES) to describe multiple chemical exposures in US children, examine levels of chemical concentrations present over time, and examine differences in chemical exposures by selected demographic groups. We analyzed data for 36 chemical analytes across five chemical classes in a sample of 4299 children aged 6-18. Classes included metals, pesticides, phthalates, phenols, and polycyclic aromatic hydrocarbons. We calculated the number and percent of chemicals detected and tested for secular trends over time in chemical concentrations. We compared log concentrations among groups defined by age, sex, race/ethnicity, and poverty using multiple linear regression models and report adjusted geometric means. Among a smaller subgroup of 733 children with data across chemical classes, we calculated the linear correlations within and between classes and conducted a principal component analysis. The percentage of children with detectable concentrations of an individual chemical ranged from 26 to 100%; the average was 93%, and 29 of 36 were detected in more than 90% of children. Concentrations of most tested chemicals were either unchanged or declined from earlier to more recent years. Many differences in concentrations were present by age, sex, poverty, and race/ethnicity categories. Within and between class correlations were all significant and positive, and the principal component analysis suggested a one factor solution, indicating that children exposed to higher levels of one chemical were exposed to higher levels of other chemicals. In conclusion, children in the USA are exposed to multiple simultaneous chemicals at uneven risk across socioeconomic and demographic groups. Further efforts to understand the effects of

Interactions between toxic chemicals and natural environmental factors--a meta-analysis and case studies.

PubMed

Laskowski, Ryszard; Bednarska, Agnieszka J; Kramarz, Paulina E; Loureiro, Susana; Scheil, Volker; Kudłek, Joanna; Holmstrup, Martin

2010-08-15

The paper addresses problems arising from effects of natural environmental factors on toxicity of pollutants to organisms. Most studies on interactions between toxicants and natural factors, including those completed in the EU project NoMiracle (Novel Methods for Integrated Risk Assessment of Cumulative Stressors in Europe) described herein, showed that effects of toxic chemicals on organisms can differ vastly depending purely on external conditions. We compiled data from 61 studies on effects of temperature, moisture and dissolved oxygen on toxicity of a range of chemicals representing pesticides, polycyclic aromatic hydrocarbons, plant protection products of bacterial origin and trace metals. In 62.3% cases significant interactions (p< or =0.05 or less) between natural factors and chemicals were found, reaching 100% for the effect of dissolved oxygen on toxicity of waterborne chemicals. The meta-analysis of the 61 studies showed that the null hypothesis assuming no interactions between toxic chemicals and natural environmental factors should be rejected at p=2.7 x 10(-82) (truncated product method probability). In a few cases of more complex experimental designs, also second-order interactions were found, indicating that natural factors can modify interactions among chemicals. Such data emphasize the necessity of including information on natural factors and their variation in time and across geographic regions in ecological risk assessment. This can be done only if appropriate ecotoxicological test designs are used, in which test organisms are exposed to toxicants at a range of environmental conditions. We advocate designing such tests for the second-tier ecological risk assessment procedures. Copyright 2010 Elsevier B.V. All rights reserved.

Environmental chemical exposures and disturbances of heme synthesis.

PubMed Central

Daniell, W E; Stockbridge, H L; Labbe, R F; Woods, J S; Anderson, K E; Bissell, D M; Bloomer, J R; Ellefson, R D; Moore, M R; Pierach, C A; Schreiber, W E; Tefferi, A; Franklin, G M

1997-01-01

Porphyrias are relatively uncommon inherited or acquired disorders in which clinical manifestations are attributable to a disturbance of heme synthesis (porphyrin metabolism), usually in association with endogenous or exogenous stressors. Porphyrias are characterized by elevations of heme precursors in blood, urine, and/or stool. A number of chemicals, particularly metals and halogenated hydrocarbons, induce disturbances of heme synthesis in experimental animals. Certain chemicals have also been linked to porphyria or porphyrinuria in humans, generally involving chronic industrial exposures or environmental exposures much higher than those usually encountered. A noteworthy example is the Turkish epidemic of porphyria cutanea tarda produced by accidental ingestion of wheat treated with the fungicide hexachlorobenzene. Measurements of excreted heme precursors have the potential to serve as biological markers for harmful but preclinical effects of certain chemical exposures; this potential warrants further research and applied field studies. It has been hypothesized that several otherwise unexplained chemical-associated illnesses, such as multiple chemical sensitivity syndrome, may represent mild chronic cases of porphyria or other acquired abnormalities in heme synthesis. This review concludes that, although it is reasonable to consider such hypotheses, there is currently no convincing evidence that these illnesses are mediated by a disturbance of heme synthesis; it is premature or unfounded to base clinical management on such explanations unless laboratory data are diagnostic for porphyria. This review discusses the limitations of laboratory measures of heme synthesis, and diagnostic guidelines are provided to assist in evaluating the symptomatic individual suspected of having a porphyria. PMID:9114276

Adaptive prediction of environmental changes by microorganisms.

PubMed

Mitchell, Amir; Romano, Gal H; Groisman, Bella; Yona, Avihu; Dekel, Erez; Kupiec, Martin; Dahan, Orna; Pilpel, Yitzhak

2009-07-09

Natural habitats of some microorganisms may fluctuate erratically, whereas others, which are more predictable, offer the opportunity to prepare in advance for the next environmental change. In analogy to classical Pavlovian conditioning, microorganisms may have evolved to anticipate environmental stimuli by adapting to their temporal order of appearance. Here we present evidence for environmental change anticipation in two model microorganisms, Escherichia coli and Saccharomyces cerevisiae. We show that anticipation is an adaptive trait, because pre-exposure to the stimulus that typically appears early in the ecology improves the organism's fitness when encountered with a second stimulus. Additionally, we observe loss of the conditioned response in E. coli strains that were repeatedly exposed in a laboratory evolution experiment only to the first stimulus. Focusing on the molecular level reveals that the natural temporal order of stimuli is embedded in the wiring of the regulatory network-early stimuli pre-induce genes that would be needed for later ones, yet later stimuli only induce genes needed to cope with them. Our work indicates that environmental anticipation is an adaptive trait that was repeatedly selected for during evolution and thus may be ubiquitous in biology.

Integrated Model of Chemical Perturbations of a Biological ...

EPA Pesticide Factsheets

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“”assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 52 (2.8%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in v

Environmental release of chemicals and reproductive ecology.

PubMed Central

Bajaj, J S; Misra, A; Rajalakshmi, M; Madan, R

1993-01-01

Reproductive ecology is defined as "the study of causes and mechanisms of the effects of environmental risk factors on reproductive health and the methods of their prevention and management." Major areas of concern, within the purview of this paper, relate to adverse pregnancy outcomes, effects on target tissues in the male and the female, and alterations in the control and regulatory mechanisms of reproductive processes. Teratogenic potential of chemicals, released as a result of accidents and catastrophes, is of critical significance. Congenital Minamata disease is due to transplacental fetal toxicity caused by accidental ingestion of methyl mercury. Generalized disorders of ectodermal tissue following prenatal exposure to polychlorinated biphenyls have been reported in Taiwan and Japan. The Bhopal gas disaster, a catastrophic industrial accident, was due to a leak of toxic gas, methyl isocyanate (MIC), in the pesticide manufacturing process. The outcome of pregnancy was studied in female survivors of MIC exposure. The spontaneous abortion rate was nearly four times more common in the affected areas as compared to the control area (24.2% versus 5.6%; p < 0.0001). Furthermore, while stillbirth rate was found to be similar in the affected and control areas, the perinatal and neonatal mortality rates were observed to be higher in the affected area. The rate of congenital malformations in the affected and control areas did not show any significant difference. Chromosomal aberrations and sister chromatid exchange (SCE) frequencies were investigated in human survivors of exposure. The observed SCE frequencies in control and exposed groups indicated that mutagenesis has been induced. Strategies for the management, prediction, and preventability of such disasters are outlined. PMID:8243381

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

Significance of vapor phase chemical reactions on CVD rates predicted by chemically frozen and local thermochemical equilibrium boundary layer theories

NASA Technical Reports Server (NTRS)

Gokoglu, Suleyman A.

1988-01-01

This paper investigates the role played by vapor-phase chemical reactions on CVD rates by comparing the results of two extreme theories developed to predict CVD mass transport rates in the absence of interfacial kinetic barrier: one based on chemically frozen boundary layer and the other based on local thermochemical equilibrium. Both theories consider laminar convective-diffusion boundary layers at high Reynolds numbers and include thermal (Soret) diffusion and variable property effects. As an example, Na2SO4 deposition was studied. It was found that gas phase reactions have no important role on Na2SO4 deposition rates and on the predictions of the theories. The implications of the predictions of the two theories to other CVD systems are discussed.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

PubMed

Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

STRESS PATHWAY-BASED REPORTER ASSAYS TO ASSESS TOXICITY OF ENVIRONMENTAL CHEMICALS.

EPA Science Inventory

There is an increasing need for assays for the rapid and efficient assessment of toxicities of large numbers of environmental chemicals. To meet this need, we are developing cell-based reporter assays that measure the activation of key molecular stress pathways. We are using pro...

Environmental Pollution, Toxicity Profile and Treatment Approaches for Tannery Wastewater and Its Chemical Pollutants.

PubMed

Saxena, Gaurav; Chandra, Ram; Bharagava, Ram Naresh

Leather industries are key contributors in the economy of many developing countries, but unfortunately they are facing serious challenges from the public and governments due to the associated environmental pollution. There is a public outcry against the industry due to the discharge of potentially toxic wastewater having alkaline pH, dark brown colour, unpleasant odour, high biological and chemical oxygen demand, total dissolved solids and a mixture of organic and inorganic pollutants. Various environment protection agencies have prioritized several chemicals as hazardous and restricted their use in leather processing however; many of these chemicals are used and discharged in wastewater. Therefore, it is imperative to adequately treat/detoxify the tannery wastewater for environmental safety. This paper provides a detail review on the environmental pollution and toxicity profile of tannery wastewater and chemicals. Furthermore, the status and advances in the existing treatment approaches used for the treatment and/or detoxification of tannery wastewater at both laboratory and pilot/industrial scale have been reviewed. In addition, the emerging treatment approaches alone or in combination with biological treatment approaches have also been considered. Moreover, the limitations of existing and emerging treatment approaches have been summarized and potential areas for further investigations have been discussed. In addition, the clean technologies for waste minimization, control and management are also discussed. Finally, the international legislation scenario on discharge limits for tannery wastewater and chemicals has also been discussed country wise with discharge standards for pollution prevention due to tannery wastewater.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.

Effect-Based Tools for Monitoring and Predicting the Ecotoxicological Effects of Chemicals in the Aquatic Environment

PubMed Central

Connon, Richard E.; Geist, Juergen; Werner, Inge

2012-01-01

Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water quality standards; currently applied effect-based tools are presented based on whether exposure occurs in a controlled laboratory environment or in the field. With increasing ecological relevance the reproducibility, specificity and thus suitability for standardisation of methods tends to diminish. We discuss the use of biomarkers in ecotoxicology including ecotoxicogenomics-based endpoints, which are becoming increasingly important for the detection of sublethal effects. Carefully selected sets of biomarkers allow an assessment of exposure to and effects of toxic chemicals, as well as the health status of organisms and, when combined with chemical analysis, identification of toxicant(s). The promising concept of “adverse outcome pathways (AOP)” links mechanistic responses on the cellular level with whole organism, population, community and potentially ecosystem effects and services. For most toxic mechanisms, however, practical application of AOPs will require more information and the identification of key links between responses, as well as key indicators, at different levels of biological organization, ecosystem functioning and ecosystem services. PMID:23112741

QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

PubMed

Gramatica, Paola; Papa, Ester; Sangion, Alessandro

2018-01-24

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

The U.S. Environmental Protection Agency strategic plan for evaluating the toxicity of chemicals.

PubMed

Firestone, Michael; Kavlock, Robert; Zenick, Hal; Kramer, Melissa

2010-02-01

In the 2007 report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences envisioned a major transition in toxicity testing from cumbersome, expensive, and lengthy in vivo testing with qualitative endpoints, to in vitro robotic high-throughput screening with mechanistic quantitative parameters. Recognizing the need for agencies to partner and collaborate to ensure global harmonization, standardization, quality control and information sharing, the U.S. Environmental Protection Agency is leading by example and has established an intra-agency Future of Toxicity Testing Workgroup (FTTW). This workgroup has produced an ambitious blueprint for incorporating this new scientific paradigm to change the way chemicals are screened and evaluated for toxicity. Four main components of this strategy are discussed, as follows: (1) the impact and benefits of various types of regulatory activities, (2) chemical screening and prioritization, (3) toxicity pathway-based risk assessment, and (4) institutional transition. The new paradigm is predicated on the discovery of molecular perturbation pathways at the in vitro level that predict adverse health effects from xenobiotics exposure, and then extrapolating those events to the tissue, organ, or whole organisms by computational models. Research on these pathways will be integrated and compiled using the latest technology with the cooperation of global agencies, industry, and other stakeholders. The net result will be that chemical toxicity screening will become more efficient and cost-effective, include real-world exposure assessments, and eliminate currently used uncertainty factors.

Disruptive environmental chemicals and cellular mechanisms that confer resistance to cell death.

PubMed

Narayanan, Kannan Badri; Ali, Manaf; Barclay, Barry J; Cheng, Qiang Shawn; D'Abronzo, Leandro; Dornetshuber-Fleiss, Rita; Ghosh, Paramita M; Gonzalez Guzman, Michael J; Lee, Tae-Jin; Leung, Po Sing; Li, Lin; Luanpitpong, Suidjit; Ratovitski, Edward; Rojanasakul, Yon; Romano, Maria Fiammetta; Romano, Simona; Sinha, Ranjeet K; Yedjou, Clement; Al-Mulla, Fahd; Al-Temaimi, Rabeah; Amedei, Amedeo; Brown, Dustin G; Ryan, Elizabeth P; Colacci, Annamaria; Hamid, Roslida A; Mondello, Chiara; Raju, Jayadev; Salem, Hosni K; Woodrick, Jordan; Scovassi, A Ivana; Singh, Neetu; Vaccari, Monica; Roy, Rabindra; Forte, Stefano; Memeo, Lorenzo; Kim, Seo Yun; Bisson, William H; Lowe, Leroy; Park, Hyun Ho

2015-06-01

Cell death is a process of dying within biological cells that are ceasing to function. This process is essential in regulating organism development, tissue homeostasis, and to eliminate cells in the body that are irreparably damaged. In general, dysfunction in normal cellular death is tightly linked to cancer progression. Specifically, the up-regulation of pro-survival factors, including oncogenic factors and antiapoptotic signaling pathways, and the down-regulation of pro-apoptotic factors, including tumor suppressive factors, confers resistance to cell death in tumor cells, which supports the emergence of a fully immortalized cellular phenotype. This review considers the potential relevance of ubiquitous environmental chemical exposures that have been shown to disrupt key pathways and mechanisms associated with this sort of dysfunction. Specifically, bisphenol A, chlorothalonil, dibutyl phthalate, dichlorvos, lindane, linuron, methoxychlor and oxyfluorfen are discussed as prototypical chemical disruptors; as their effects relate to resistance to cell death, as constituents within environmental mixtures and as potential contributors to environmental carcinogenesis. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Validation of minicams for measuring concentrations of chemical agent in environmental air

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menton, R.G.; Hayes, T.L.; Chou, Y.L.

1993-05-13

Environmental monitoring for chemical agents is necessary to ensure that notification and appropriate action will be taken in the, event that there is a release exceeding control limits of such agents into the workplace outside of engineering controls. Prior to implementing new analytical procedures for environmental monitoring, precision and accuracy (PA) tests are conducted to ensure that an agent monitoring system performs according to specified accuracy, precision, and sensitivity requirements. This testing not only establishes the accuracy and precision of the method, but also determines what factors can affect the method's performance. Performance measures that are particularly important in agentmore » monitoring include the Detection Limit (DL), Decision Limit (DC), Found Action Level (FAL), and the Target Action Level (TAL). PA experiments were performed at Battelle's Medical Research and Evaluation Facility (MREF) to validate the use of the miniature chemical agent monitoring system (MINICAMs) for measuring environmental air concentrations of sulfur mustard (HD). This presentation discusses the experimental and statistical approaches for characterizing the performance of MINICAMS for measuring HD in air.« less

Integrating Sustainable Development in Chemical Engineering Education: The Application of an Environmental Management System

ERIC Educational Resources Information Center

Montanes, M. T.; Palomares, A. E.; Sanchez-Tovar, R.

2012-01-01

The principles of sustainable development have been integrated in chemical engineering education by means of an environmental management system. These principles have been introduced in the teaching laboratories where students perform their practical classes. In this paper, the implementation of the environmental management system, the problems…

Predicting Toxic and Therapeutic Mechanisms of the ToxCast Chemical Library by Phenotypic Screening (SOT)

EPA Science Inventory

Addressing safety aspects of drugs and environmental chemicals relies extensively on animal testing. However the quantity of chemicals needing assessment and challenges of species extrapolation require development of alternative approaches. Using 8 primary human cell systems (Bio...

Predicting chemical bioavailability using microarray gene expression data and regression modeling: A tale of three explosive compounds.

PubMed

Gong, Ping; Nan, Xiaofei; Barker, Natalie D; Boyd, Robert E; Chen, Yixin; Wilkins, Dawn E; Johnson, David R; Suedel, Burton C; Perkins, Edward J

2016-03-08

Chemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation. This refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R(2) = 0.71-0.82 was achievable at a relatively low model complexity with as few as 3-10 predictor genes per model. These results are much more encouraging than our previous ones. This study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings.

PREDICTION METRICS FOR CHEMICAL DETECTION IN LONG-WAVE INFRARED HYPERSPECTRAL IMAGERY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chilton, M.; Walsh, S.J.; Daly, D.S.

2009-01-01

Natural and man-made chemical processes generate gaseous plumes that may be detected by hyperspectral imaging, which produces a matrix of spectra affected by the chemical constituents of the plume, the atmosphere, the bounding background surface and instrument noise. A physics-based model of observed radiance shows that high chemical absorbance and low background emissivity result in a larger chemical signature. Using simulated hyperspectral imagery, this study investigated two metrics which exploited this relationship. The objective was to explore how well the chosen metrics predicted when a chemical would be more easily detected when comparing one background type to another. The twomore » predictor metrics correctly rank ordered the backgrounds for about 94% of the chemicals tested as compared to the background rank orders from Whitened Matched Filtering (a detection algorithm) of the simulated spectra. These results suggest that the metrics provide a reasonable summary of how the background emissivity and chemical absorbance interact to produce the at-sensor chemical signal. This study suggests that similarly effective predictors that account for more general physical conditions may be derived.« less

Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

ERIC Educational Resources Information Center

Arnocky, Steven; Stroink, Mirella L.

2011-01-01

In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…

Integrating mechanistic and polymorphism data to characterize human genetic susceptibility for environmental chemical risk assessment in the 21st century

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mortensen, Holly M., E-mail: mortensen.holly@epa.gov; Euling, Susan Y.

Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization ofmore » drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment.« less

Integrating environmental and genetic effects to predict responses of tree populations to climate.

PubMed

Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

2010-01-01

Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

Predicting the sensitivity of populations from individual exposure to chemicals: the role of ecological interactions.

PubMed

Gabsi, Faten; Schäffer, Andreas; Preuss, Thomas G

2014-07-01

Population responses to chemical stress exposure are influenced by nonchemical, environmental processes such as species interactions. A realistic quantification of chemical toxicity to populations calls for the use of methodologies that integrate these multiple stress effects. The authors used an individual-based model for Daphnia magna as a virtual laboratory to determine the influence of ecological interactions on population sensitivity to chemicals with different modes of action on individuals. In the model, hypothetical chemical toxicity targeted different vital individual-level processes: reproduction, survival, feeding rate, or somatic growth rate. As for species interactions, predatory and competition effects on daphnid populations were implemented following a worst-case approach. The population abundance was simulated at different food levels and exposure scenarios, assuming exposure to chemical stress solely or in combination with either competition or predation. The chemical always targeted one vital endpoint. Equal toxicity-inhibition levels differently affected the population abundance with and without species interactions. In addition, population responses to chemicals were highly sensitive to the environmental stressor (predator or competitor) and to the food level. Results show that population resilience cannot be attributed to chemical stress only. Accounting for the relevant ecological interactions would reduce uncertainties when extrapolating effects of chemicals from individuals to the population level. Validated population models should be used for a more realistic risk assessment of chemicals. © 2014 SETAC.

Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.

PubMed

Liu, Zhongyang; Guo, Feifei; Gu, Jiangyong; Wang, Yong; Li, Yang; Wang, Dan; Lu, Liang; Li, Dong; He, Fuchu

2015-06-01

Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE ( S: imilarity-based P: redictor of A: TC C: od E: ), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. The web service SPACE is available at http://www.bprc.ac.cn/space. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals with Application to Risk-Based Prioritization

EPA Science Inventory

Toxicokinetic (TK) models can help bridge the gap between chemical exposure and measured toxicity endpoints, thereby addressing an important component of chemical risk assessments. The fraction of a chemical unbound by plasma proteins (Fub) and metabolic clearance rate (CLint) ar...

Environmental chemicals mediated the effect of old housing on adult health problems: US NHANES, 2009-2010.

PubMed

Shiue, Ivy; Bramley, Glen

2015-01-01

Housing conditions affect occupants continuously, and health interventions have shown a positive association between housing investment or improvement and occupant's health. However, the sources of the housing problems were less understood. Since it was observed that lead dust and chloroanisoles released from housing (materials) as indoor pollutants affected child's health, we now aimed to examine the relationships among built year, environmental chemicals and individual health in adults in a national and population-based setting. Data were retrieved from the US National Health and Nutrition Examination Survey, 2009-2010, including demographics, housing characteristics, self-reported health status, biomarkers and blood and urinary chemical concentrations. Adults aged 20 and above were included for statistical analysis (n = 5,793). Analysis involved chi-square test, t test, and survey-weighted general linear regression and logistic regression modelling. People who resided in older housing built before 1990 tended to report chronic bronchitis, liver problems, stroke, heart failure, diabetes, asthma and emphysema. Higher values in HDL cholesterol, blood lead and blood cadmium and having positive responses of hepatitis A, B, C and E antibodies among occupants were also observed. Furthermore, higher environmental chemical concentrations related to old housing including urinary cadmium, cobalt, platinum, mercury, 2,5-dichlorophenol and 2,4-dichlorophenol concentrations and mono-cyclohexyl phthalate and mono-isobutyl phthalate metabolites were shown in occupants as well. Older housing (≥30 years) seemed to contribute to the amount of environmental chemicals that affected human health. Regular monitoring, upgrading and renovation of housing to remove environmental chemicals and policy to support people in deprived situations against environmental injustice would be needed.

Simulating Microdosimetry of Environmental Chemicals for EPA’s Virtual Liver

EPA Science Inventory

US EPA Virtual Liver (v-Liver) is a cellular systems model of hepatic tissues aimed at predicting chemical-induced adverse effects through agent-based modeling. A primary objective of the project is to extrapolate in vitro data to in vivo outcomes. Agent-based approaches to tissu...

Chemical mixtures and environmental effects: a pilot study to assess ecological exposure and effects in streams

USGS Publications Warehouse

Buxton, Herbert T.; Reilly, Timothy J.; Kuivila, Kathryn; Kolpin, Dana W.; Bradley, Paul M.; Villeneuve, Daniel L.; Mills, Marc A.

2015-01-01

Assessment and management of the risks of exposure to complex chemical mixtures in streams are priorities for human and environmental health organizations around the world. The current lack of information on the composition and variability of environmental mixtures and a limited understanding of their combined effects are fundamental obstacles to timely identification and prevention of adverse human and ecological effects of exposure. This report describes the design of a field-based study of the composition and biological activity of chemical mixtures in U.S. stream waters affected by a wide range of human activities and contaminant sources. The study is a collaborative effort by the U.S. Geological Survey and the U.S. Environmental Protection Agency. Scientists sampled 38 streams spanning 24 States and Puerto Rico. Thirty-four of the sites were located in watersheds impacted by multiple contaminant sources, including industrial and municipal wastewater discharges, crop and animal agricultural runoff, urban runoff, and other point and nonpoint contaminant sources. The remaining four sites were minimally development reference watersheds. All samples underwent comprehensive chemical and biological characterization, including sensitive and specific direct analysis for over 700 dissolved organic and inorganic chemicals and field parameters, identification of unknown contaminants (environmental diagnostics), and a variety of bioassays to evaluate biological activity and toxicity.

Integrating Environmental Management in Chemical Engineering Education by Introducing an Environmental Management System in the Student's Laboratory

ERIC Educational Resources Information Center

Montanes, Maria T.; Palomares, Antonio E.

2008-01-01

In this work we show how specific challenges related to sustainable development can be integrated into chemical engineering education by introducing an environmental management system in the laboratory where the students perform their experimental lessons. It is shown how the system has been developed and implemented in the laboratory, what role…

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Efficient first-principles prediction of solid stability: Towards chemical accuracy

NASA Astrophysics Data System (ADS)

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; Chen, Tina; Dacek, Stephen T.; Sarmiento-Pérez, Rafael A.; Marques, Maguel A. L.; Peng, Haowei; Ceder, Gerbrand; Perdew, John P.; Sun, Jianwei

2018-03-01

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.

Efficient first-principles prediction of solid stability: Towards chemical accuracy

DOE PAGES

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; ...

2018-03-09

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less

Efficient first-principles prediction of solid stability: Towards chemical accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less

EPAs ToxCast Program for Predicting Toxcity and Prioritizing Chemicals for Further Screening and Testing

EPA Science Inventory

Testing of environmental and industrial chemicals for toxicity potential is a daunting task because of the wide range of possible toxicity mechanisms. Although animal testing is one means of achieving broad toxicity coverage, evaluation of large numbers of chemicals is challengin...

Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

NASA Astrophysics Data System (ADS)

Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

2017-04-01

Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

Chemical activity-based environmental risk analysis of the plasticizer di-ethylhexyl phthalate and its main metabolite mono-ethylhexyl phthalate.

PubMed

Gobas, Frank A P C; Otton, S Victoria; Tupper-Ring, Laura F; Crawford, Meara A; Clark, Kathryn E; Ikonomou, Michael G

2017-06-01

The present study applies a chemical activity-based approach to: 1) evaluate environmental concentrations of di-ethylhexyl phthalate (DEHP; n = 23 651) and its metabolite mono-ethylhexyl phthalate (MEHP; n = 1232) in 16 environmental media from 1174 studies in the United States, Canada, Europe, and Asia, and in vivo toxicity data from 934 studies in 20 species, as well as in vitro biological activity data from the US Environmental Protection Agency's Toxicity Forecaster and other sources; and 2) conduct a comprehensive environmental risk analysis. The results show that the mean chemical activities of DEHP and MEHP in abiotic environmental samples from locations around the globe are 0.001 and 10 -8 , respectively. This indicates that DEHP has reached on average 0.1% of saturation in the abiotic environment. The mean chemical activity of DEHP in biological samples is on average 100-fold lower than that in abiotic samples, likely because of biotransformation of DEHP in biota. Biological responses in both in vivo and in vitro tests occur at chemical activities between 0.01 to 1 for DEHP and between approximately 10 -6 and 10 -2 for MEHP, suggesting a greater potency of MEHP compared with DEHP. Chemical activities of both DEHP and MEHP in biota samples were less than those causing biological responses in the in vitro bioassays, without exception. A small fraction of chemical activities of DEHP in abiotic environmental samples (i.e., 4-8%) and none (0%) for MEHP were within the range of chemical activities associated with observed toxicological responses in the in vivo tests. The present study illustrates the chemical activity approach for conducting risk analyses. Environ Toxicol Chem 2017;36:1483-1492. © 2016 SETAC. © 2016 SETAC.

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals.

PubMed

Forbes, Valery E; Salice, Chris J; Birnir, Bjorn; Bruins, Randy J F; Calow, Peter; Ducrot, Virginie; Galic, Nika; Garber, Kristina; Harvey, Bret C; Jager, Henriette; Kanarek, Andrew; Pastorok, Robert; Railsback, Steve F; Rebarber, Richard; Thorbek, Pernille

2017-04-01

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. Environ Toxicol Chem 2017;36:845-859. © 2017 SETAC. © 2017 SETAC.

Inter-Individual Variability in High-Throughput Risk Prioritization of Environmental Chemicals (Sot)

EPA Science Inventory

We incorporate realistic human variability into an open-source high-throughput (HT) toxicokinetics (TK) modeling framework for use in a next-generation risk prioritization approach. Risk prioritization involves rapid triage of thousands of environmental chemicals, most which have...

Inter-individual variability in high-throughput risk prioritization of environmental chemicals (IVIVE)

EPA Science Inventory

We incorporate inter-individual variability into an open-source high-throughput (HT) toxicokinetics (TK) modeling framework for use in a next-generation risk prioritization approach. Risk prioritization involves rapid triage of thousands of environmental chemicals, most which hav...

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Local environmental quality positively predicts breastfeeding in the UK’s Millennium Cohort Study

PubMed Central

Sear, Rebecca

2017-01-01

ABSTRACT Background and Objectives: Breastfeeding is an important form of parental investment with clear health benefits. Despite this, rates remain low in the UK; understanding variation can therefore help improve interventions. Life history theory suggests that environmental quality may pattern maternal investment, including breastfeeding. We analyse a nationally representative dataset to test two predictions: (i) higher local environmental quality predicts higher likelihood of breastfeeding initiation and longer duration; (ii) higher socioeconomic status (SES) provides a buffer against the adverse influences of low local environmental quality. Methodology: We ran factor analysis on a wide range of local-level environmental variables. Two summary measures of local environmental quality were generated by this analysis—one ‘objective’ (based on an independent assessor’s neighbourhood scores) and one ‘subjective’ (based on respondent’s scores). We used mixed-effects regression techniques to test our hypotheses. Results: Higher objective, but not subjective, local environmental quality predicts higher likelihood of starting and maintaining breastfeeding over and above individual SES and area-level measures of environmental quality. Higher individual SES is protective, with women from high-income households having relatively high breastfeeding initiation rates and those with high status jobs being more likely to maintain breastfeeding, even in poor environmental conditions. Conclusions and Implications: Environmental quality is often vaguely measured; here we present a thorough investigation of environmental quality at the local level, controlling for individual- and area-level measures. Our findings support a shift in focus away from individual factors and towards altering the landscape of women’s decision making contexts when considering behaviours relevant to public health. PMID:29354262

Joint actions of environmental nonionizing electromagnetic fields and chemical pollution in cancer promotion.

PubMed Central

Adey, W R

1990-01-01

Studies of environmental electromagnetic (EM) field interactions in tissues have contributed to a new understanding of both normal growth and the biology of cancer in cell growth. From cancer research comes a floodtide of new knowledge about the disruption of communication by cancer-promoting chemicals with an onset of unregulated growth. Bioelectromagnetic research reveals clear evidence of joint actions at cell membranes of chemical cancer promoters and environmental electromagnetic fields. The union of these two disciplines has resulted in the first major new approach to tumor formation in 75 years, directing attention to dysfunctions in inward and outward streams of signals at cell membranes, rather than to damage DNA in cell nuclei, and to synergic actions of chemical pollutants and environmental electromagnetic fields. We are witnesses and, in great measure, participants in one of the great revolutions in the history of biology. In little more than a century, we have moved from organs, to tissues, to cells, and finally to the molecules that are the elegant fabric of living tissues. Today, we stand at a new frontier. It may be more difficult to comprehend, but it is far more significant; for it is at the atomic level, rather than the molecular, that physical, rather than chemical, processes appear to shape the flow of signals that are at the essence of living matter. To pursue these problems in the environment and in the laboratory, our needs for further research with appropriate budgets are great.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:2205491

NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

PubMed

Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

2013-01-01

Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

Physico-chemical measurements of CL-20 for environmental applications. Comparison with RDX and HMX.

PubMed

Monteil-Rivera, Fanny; Paquet, Louise; Deschamps, Stéphane; Balakrishnan, Vimal K; Beaulieu, Chantale; Hawari, Jalal

2004-01-30

CL-20 is a polycyclic energetic nitramine, which may soon replace the monocyclic nitramines RDX and HMX, because of its superior explosive performance. Therefore, to predict its environmental fate, analytical and physico-chemical data must be made available. An HPLC technique was thus developed to measure CL-20 in soil samples based on the US Environmental Protection Agency method 8330. We found that the soil water content and aging (21 days) had no effect on the recoveries (>92%) of CL-20, provided that the extracts were kept acidic (pH 3). The aqueous solubility of CL-20 was poor (3.6 mg l(-1) at 25 degrees C) and increased with temperature to reach 18.5 mg l(-1) at 60 degrees C. The octanol-water partition coefficient of CL-20 (log KOW = 1.92) was higher than that of RDX (log KOW = 0.90) and HMX (log KOW = 0.16), indicating its higher affinity to organic matter. Finally, CL-20 was found to decompose in non-acidified water upon contact with glass containers to give NO2- (2 equiv.), N2O (2 equiv.), and HCOO- (2 equiv.). The experimental findings suggest that CL-20 should be less persistent in the environment than RDX and HMX.

Environmental health attitudes and behaviors: findings from a large pregnancy cohort study.

PubMed

Barrett, Emily S; Sathyanarayana, Sheela; Janssen, Sarah; Redmon, J Bruce; Nguyen, Ruby H N; Kobrosly, Roni; Swan, Shanna H

2014-05-01

Environmental chemicals are widely found in food and personal care products and may have adverse effects on fetal development. Our aim was to examine women's attitudes about these chemicals and ask whether they try to limit their exposure during pregnancy. A multi-center cohort of women in the first trimester of pregnancy completed questionnaires including items on attitudes and behaviors related to environmental chemicals. Multivariable logistic regression models were used to examine: (1) whether sociodemographic variables predict environmental health attitudes and behaviors; and (2) whether women's attitudes about environmental chemicals affect their lifestyle behaviors, particularly diet and personal care product use. Of the 894 subjects, approximately 60% strongly agreed that environmental chemicals are dangerous and 25% strongly felt they were impossible to avoid. Adjusting for covariates, educated women were more likely to believe that environmental chemicals are dangerous (OR 1.74, 95% CI 1.13, 2.66), and that belief, in turn, was associated with a number of healthy behaviors including choosing organic foods, foods in safe plastics, and chemical-free personal care products, and limiting fast food intake. Younger women were more likely to believe that environmental chemicals are impossible to avoid (OR 1.04, 95% CI 1.00, 1.08). Women's attitudes about environmental chemicals may impact their choices during pregnancy. Overcoming a lack of concern about environmental chemicals, particularly among certain sociodemographic groups, is important for the success of clinical or public health prevention measures. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Environmental health attitudes and behaviors: findings from a large pregnancy cohort study

PubMed Central

Barrett, Emily S.; Sathyanarayana, Sheela; Janssen, Sarah; Redmon, J. Bruce; Nguyen, Ruby H.N.; Kobrosly, Roni; Swan, Shanna H.

2014-01-01

Objective Environmental chemicals are widely found in food and personal care products and may have adverse effects on fetal development. Our aim was to examine women’s attitudes about these chemicals and ask whether they try to limit their exposure during pregnancy. Study design A multi-center cohort of women in the first trimester of pregnancy completed questionnaires including items on attitudes and behaviors related to environmental chemicals. Multivariable logistic regression models were used to examine: (1) whether sociodemographic variables predict environmental health attitudes and behaviors; and (2) whether women’s attitudes about environmental chemicals affect their lifestyle behaviors, particularly diet and personal care product use. Results Of the 894 subjects, approximately 60% strongly agreed that environmental chemicals are dangerous and 25% strongly felt they were impossible to avoid. Adjusting for covariates, educated women were more likely to believe that environmental chemicals are dangerous (OR 1.74, 95% CI 1.13, 2.66), and that belief, in turn, was associated with a number of healthy behaviors including choosing organic foods, foods in safe plastics, and chemical-free personal care products, and limiting fast food intake. Younger women were more likely to believe environmental chemicals are impossible to avoid (OR 1.04, 95% CI 1.00, 1.08). Conclusions Women’s attitudes about environmental chemicals may impact their choices during pregnancy. Overcoming a lack of concern about environmental chemicals, particularly among certain sociodemographic groups, is important for the success of clinical or public health prevention measures. PMID:24647207

Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity -NLTO Poster

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

Challenges to studying the health effects of early life environmental chemical exposures on children's health.

PubMed

Braun, Joseph M; Gray, Kimberly

2017-12-01

Epidemiological studies play an important role in quantifying how early life environmental chemical exposures influence the risk of childhood diseases. These studies face at least four major challenges that can produce noise when trying to identify signals of associations between chemical exposure and childhood health. Challenges include accurately estimating chemical exposure, confounding from causes of both exposure and disease, identifying periods of heightened vulnerability to chemical exposures, and determining the effects of chemical mixtures. We provide recommendations that will aid in identifying these signals with more precision.

A General, Synthetic Model for Predicting Biodiversity Gradients from Environmental Geometry.

PubMed

Gross, Kevin; Snyder-Beattie, Andrew

2016-10-01

Latitudinal and elevational biodiversity gradients fascinate ecologists, and have inspired dozens of explanations. The geometry of the abiotic environment is sometimes thought to contribute to these gradients, yet evaluations of geometric explanations are limited by a fragmented understanding of the diversity patterns they predict. This article presents a mathematical model that synthesizes multiple pathways by which environmental geometry can drive diversity gradients. The model characterizes species ranges by their environmental niches and limits on range sizes and places those ranges onto the simplified geometries of a sphere or cone. The model predicts nuanced and realistic species-richness gradients, including latitudinal diversity gradients with tropical plateaus and mid-latitude inflection points and elevational diversity gradients with low-elevation diversity maxima. The model also illustrates the importance of a mid-environment effect that augments species richness at locations with intermediate environments. Model predictions match multiple empirical biodiversity gradients, depend on ecological traits in a testable fashion, and formally synthesize elements of several geometric models. Together, these results suggest that previous assessments of geometric hypotheses should be reconsidered and that environmental geometry may play a deeper role in driving biodiversity gradients than is currently appreciated.

Chemical Transformation System: Cloud Based Cheminformatic Services to Support Integrated Environmental Modeling

EPA Science Inventory

Integrated Environmental Modeling (IEM) systems that account for the fate/transport of organics frequently require physicochemical properties as well as transformation products. A myriad of chemical property databases exist but these can be difficult to access and often do not co...

High-Throughput Dietary Exposure Predictions for Chemical Migrants from Food Packaging Materials

EPA Science Inventory

United States Environmental Protection Agency researchers have developed a Stochastic Human Exposure and Dose Simulation High -Throughput (SHEDS-HT) model for use in prioritization of chemicals under the ExpoCast program. In this research, new methods were implemented in SHEDS-HT...

Students' Predictions about the Sensory Properties of Chemical Compounds: Additive versus Emergent Frameworks

ERIC Educational Resources Information Center

Talanquer, Vicente

2008-01-01

We investigated general chemistry students' intuitive ideas about the expected properties of the products of a chemical reaction. In particular, we analyzed college chemistry students' predictions about the color, smell, and taste of the products of chemical reactions represented at the molecular level. The study was designed to explore the extent…

In vivo ultrasound and biometric measurements predict the empty body chemical composition in Nellore cattle.

PubMed

Castilhos, A M; Francisco, C L; Branco, R H; Bonilha, S F M; Mercadante, M E Z; Meirelles, P R L; Pariz, C M; Jorge, A M

2018-05-04

Evaluation of the body chemical composition of beef cattle can only be measured postmortem and those data cannot be used in real production scenarios to adjust nutritional plans. The objective of this study was to develop multiple linear regression equations from in vivo measurements, such as ultrasound parameters [backfat thickness (uBFT, mm), rump fat thickness (uRF, mm), and ribeye area (uLMA, cm2)], shrunk body weight (SBW, kg), age (AG, d), hip height (HH, m), as well as from postmortem measurements (composition of the 9th to 11th rib section) to predict the empty body and carcass chemical composition for Nellore cattle. Thirty-three young bulls were used (339 ± 36.15 kg and 448 ± 17.78 d for initial weight and age, respectively). Empty body chemical composition (protein, fat, water, and ash in kg) was obtained by combining noncarcass and carcass components. Data were analyzed using the PROC REG procedure of SAS software. Mallows' Cp values were close to the ideal value of number of independent variables in the prediction equations plus one. Equations to predict chemical components of both empty body and carcass using in vivo measurements presented higher R2 values than those determined by postmortem measurements. Chemical composition of the empty body using in vivo measurements was predicted with R2 > 0.73. Equations to predict chemical composition of the carcass from in vivo measurements showed R2 lower (R2< 0.68) than observed for empty body, except for the water (R2 = 0.84). The independent variables SBW, uRF, and AG were sufficient to predict the fat, water, energy components of the empty body, whereas for estimation of protein content the uRF, HH, and SBW were satisfactory. For the calculation of the ash, the SBW variable in the equation was sufficient. Chemical compounds from components of the empty body of Nellore cattle can be calculated by the following equations: protein (kg) = 47.92 + 0.18 × SBW - 1.46 × uRF - 30.72 × HH (R2 = 0.94, RMSPE = 1

Multiple Classes of Environmental Chemicals are Associated with Liver Disease: NHANES 2003-2006 [Journal Article

EPA Science Inventory

Biomonitoring of human tissues and fluids has shown that virtually all individuals, worldwide, carry a “body burden” of synthetic chemicals (Thornton et al. 2002; CDC 2009). Although the measurement of an environmental chemical in a person’s tissues or fluids is an indication of...

Developing a predictive model for the chemical composition of soot nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Violi, Angela; Michelsen, Hope; Hansen, Nils

In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed amore » series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.« less

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

PubMed

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Laser applications to chemical, security, and environmental analysis: introduction to the feature issue.

PubMed

Seeger, Thomas; Dreier, Thomas; Chen, Weidong; Kearny, Sean; Kulatilaka, Waruna

2017-04-10

This Applied Optics feature issue on laser applications to chemical, security, and environmental analysis (LACSEA) highlights papers presented at the LACSEA 2016 Fifteenth Topical Meeting sponsored by the Optical Society of America.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

PubMed Central

Skinnider, Michael A.; Merwin, Nishanth J.; Johnston, Chad W.

2017-01-01

Abstract Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/. PMID:28460067

Disruption of the ‘disease triangle’ by chemical and physical environmental change

Treesearch

A. H. Chappelka; N. E. Grulke; L. De Kok

2015-01-01

The physical and chemical environment of the Earth has changed rapidly over the last 100 years and is predicted to continue to change into the foreseeable future. One of the main concerns with potential alterations in climate is the propensity for increases in the magnitude and frequency of extremes to occur. Even though precipitation is predicted to increase in some...

Bringing the Polluters Back In: Environmental Inequality and the Organization of Chemical Production

PubMed Central

Grant, Don; Trautner, Mary Nell; Downey, Liam; Thiebaud, Lisa

2011-01-01

Environmental justice scholars have suggested that because chemical plants and other hazardous facilities emit more pollutants where they face the least resistance, disadvantaged communities face a special health risk. In trying to determine whether race or income has the bigger impact on a neighborhood’s exposure to pollution, however, scholars tend to overlook the facilities themselves and the effect of their characteristics on emissions. In particular, how do the characteristics of facilities and their surrounding communities jointly shape pollution outcomes? We propose a new line of environmental justice research that focuses on facilities and how their features combine with communities’ features to create dangerous emissions. Using novel fuzzy-set analysis techniques and the EPA’s newly developed Risk-Screening Environmental Indicators, we test the influence of facility and community factors on chemical plants’ health-threatening emissions. Contrary to the idea that community characteristics have singular, linear effects, findings show that facility and community factors combine in a variety of ways to produce risky emissions. We speculate that as chemical firms experiment with different ways of producing goods and externalizing pollution costs, new “recipes of risk” are likely to emerge. The question, then, will no longer be whether race or income matters most, but in which of these recipes do they matter and how. PMID:21921966

USE OF A CONVECTION-DIFFUSION MODEL TO UNDERSTAND GASTROINTESTINAL ABSORPTION OF ENVIRONMENTALLY-RELEVANT CHEMICALS

EPA Science Inventory

Understanding the factors that affect the gastrointestinal absorption of chemicals is important to predicting the delivered systemic dose of chemicals following exposure in food, water, and other media. Two factors of particular interest are the effects of a matrix to which th...

Integrative Approaches for Predicting in vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-term Biological Assays

PubMed Central

Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander

2017-01-01

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064

Characterization and prediction of chemical functions and weight fractions in consumer products.

PubMed

Isaacs, Kristin K; Goldsmith, Michael-Rock; Egeghy, Peter; Phillips, Katherine; Brooks, Raina; Hong, Tao; Wambaugh, John F

2016-01-01

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization.

Predictive performance of the human Cell Line Activation Test (h-CLAT) for lipophilic chemicals with high octanol-water partition coefficients.

PubMed

Takenouchi, Osamu; Miyazawa, Masaaki; Saito, Kazutoshi; Ashikaga, Takao; Sakaguchi, Hitoshi

2013-01-01

To meet the urgent need for a reliable alternative test for predicting skin sensitizing potential of many chemicals, we have developed a cell-based in vitro test, human Cell Line Activation Test (h-CLAT). However, the predictive performance for lipophilic chemicals in the h-CLAT still remains relatively unknown. Moreover, it's suggested that low water solubility of chemicals might induce false negative outcomes. Thus, in this study, we tested relatively low water soluble 37 chemicals with log Kow values above and below 3.5 in the h-CLAT. The small-scale assessment resulted in nine false negative outcomes for chemicals with log Kow values greater than 3.5. We then created a dataset of 143 chemicals by combining the existing dataset of 106 chemicals and examined the predictive performance of the h-CLAT for chemicals with a log Kow of less than 3.5; a total of 112 chemicals from the 143 chemicals in the dataset. The sensitivity and overall accuracy for the 143 chemicals were 83% and 80%, respectively. In contrast, sensitivity and overall accuracy for the 112 chemicals with log Kow values below 3.5 improved to 94% and 88%, respectively. These data suggested that the h-CLAT could successfully detect sensitizers with log Kow values up to 3.5. When chemicals with log Kow values greater than 3.5 that were deemed positive by h-CLAT were included with the 112 chemicals, the sensitivity and accuracy in terms of the resulting applicable 128 chemicals out of the 143 chemicals became 95% and 88%, respectively. The use of log Kow values gave the h-CLAT a higher predictive performance. Our results demonstrated that the h-CLAT could predict sensitizing potential of various chemicals, which contain lipophilic chemicals using a large-scale chemical dataset.

A Proposal for Assessing Study Quality: Biomonitoring, Environmental Epidemiology, and Short-Lived Chemicals (BEES-C) Instrument

EPA Science Inventory

The quality of exposure assessment is a major determinant of the overall quality of any environmental epidemiology study. The use of biomonitoring as a tool for assessing exposure to ubiquitous chemicals with short physiologic half-lives began relatively recently. These chemicals...

Environmental chemicals and breast cancer: An updated review of epidemiological literature informed by biological mechanisms.

PubMed

Rodgers, Kathryn M; Udesky, Julia O; Rudel, Ruthann A; Brody, Julia Green

2018-01-01

Many common environmental chemicals are mammary gland carcinogens in animal studies, activate relevant hormonal pathways, or enhance mammary gland susceptibility to carcinogenesis. Breast cancer's long latency and multifactorial etiology make evaluation of these chemicals in humans challenging. For chemicals previously identified as mammary gland toxicants, we evaluated epidemiologic studies published since our 2007 review. We assessed whether study designs captured relevant exposures and disease features suggested by toxicological and biological evidence of genotoxicity, endocrine disruption, tumor promotion, or disruption of mammary gland development. We systematically searched the PubMed database for articles with breast cancer outcomes published in 2006-2016 using terms for 134 environmental chemicals, sources, or biomarkers of exposure. We critically reviewed the articles. We identified 158 articles. Consistent with experimental evidence, a few key studies suggested higher risk for exposures during breast development to dichlorodiphenyltrichloroethane (DDT), dioxins, perfluorooctane-sulfonamide (PFOSA), and air pollution (risk estimates ranged from 2.14 to 5.0), and for occupational exposure to solvents and other mammary carcinogens, such as gasoline components (risk estimates ranged from 1.42 to 3.31). Notably, one 50-year cohort study captured exposure to DDT during several critical windows for breast development (in utero, adolescence, pregnancy) and when this chemical was still in use. Most other studies did not assess exposure during a biologically relevant window or specify the timing of exposure. Few studies considered genetic variation, but the Long Island Breast Cancer Study Project reported higher breast cancer risk for polycyclic aromatic hydrocarbons (PAHs) in women with certain genetic variations, especially in DNA repair genes. New studies that targeted toxicologically relevant chemicals and captured biological hypotheses about genetic variants

EPA’s ToxCast Program for Predicting Toxicity and Prioritizing Chemicals for Further Screening and Testing

EPA Science Inventory

Testing of environmental and industrial chemicals for toxicity potential is a daunting task because of the wide range of possible toxicity mechanisms. Although animal testing is one means of achieving broad toxicity coverage, evaluation of large numbers of chemicals is challengin...

Engineering Education: Environmental and Chemical Engineering or Technology Curricula--A European Perspective

ERIC Educational Resources Information Center

Glavic, Peter; Lukman, Rebeka; Lozano, Rodrigo

2009-01-01

Over recent years, universities have been incorporating sustainable development (SD) into their systems, including their curricula. This article analyses the incorporation of SD into the curricula of chemical and environmental engineering or technology bachelor degrees at universities in the European Union (EU) and European Free Trade Association…

Three-dimensional prediction of soil physical, chemical, and hydrological properties in a forested catchment of the Santa Catalina CZO

NASA Astrophysics Data System (ADS)

Shepard, C.; Holleran, M.; Lybrand, R. A.; Rasmussen, C.

2014-12-01

Understanding critical zone evolution and function requires an accurate assessment of local soil properties. Two-dimensional (2D) digital soil mapping provides a general assessment of soil characteristics across a sampled landscape, but lacks the ability to predict soil properties with depth. The utilization of mass-preserving spline functions enable the extrapolation of soil properties with depth, extending predictive functions to three-dimensions (3D). The present study was completed in the Marshall Gulch (MG) catchment, located in the Santa Catalina Mountains, 30 km northwest of Tucson, Arizona, as part of the Santa Catalina-Jemez Mountains Critical Zone Observatory. Twenty-four soil pits were excavated and described following standard procedures. Mass-preserving splines were used to extrapolate mass carbon (kg C m-2); percent clay, silt, and sand (%); sodium mass flux (kg Na m-2); and pH for 24 sampled soil pits in 1-cm depth increments. Saturated volumetric water content (θs) and volumetric water content at 10 kPa (θ10) were predicted using ROSETTA and established empirical relationships. The described profiles were all sampled to differing depths; to compensate for the unevenness of the profile descriptions, the soil depths were standardized from 0.0 to 1.0 and then split into five equal standard depth sections. A logit-transformation was used to normalize the target variables. Step-wise regressions were calculated using available environmental covariates to predict the properties of each variable across the catchment in each depth section, and interpolated model residuals added back to the predicted layers to generate the final soil maps. Logit-transformed R2 for the predictive functions varied widely, ranging from 0.20 to 0.79, with logit-transformed RMSE ranging from 0.15 to 2.77. The MG catchment was further classified into clusters with similar properties based on the environmental covariates, and representative depth functions for each target variable

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver