Background: Biological pathway-based chemical testing approaches are central to the National Research Council’s vision for 21st century toxicity testing. Approaches such as high-throughput in vitro screening offer the potential to evaluate thousands of chemicals faster and cheaper than ever before and to reduce testing on laboratory animals. Collaborative scientific engagement is important in addressing scientific issues arising in new federal chemical testing programs and for achieving stakeholder support of their use.
Objectives: We present two recommendations specifically focused on increasing scientific engagement in the U.S. Environmental Protection Agency (EPA) ToxCast™ initiative. Through these recommendations we seek to bolster the scientific foundation of federal chemical testing efforts such as ToxCast™ and the public health decisions that rely upon them.
Discussion: Environmental Defense Fund works across disciplines and with diverse groups to improve the science underlying environmental health decisions. We propose that the U.S. EPA can strengthen the scientific foundation of its new chemical testing efforts and increase support for them in the scientific research community by a) expanding and diversifying scientific input into the development and application of new chemical testing methods through collaborative workshops, and b) seeking out mutually beneficial research partnerships.
Conclusions: Our recommendations provide concrete actions for the U.S. EPA to increase and diversify engagement with the scientific research community in its ToxCast™ initiative. We believe that such engagement will help ensure that new chemical testing data are scientifically robust and that the U.S. EPA gains the support and acceptance needed to sustain new testing efforts to protect public health.
Citation: McPartland J, Dantzker HC, Portier CJ. 2015. Building a robust 21st century chemical testing program at the U.S. Environmental Protection Agency: recommendations for strengthening scientific engagement. Environ Health Perspect 123:1–5; http://dx.doi.org/10.1289/ehp.1408601
Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases.
To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions.
Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans.
Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions.
Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.
Background: Biomonitoring data reported in the National Report on Human Exposure to Environmental Chemicals [NER; Centers for Disease Control and Prevention (2012)] provide information on the presence and concentrations of > 400 chemicals in human blood and urine. Biomonitoring Equivalents (BEs) and other risk assessment–based values now allow interpretation of these biomonitoring data in a public health risk context.
Objectives: We compared the measured biomarker concentrations in the NER with BEs and similar risk assessment values to provide an across-chemical risk assessment perspective on the measured levels for approximately 130 analytes in the NER.
Methods: We identified available risk assessment–based biomarker screening values, including BEs and Human Biomonitoring-I (HBM-I) values from the German Human Biomonitoring Commission. Geometric mean and 95th percentile population biomarker concentrations from the NER were compared to the available screening values to generate chemical-specific hazard quotients (HQs) or cancer risk estimates.
Conclusions: Most analytes in the NER show HQ values of < 1; however, some (including acrylamide, dioxin-like chemicals, benzene, xylene, several metals, di-2(ethylhexyl)phthalate, and some legacy organochlorine pesticides) approach or exceed HQ values of 1 or cancer risks of > 1 × 10–4 at the geometric mean or 95th percentile, suggesting exposure levels may exceed published human health benchmarks. This analysis provides for the first time a means for examining population biomonitoring data for multiple environmental chemicals in the context of the risk assessments for those chemicals. The results of these comparisons can be used to focus more detailed chemical-specific examination of the data and inform priorities for chemical risk management and research.
biomonitoring; Biomonitoring Equivalents; blood; cancer risk; CDC National Exposure Report; hazard quotient; NHANES; risk assessment; urine
The National Toxicology Program is developing a high throughput screening (HTS) program to set testing priorities for compounds of interest, to identify mechanisms of action, and potentially to develop predictive models for human toxicity. This program will generate extensive data on the activity of large numbers of chemicals in a wide variety of biochemical-and cell-based assays. The first step in relating patterns of response among batteries of HTS assays to in vivo toxicity is to distinguish between positive and negative compounds in individual assays. Here, we report on a statistical approach developed to identify compounds positive or negative in a HTS cytotoxicity assay based on data collected from screening 1353 compounds for concentration-response effects in nine human and four rodent cell types. In this approach, we develop methods to normalize the data (removing bias due to the location of the compound on the 1536-well plates used in the assay) and to analyze for concentration-response relationships. Various statistical tests for identifying significant concentration-response relationships and for addressing reproducibility are developed and presented.
high-throughput screening; dose-response; statistical modeling; viability assay
Background: The U.S. National Toxicology Program (NTP) cancer bioassay database provides an opportunity to compare both existing and new approaches to determining points of departure (PoDs) for establishing reference doses (RfDs).
Objectives: The aims of this study were a) to investigate the risk associated with the traditional PoD used in human health risk assessment [the no observed adverse effect level (NOAEL)]; b) to present a new approach based on the signal-to-noise crossover dose (SNCD); and c) to compare the SNCD and SNCD-based RfD with PoDs and RfDs based on the NOAEL and benchmark dose (BMD) approaches.
Methods: The complete NTP database was used as the basis for these analyses, which were performed using the Hill model. We determined NOAELs and estimated corresponding extra risks. Lower 95% confidence bounds on the BMD (BMDLs) corresponding to extra risks of 1%, 5%, and 10% (BMDL01, BMDL05, and BMDL10, respectively) were also estimated. We introduce the SNCD as a new PoD, defined as the dose where the additional risk is equal to the “background noise” (the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute risk) or a specified fraction thereof.
Results: The median risk at the NOAEL was approximately 10%, and the default uncertainty factor (UF = 100) was considered most applicable to the BMDL10. Therefore, we chose a target risk of 1/1,000 (0.1/100) to derive an SNCD-based RfD by linear extrapolation. At the median, this approach provided the same RfD as the BMDL10 divided by the default UF.
Conclusions: Under a standard BMD approach, the BMDL10 is considered to be the most appropriate PoD. The SNCD approach, which is based on the lowest dose at which the signal can be reliably detected, warrants further development as a PoD for human health risk assessment.
benchmark dose; cancer bioassay; human exposure guideline; low-dose extrapolation; point of departure; reference dose; signal-to-noise crossover dose; uncertainty factor
Background: The growing health risks associated with greenhouse gas emissions
highlight the need for new energy policies that emphasize efficiency and
low-carbon energy intensity.
Objectives: We assessed the relationships among electricity use, coal
consumption, and health outcomes.
Methods: Using time-series data sets from 41 countries with varying development
trajectories between 1965 and 2005, we developed an autoregressive model of life
expectancy (LE) and infant mortality (IM) based on electricity consumption, coal
consumption, and previous year’s LE or IM. Prediction of health impacts
from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)
integrated air pollution emissions health impact model for coal-fired power
plants was compared with the time-series model results.
Results: The time-series model predicted that increased electricity consumption
was associated with reduced IM for countries that started with relatively high
IM (> 100/1,000 live births) and low LE (< 57 years) in 1965, whereas LE
was not significantly associated with electricity consumption regardless of IM
and LE in 1965. Increasing coal consumption was associated with increased IM and
reduced LE after accounting for electricity consumption. These results are
consistent with results based on the GAINS model and previously published
estimates of disease burdens attributable to energy-related environmental
factors, including indoor and outdoor air pollution and water and
Conclusions: Increased electricity consumption in countries with IM <
100/1,000 live births does not lead to greater health benefits, whereas coal
consumption has significant detrimental health impacts.
air pollution; climate change; coal; electricity; energy policy; global health; health impact modeling; infant mortality; life expectancy; time series
Benzene, an established cause of acute myeloid leukemia (AML), may also cause one or more lymphoid malignancies in humans. Previously, we identified genes and pathways associated with exposure to high (> 10 ppm) levels of benzene through transcriptomic analyses of blood cells from a small number of occupationally exposed workers.
The goals of this study were to identify potential biomarkers of benzene exposure and/or early effects and to elucidate mechanisms relevant to risk of hematotoxicity, leukemia, and lymphoid malignancy in occupationally exposed individuals, many of whom were exposed to benzene levels < 1 ppm, the current U.S. occupational standard.
We analyzed global gene expression in the peripheral blood mononuclear cells of 125 workers exposed to benzene levels ranging from < 1 ppm to > 10 ppm. Study design and analysis with a mixed-effects model minimized potential confounding and experimental variability.
We observed highly significant widespread perturbation of gene expression at all exposure levels. The AML pathway was among the pathways most significantly associated with benzene exposure. Immune response pathways were associated with most exposure levels, potentially providing biological plausibility for an association between lymphoma and benzene exposure. We identified a 16-gene expression signature associated with all levels of benzene exposure.
