High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Adverse effects in traditional and alternative toxicity tests.

Chemical safety assessment begins with defining the lowest level of chemical that alters one or more measured endpoints. This critical effect level, along with factors to account for uncertainty, is used to derive limits for human exposure. In the absence of data regarding the specific mechanisms or biological pathways affected, non-specific endpoints such as body weight and non-target organ weight changes are used to set critical effect levels. Specific apical endpoints such as impaired reproductive function or altered neurodevelopment have also been used to set chemical safety limits; however, in test guidelines designed for specific apical effect(s), concurrently measured non-specific endpoints may be equally or more sensitive than specific endpoints. This means that rather than predicting a specific toxicological response, animal data are often used to develop protective critical effect levels, without assuming the same change would be observed in humans. This manuscript is intended to encourage a rethinking of how adverse chemical effects are interpreted: non-specific endpoints from in vivo toxicological studies data are often used to derive points of departure for use with safety assessment factors to create recommended exposure levels that are broadly protective but not necessarily target-specific.

A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing.

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.

A Framework that Considers the Impacts of Time, Cost, and Uncertainty in the Determination of the Cost Effectiveness of Toxicity-Testing Methodologies.

Regulatory agencies are required to evaluate the impacts of thousands of chemicals. Toxicological tests currently used in such evaluations are time-consuming and resource intensive; however, advances in toxicology and related fields are providing new testing methodologies that reduce the cost and time required for testing. The selection of a preferred methodology is challenging because the new methodologies vary in duration and cost, and the data they generate vary in the level of uncertainty. This article presents a framework for performing cost-effectiveness analyses (CEAs) of toxicity tests that account for cost, duration, and uncertainty. This is achieved by using an output metric-the cost per correct regulatory decision-that reflects the three elements. The framework is demonstrated in two example CEAs, one for a simple decision of risk acceptability and a second, more complex decision, involving the selection of regulatory actions. Each example CEA evaluates five hypothetical toxicity-testing methodologies which differ with respect to cost, time, and uncertainty. The results of the examples indicate that either a fivefold reduction in cost or duration can be a larger driver of the selection of an optimal toxicity-testing methodology than a fivefold reduction in uncertainty. Uncertainty becomes of similar importance to cost and duration when decisionmakers are required to make more complex decisions that require the determination of small differences in risk predictions. The framework presented in this article may provide a useful basis for the identification of cost-effective methods for toxicity testing of large numbers of chemicals.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accelerating the Pace of Chemical Risk Assessment.

Changes in chemical regulations worldwide have increased the demand for new data on chemical safety. New approach methodologies (NAMs) are defined broadly here as including in silico approaches and in chemico and in vitro assays, as well as the inclusion of information from the exposure of chemicals in the context of hazard [European Chemicals Agency, " New Approach Methodologies in Regulatory Science ", 2016]. NAMs for toxicity testing, including alternatives to animal testing approaches, have shown promise to provide a large amount of data to fill information gaps in both hazard and exposure. In order to increase experience with the new data and to advance the applications of NAM data to evaluate the safety of data-poor chemicals, demonstration case studies have to be developed to build confidence in their usability. Case studies can be used to explore the domains of applicability of the NAM data and identify areas that would benefit from further research, development, and application. To ensure that this science evolves with direct input from and engagement by risk managers and regulatory decision makers, a workshop was convened among senior leaders from international regulatory agencies to identify common barriers for using NAMs and to propose next steps to address them. Central to the workshop were a series of collaborative case studies designed to explore areas where the benefits of NAM data could be demonstrated. These included use of in vitro bioassays data in combination with exposure estimates to derive a quantitative assessment of risk, use of NAMs for updating chemical categorizations, and use of NAMs to increase understanding of exposure and human health toxicity of various chemicals. The case study approach proved effective in building collaborations and engagement with regulatory decision makers and to promote the importance of data and knowledge sharing among international regulatory agencies. The case studies will be continued to explore new ways of describing hazard (i.e., pathway perturbations as a measure of adversity) and new ways of describing risk (i.e., using NAMs to identify protective levels without necessarily being predictive of a specific hazard). Importantly, the case studies also highlighted the need for increased training and communication across the various communities including the risk assessors, regulators, stakeholders (e.g., industry, non-governmental organizations), and the general public. The development and application of NAMs will play an increasing role in filling important data gaps on the safety of chemicals, but confidence in NAMs will only come with learning by doing and sharing in the experience.

Suspect Screening Analysis of Chemicals in Consumer Products.

A two-dimensional gas chromatography-time-of-flight/mass spectrometry (GC×GC-TOF/MS) suspect screening analysis method was used to rapidly characterize chemicals in 100 consumer products-which included formulations (e.g., shampoos, paints), articles (e.g., upholsteries, shower curtains), and foods (cereals)-and therefore supports broader efforts to prioritize chemicals based on potential human health risks. Analyses yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 tentatively identified chemicals, 1404 were not present in a public database of known consumer product chemicals. Reported data and model predictions of chemical functional use were applied to evaluate the tentative chemical identifications. Estimated chemical concentrations were compared to manufacturer-reported values and other measured data. Chemical presence and concentration data can now be used to improve estimates of chemical exposure, and refine estimates of risk posed to human health and the environment.

Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure.

Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performance was assessed based on F1 scores using 5-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%), and these gains were correlated (ρ = 0.92) with the number of chemicals. Overall, the results demonstrate that a combination of bioactivity and chemical descriptors can accurately predict a range of target organ toxicity outcomes in repeat-dose studies, but specific experimental and methodologic improvements may increase predictivity.

Development and Validation of a Computational Model for Androgen Receptor Activity.

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 µM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.

Chemical Risk Assessment: Traditional vs Public Health Perspectives.

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.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment.

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose-response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

On selecting a minimal set of in vitro assays to reliably determine estrogen agonist activity.

The US EPA is charged with screening chemicals for their ability to be endocrine disruptors through interaction with the estrogen, androgen and thyroid axes. The agency is exploring the use of high-throughput in vitro assays to use in the Endocrine Disruptor Screening Program (EDSP), potentially as replacements for lower-throughput in vitro and in vivo tests. The first replacement is an integrated computational and experimental model for estrogen receptor (ER) activity, to be used as an alternative to the EDSP Tier 1 in vitro ER binding and transactivation assays and the in vivo uterotrophic bioassay. The ER agonist model uses a set of 16 in vitro assays that incorporate multiple technologies and cell lines and probe multiple points in the ER pathway. Here, we demonstrate that subsets of assays with as few as 4 assays can predict the activity of all 1811 chemicals tested with accuracy equivalent to that of the full 16-assay model. The prediction accuracy against reference chemicals is higher than that of the full chemical set, partly because the larger set contains many chemicals that can cause a variety of types of assay interference There are multiple accurate assay subsets, allowing flexibility in the construction of a multiplexed assay battery. We also discuss the issue of challenging chemicals, i.e. those that can give false positive results in certain assays, and could hence be more problematic when only a few assays are used.

Lineage-dependent effects of aryl hydrocarbon receptor agonists contribute to liver tumorigenesis.

UNLABELLED: Rodent cancer bioassays indicate that the aryl hydrocarbon receptor (AHR) agonist, 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD), causes increases in both hepatocytic and cholangiocytic tumors. Effects of AHR activation have been evaluated on rodent hepatic stem cells (rHpSCs) versus their descendants, hepatoblasts (rHBs), two lineage stages of multipotent, hepatic precursors with overlapping but also distinct phenotypic traits. This was made possible by defining the first successful culture conditions for ex vivo maintenance of rHpScs consisting of a substratum of hyaluronans and Kubota's medium (KM), a serum-free medium designed for endodermal stem/progenitor cells. Supplementation of KM with leukemia inhibitory factor elicited lineage restriction to rHBs. Cultures were treated with various AHR agonists including TCDD, 6-formylindolo-[3,2-b]carbazole (FICZ), and 3-3'-diindolylmethane (DIM) and then analyzed with a combination of immunocytochemistry, gene expression, and high-content image analysis. The AHR agonists increased proliferation of rHpSCs at concentrations producing a persistent AHR activation as indicated by induction of Cyp1a1. By contrast, treatment with TCDD resulted in a rapid loss of viability of rHBs, even though the culture conditions, in the absence of the agonists, were permissive for survival and expansion of rHBs. The effects were not observed with FICZ and at lower concentrations of DIM. CONCLUSION: Our findings are consistent with a lineage-dependent mode of action for AHR agonists in rodent liver tumorigenesis through selective expansion of rHpSCs in combination with a toxicity-induced loss of viability of rHBs. These lineage-dependent effects correlate with increased frequency of liver tumors.

ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.

The U.S. Environmental Protection Agency's (EPA) ToxCast program is testing a large library of Agency-relevant chemicals using in vitro high-throughput screening (HTS) approaches to support the development of improved toxicity prediction models. Launched in 2007, Phase I of the program screened 310 chemicals, mostly pesticides, across hundreds of ToxCast assay end points. In Phase II, the ToxCast library was expanded to 1878 chemicals, culminating in the public release of screening data at the end of 2013. Subsequent expansion in Phase III has resulted in more than 3800 chemicals actively undergoing ToxCast screening, 96% of which are also being screened in the multi-Agency Tox21 project. The chemical library unpinning these efforts plays a central role in defining the scope and potential application of ToxCast HTS results. The history of the phased construction of EPA's ToxCast library is reviewed, followed by a survey of the library contents from several different vantage points. CAS Registry Numbers are used to assess ToxCast library coverage of important toxicity, regulatory, and exposure inventories. Structure-based representations of ToxCast chemicals are then used to compute physicochemical properties, substructural features, and structural alerts for toxicity and biotransformation. Cheminformatics approaches using these varied representations are applied to defining the boundaries of HTS testability, evaluating chemical diversity, and comparing the ToxCast library to potential target application inventories, such as used in EPA's Endocrine Disruption Screening Program (EDSP). Through several examples, the ToxCast chemical library is demonstrated to provide comprehensive coverage of the knowledge domains and target inventories of potential interest to EPA. Furthermore, the varied representations and approaches presented here define local chemistry domains potentially worthy of further investigation (e.g., not currently covered in the testing library or defined by toxicity "alerts") to strategically support data mining and predictive toxicology modeling moving forward.

Aryl hydrocarbon receptor knock-out exacerbates choroidal neovascularization via multiple pathogenic pathways.

The aryl hydrocarbon receptor (AhR) is a heterodimeric transcriptional regulator with pleiotropic functions in xenobiotic metabolism and detoxification, vascular development and cancer. Herein, we report a previously undescribed role for the AhR signalling pathway in the pathogenesis of the wet, neovascular subtype of age-related macular degeneration (AMD), the leading cause of vision loss in the elderly in the Western world. Comparative analysis of gene expression profiles of aged AhR(-/-) and wild-type (wt) mice, using high-throughput RNA sequencing, revealed differential modulation of genes belonging to several AMD-related pathogenic pathways, including inflammation, angiogenesis and extracellular matrix regulation. To investigate AhR regulation of these pathways in wet AMD, we experimentally induced choroidal neovascular lesions in AhR(-/-) mice and found that they measured significantly larger in area and volume compared to age-matched wt mice. Furthermore, these lesions displayed a higher number of ionized calcium-binding adaptor molecule 1-positive (Iba1(+) ) microglial cells and a greater amount of collagen type IV deposition, events also seen in human wet AMD pathology specimens. Consistent with our in vivo observations, AhR knock-down was sufficient to increase choroidal endothelial cell migration and tube formation in vitro. Moreover, AhR knock-down caused an increase in collagen type IV production and secretion in both retinal pigment epithelial (RPE) and choroidal endothelial cell cultures, increased expression of angiogenic and inflammatory molecules, including vascular endothelial growth factor A (VEGFA) and chemokine (C-C motif) ligand 2 (CCL2) in RPE cells, and increased expression of secreted phosphoprotein 1 (SPP1) and transforming growth factor-ß1 (TGFß1) in choroidal endothelial cells. Collectively, our findings identify AhR as a regulator of multiple pathogenic pathways in experimentally induced choroidal neovascularization, findings that are consistent with a possible role of AhR in wet AMD. The data discussed in this paper have been deposited in NCBI's Gene Expression Omnibus; GEO Submission No. GSE56983, NCBI Tracking System No. 17021116.

Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information.

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data ("bioactivity descriptors", from EPA's ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for up to ten in vivo repeated dose toxicity study types. Over 3239 different chemical structure descriptors were generated for a set of 1778 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction of toxicity for 600 chemicals with in vivo data was based on the similarity weighted endpoint outcomes of its nearest neighbors. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. This generalized read-across (GenRA) forms a first step in systemizing read-across predictions and serves as a useful component of a screening level hazard assessment for new untested chemicals.

BMDExpress Data Viewer - a visualization tool to analyze BMDExpress datasets.

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure for risk assessment. BMDExpress applies BMD modeling to transcriptomic datasets to identify transcriptional BMDs. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web-based application, BMDExpress Data Viewer (http://apps.sciome.com:8082/BMDX_Viewer/), for visualizing and graphing BMDExpress output files. The application consists of "Summary Visualization" and "Dataset Exploratory" tools. Through analysis of transcriptomic datasets of the toxicants furan and 4,4'-methylenebis(N,N-dimethyl)benzenamine, we demonstrate that the "Summary Visualization Tools" can be used to examine distributions of gene and pathway BMD values, and to derive a potential point of departure value based on summary statistics. By applying filters on enrichment P-values and minimum number of significant genes, the "Functional Enrichment Analysis" tool enables the user to select biological processes or pathways that are selectively perturbed by chemical exposure and identify the related BMD. The "Multiple Dataset Comparison" tool enables comparison of gene and pathway BMD values across multiple experiments (e.g., across timepoints or tissues). The "BMDL-BMD Range Plotter" tool facilitates the observation of BMD trends across biological processes or pathways. Through our case studies, we demonstrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files. Visualizing the data in this manner enables rapid assessment of data quality, model fit, doses of peak activity, most sensitive pathway perturbations and other metrics that will be useful in applying toxicogenomics in risk assessment. © 2015 Her Majesty the Queen in Right of Canada. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.

Aryl hydrocarbon receptor knockout rats are insensitive to the pathological effects of repeated oral exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin.

Sustained activation of the aryl hydrocarbon receptor (AHR) is believed to be the initial key event in AHR receptor-mediated tumorigenesis in the rat liver. The role of AHR in mediating pathological changes in the liver prior to tumor formation was investigated in a 4-week, repeated-dose study using adult female wild-type (WT) and AHR knockout (AHR-KO) rats treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Beginning at 8 weeks of age, AHR-KO and WT rats were dosed by oral gavage with varying concentrations of TCDD (0, 3, 22, 100, 300 and 1000 ng kg(-1) day(-1) ). Lung, liver and thymus histopathology, hematology, serum chemistry and the distribution of TCDD in liver and adipose tissue were examined. Treatment-related increases in the severity of liver and thymus pathology were observed in WT, but not AHR-KO rats. In the liver, these included hepatocellular hypertrophy, bile duct hyperplasia, multinucleated hepatocytes and inflammatory cell foci. A loss of cellularity in the thymic cortex and thymic atrophy was observed. Treatment-related changes in serum chemistry parameters were also observed in WT, but not AHR-KO rats. Finally, dose-dependent accumulation of TCDD was observed primarily in the liver of WT rats and primarily in the adipose tissue of AHR-KO rats. The results suggest that AHR activation is the initial key event underlying the progression of histological effects leading to liver tumorigenesis following TCDD treatment. Copyright © 2015 John Wiley & Sons, Ltd.