Our findings suggest that chronic benzene exposure, even at levels below the current U.S. occupational standard, perturbs many genes, biological processes, and pathways. These findings expand our understanding of the mechanisms by which benzene may induce hematotoxicity, leukemia, and lymphoma and reveal relevant potential biomarkers associated with a range of exposures.
benzene; biomarker; human; microarray; transcriptomics
Assessing the risk profiles of potentially sensitive populations requires a “tool chest” of methodological approaches to adequately characterize and evaluate these populations. At present, there is an extensive body of literature on methodologies that apply to the evaluation of the pediatric population. The Health and Environmental Sciences Institute Subcommittee on Risk Assessment of Sensitive Populations evaluated key references in the area of pediatric risk to identify a spectrum of methodological approaches. These approaches are considered in this article for their potential to be extrapolated for the identification and assessment of other sensitive populations. Recommendations as to future research needs and/or alternate methodological considerations are also made.
sensitive populations; pharmacokinetics; pharmacodynamics; genetic variability; pediatric population; risk assessment; exposure assessment
We hypothesize that TCDD induced developmental neurotoxicity is modulated through an AhR dependent interaction with key regulatory neuronal differentiation pathways during telencephalon development. To test this hypothesis we examined global gene expression in both dorsal and ventral telencephalon tissues in E13.5 AhR -/- and wildtype mice exposed to TCDD or vehicle. Consistent with previous biochemical, pathological and behavioral studies, our results suggest TCDD initiated changes in gene expression in the developing telencephalon are primarily AhR dependent, as no statistically significant gene expression changes are evident after TCDD exposure in AhR -/- mice. Based on a gene regulatory network for neuronal specification in the developing telencephalon, the present analysis suggests differentiation of GABAergic neurons in the ventral telencephalon is compromised in TCDD exposed and AhR-/- mice. In addition, our analysis suggests Sox11 may be directly regulated by AhR based on gene expression and comparative genomics analyses. In conclusion, this analysis supports the hypothesis that AhR has a specific role in the normal development of the telencephalon and provides a mechanistic framework for neurodevelopmental toxicity of chemicals that perturb AhR signaling.
neurodevelopment; gene expression; bioinformatics; TCDD; dioxin; neurotoxicity
Biologically based dose–response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect.
Our goal was to examine the utility of BBDR models in estimating low-dose risk.
We reviewed the utility of BBDR models in risk assessment.
BBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling.
The problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems.
biologically based dose response; dose-response model; low-dose risk; risk assessment; two-stage model
In horses, gastrointestinal (GI) disorders occur frequently and cause a considerable demand for efficient medication. 5-Hydroxytryptamine receptors (5-HT) have been reported to be involved in GI tract motility and thus, are potential targets for treating functional bowel disorders. Our studies extend current knowledge on the 5-HT7 receptor in equine duodenum, ileum and pelvic flexure by studying its expression throughout the intestine and its role in modulating contractility in vitro by immunofluorescence and organ bath experiments, respectively.
5-HT7 immunoreactivity was demonstrated in both smooth muscle layers, particularly in the circular one, and within the myenteric plexus. Interstitial cells of Cajal (ICC), identified by c-Kit labeling, show a staining pattern similar to that of 5-HT7 immunoreactivity.
The selective 5-HT7 receptor antagonist SB-269970 increased the amplitude of contractions in spontaneous contracting specimens of the ileum and in electrical field-stimulated specimens of the pelvic flexure concentration-dependently.
Our in vitro experiments suggest an involvement of the 5-HT7 receptor subtype in contractility of equine intestine. While the 5-HT7 receptor has been established to be constitutively active and inhibits smooth muscle contractility, our experiments demonstrate an increase in contractility by the 5-HT7 receptor ligand SB-269970, suggesting it exerting inverse agonist properties.
5-HT7 receptor; Interstitial cells of Cajal; c-Kit; Equine; Intestine; Motility
The mitogen activated protein kinase (MAPK) cascade is a three-tiered phosphorylation cascade that is ubiquitously expressed among eukaryotic cells. Its primary function is to propagate signals from cell surface receptors to various cytosolic and nuclear targets. Recent studies have demonstrated that the MAPK cascade exhibits an all-or-none response to graded stimuli. This study quantitatively investigates MAPK activation in Xenopus oocytes using both empirical and biologically-based mechanistic models. Empirical models can represent overall tissue MAPK activation in the oocytes. However, these models lack description of key biological processes and therefore give no insight into whether the cellular response occurs in a graded or all-or-none fashion. To examine the propagation of cellular MAPK all-or-none activation to overall tissue response, mechanistic models in conjunction with Monte Carlo simulations are employed. An adequate description of the dose response relationship of MAPK activation in Xenopus oocytes is achieved. Furthermore, application of these mechanistic models revealed that the initial receptor-ligand binding rate contributes to the cells’ ability to exhibit an all-or-none MAPK activation response, while downstream activation parameters contribute more to the magnitude of activation. These mechanistic models enable us to identify key biological events which quantitatively impact the shape of the dose response curve, especially at low environmentally relevant doses.
As part of a program to predict the toxicity of environmental agents on human health using alternative methods, several in vivo high- and medium-throughput assays are being developed that use C. elegans as a model organism. C. elegans-based toxicological assays utilize the COPAS Biosort flow sorting system that can rapidly measure size, extinction (EXT) and time-of-flight (TOF), of individual nematodes. The use of this technology requires the development of mathematical and statistical tools to properly analyze the large volumes of biological data.
Findings A Markov model was developed that predicts the growth of populations of C. elegans. The model was developed using observations from a 60 h growth study in which five cohorts of 300 nematodes each were aspirated and measured every 12 h. Frequency distributions of log(EXT) measurements that were made when loading C. elegans L1 larvae into 96 well plates (t = 0 h) were used by the model to predict the frequency distributions of the same set of nematodes when measured at 12 h intervals. The model prediction coincided well with the biological observations confirming the validity of the model. The model was also applied to log(TOF) measurements following an adaptation. The adaptation accounted for variability in TOF measurements associated with potential curling or shortening of the nematodes as they passed through the flow cell of the Biosort. By providing accurate estimates of frequencies of EXT or TOF measurements following varying growth periods, the model was able to estimate growth rates. Best model fits showed that C. elegans did not grow at a constant exponential rate. Growth was best described with three different rates. Microscopic observations indicated that the points where the growth rates changed corresponded to specific developmental events: the L1/L2 molt and the start of oogenesis in young adult C. elegans.
Quantitative analysis of COPAS Biosort measurements of C. elegans growth has been hampered by the lack of a mathematical model. In addition, extraneous matter and the inability to assign specific measurements to specific nematodes made it difficult to estimate growth rates. The present model addresses these problems through a population-based Markov model.
The nematode Caenorhabditis elegans is being assessed as an alternative model organism as part of an interagency effort to develop better means to test potentially toxic substances. As part of this effort, assays that use the COPAS Biosort flow sorting technology to record optical measurements (time of flight (TOF) and extinction (EXT)) of individual nematodes under various chemical exposure conditions are being developed. A mathematical model has been created that uses Biosort data to quantitatively and qualitatively describe C. elegans growth, and link changes in growth rates to biological events. Chlorpyrifos, an organophosphate pesticide known to cause developmental delays and malformations in mammals, was used as a model toxicant to test the applicability of the growth model for in vivo toxicological testing.
L1 larval nematodes were exposed to a range of sub-lethal chlorpyrifos concentrations (0–75 µM) and measured every 12 h. In the absence of toxicant, C. elegans matured from L1s to gravid adults by 60 h. A mathematical model was used to estimate nematode size distributions at various times. Mathematical modeling of the distributions allowed the number of measured nematodes and log(EXT) and log(TOF) growth rates to be estimated. The model revealed three distinct growth phases. The points at which estimated growth rates changed (change points) were constant across the ten chlorpyrifos concentrations. Concentration response curves with respect to several model-estimated quantities (numbers of measured nematodes, mean log(TOF) and log(EXT), growth rates, and time to reach change points) showed a significant decrease in C. elegans growth with increasing chlorpyrifos concentration.
Effects of chlorpyrifos on C. elegans growth and development were mathematically modeled. Statistical tests confirmed a significant concentration effect on several model endpoints. This confirmed that chlorpyrifos affects C. elegans development in a concentration dependent manner. The most noticeable effect on growth occurred during early larval stages: L2 and L3. This study supports the utility of the C. elegans growth assay and mathematical modeling in determining the effects of potentially toxic substances in an alternative model organism using high-throughput technologies.
To describe the in vitro effects of bethanechol on contractility of smooth muscle preparations from the small intestines of healthy cows and define the muscarinic receptor subtypes involved in mediating contraction.
Tissue samples from the duodenum and jejunum collected immediately after slaughter of 40 healthy cows.
Cumulative concentration-response curves were determined for the muscarinic receptor agonist bethanechol with or without prior incubation with subtype-specific receptor antagonists in an organ bath. Effects of bethanechol and antagonists and the influence of intestinal location on basal tone, maximal amplitude (Amax), and area under the curve (AUC) were evaluated.
Bethanechol induced a significant, concentration-dependent increase in all preparations and variables. The effect of bethanechol was more pronounced in jejunal than in duodenal samples and in circular than in longitudinal preparations. Significant inhibition of the effects of bethanechol was observed after prior incubation with muscarinic receptor subtype M3 antagonists (more commonly for basal tone than for Amax and AUC). The M2 receptor antagonists partly inhibited the response to bethanechol, especially for basal tone. The M3 receptor antagonists were generally more potent than the M2 receptor antagonists. In a protection experiment, an M3 receptor antagonist was less potent than when used in combination with an M2 receptor antagonist. Receptor antagonists for M1 and M4 did not affect contractility variables.
Conclusions and Clinical Relevance
Bethanechol acting on muscarinic receptor subtypes M2 and M3 may be of clinical use as a prokinetic drug for motility disorders of the duodenum and jejunum in dairy cows.
In a series of articles and a health-risk assessment report, scientists at the CIIT Hamner Institutes developed a model (CIIT model) for estimating respiratory cancer risk due to inhaled formaldehyde within a conceptual framework incorporating extensive mechanistic information and advanced computational methods at the toxicokinetic and toxicodynamic levels. Several regulatory bodies have utilized predictions from this model; on the other hand, upon detailed evaluation the California EPA has decided against doing so. In this article, we study the CIIT model to identify key biological and statistical uncertainties that need careful evaluation if such two-stage clonal expansion models are to be used for extrapolation of cancer risk from animal bioassays to human exposure. Broadly, these issues pertain to the use and interpretation of experimental labeling index and tumor data, the evaluation and biological interpretation of estimated parameters, and uncertainties in model specification, in particular that of initiated cells. We also identify key uncertainties in the scale-up of the CIIT model to humans, focusing on assumptions underlying model parameters for cell replication rates and formaldehyde-induced mutation. We discuss uncertainties in identifying parameter values in the model used to estimate and extrapolate DNA protein cross-link levels. The authors of the CIIT modeling endeavor characterized their human risk estimates as “conservative in the face of modeling uncertainties.” The uncertainties discussed in this article indicate that such a claim is premature.
Biologically-based dose response; formaldehyde; two-stage cancer model
Pathogenesis of complex diseases involves the integration of genetic and environmental factors over time, making it particularly difficult to tease apart relationships between phenotype, genotype, and environmental factors using traditional experimental approaches.
Using gene-centered databases, we have developed a network of complex diseases and environmental factors through the identification of key molecular pathways associated with both genetic and environmental contributions. Comparison with known chemical disease relationships and analysis of transcriptional regulation from gene expression datasets for several environmental factors and phenotypes clustered in a metabolic syndrome and neuropsychiatric subnetwork supports our network hypotheses. This analysis identifies natural and synthetic retinoids, antipsychotic medications, Omega 3 fatty acids, and pyrethroid pesticides as potential environmental modulators of metabolic syndrome phenotypes through PPAR and adipocytokine signaling and organophosphate pesticides as potential environmental modulators of neuropsychiatric phenotypes.
Identification of key regulatory pathways that integrate genetic and environmental modulators define disease associated targets that will allow for efficient screening of large numbers of environmental factors, screening that could set priorities for further research and guide public health decisions.
A method is proposed that finds enriched pathways relevant to a studied condition, using molecular and network data.
A method is proposed that finds enriched pathways relevant to a studied condition using the measured molecular data and also the structural information of the pathway viewed as a network of nodes and edges. Tests are performed using simulated data and genomic data sets and the method is compared to two existing approaches. The analysis provided demonstrates the method proposed is very competitive with the current approaches and also provides biologically relevant results.
The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors.
Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets.
Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation.