ACToR--Aggregated Computational Toxicology Resource

ACToR (Aggregated Computational Toxicology Resource) is a database and set of software applications that bring into one central location many types and sources of data on environmental chemicals. Currently, the ACToR chemical database contains information on chemical structure, in vitro bioassays and in vivo toxicology assays derived from more than 150 sources including the U.S. Environmental Protection Agency (EPA), Centers for Disease Control (CDC), U.S. Food and Drug Administration (FDA), National Institutes of Health (NIH), state agencies, corresponding government agencies in Canada, Europe and Japan, universities, the World Health Organization (WHO) and non-governmental organizations (NGOs). At the EPA National Center for Computational Toxicology, ACToR helps manage large data sets being used in a high-throughput environmental chemical screening and prioritization program called ToxCast^™.     

A cross-platform approach to characterize and screen potential neurovascular unit toxicants

Development of the neurovascular unit (NVU) is a complex, multistage process that requires orchestrated cell signaling mechanisms across several cell types and ultimately results in formation of the blood-brain barrier. Typical high-throughput screening (HTS) assays investigate single biochemical or single cell responses following chemical insult. As the NVU comprises multiple cell types interacting at various stages of development, a methodology combining high-throughput results across pertinent cell-based assays is needed to investigate potential chemical-induced disruption to the development of this complex cell system. To this end, we implemented a novel method for screening putative NVU disruptors across diverse assay platforms to predict chemical perturbation of the developing NVU. HTS assay results measuring chemical-induced perturbations to cellular key events across angiogenic and neurogenic outcomes in vitro were combined to create a cell-based prioritization of NVU hazard. Chemicals were grouped according to similar modes of action to train a logistic regression literature model on a training set of 38 chemicals. This model utilizes the chemical-specific pairwise mutual information score for PubMed MeSH annotations to represent a quantitative measure of previously published results. Taken together, this study presents a methodology to investigate NVU developmental hazard using cell-based HTS assays and literature evidence to prioritize screening of putative NVU disruptors towards a knowledge-driven characterization of neurovascular developmental toxicity. The results from these screening efforts demonstrate that chemicals representing a range of putative vascular disrupting compound (pVDC) scores can also produce effects on neurogenic outcomes and characterizes possible modes of action for disrupting the developing NVU.     

Alternative Toxicity Testing: Analyses on Skin Sensitization,ToxCast Phases I and II,and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways

This article studies alternative toxicological approaches, with new (skin sensitization, ToxCast) and previous (carcinogenicity) analyses. Quantitative modeling of rate-limiting steps in skin sensitization and carcinogenicity predicts the majority of toxicants. Similarly, successful (Quantitative) Structure-Activity Relationships models exploit the quantification of only one, or few rate-limiting steps. High-throughput assays within ToxCast point to promising associations with endocrine disruption, whereas markers for pathways intermediate events have limited correlation with most endpoints. Since the pathways may be very different (often not simple linear chains of events), quantitative analysis is necessary to identify the type of mechanism and build the appropriate model.     

Evaluation of high-throughput genotoxicity assays used in profiling the US EPA ToxCast™ chemicals

Three high-throughput screening (HTS) genotoxicity assays—GreenScreen HC GADD45a-GFP (Gentronix Ltd.), CellCiphr p53 (Cellumen Inc.) and CellSensor p53RE-bla (Invitrogen Corp.)—were used to analyze the collection of 320 predominantly pesticide active compounds being tested in Phase I of US. Environmental Protection Agency’s ToxCast™ research project. Between 9% and 12% of compounds were positive for genotoxicity in the assays. However, results of the varied tests only partially overlapped, suggesting a strategy of combining data from a battery of assays. The HTS results were compared to mutagenicity (Ames) and animal tumorigenicity data. Overall, the HTS assays demonstrated low sensitivity for rodent tumorigens, likely due to: screening at a low concentration, coverage of selected genotoxic mechanisms, lack of metabolic activation and difficulty detecting non-genotoxic carcinogens. Conversely, HTS results demonstrated high specificity, >88%. Overall concordance of the HTS assays with tumorigenicity data was low, around 50% for all tumorigens, but increased to 74–78% (vs. 60% for Ames) for those compounds producing tumors in rodents at multiple sites and, thus, more likely genotoxic carcinogens. The aim of the present study was to evaluate the utility of HTS assays to identify potential genotoxicity hazard in the larger context of the ToxCast project, to aid prioritization of environmentally relevant chemicals for further testing and assessment of carcinogenicity risk to humans.     

Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program

Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity.     

Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure

Robust computational approaches are needed to characterize systems-level responses to chemical perturbations in environmental and clinical toxicology applications. Appropriate characterization of response presents a methodological challenge when dealing with diverse phenotypic endpoints measured using in vivo systems. In this article, we propose an information-theoretic method named Aggregate Entropy (AggE) and apply it to scoring multiplexed, phenotypic endpoints measured in developing zebrafish (Danio rerio) across a broad concentration-response profile for a diverse set of 1060 chemicals. AggE accurately identified chemicals with significant morphological effects, including single-endpoint effects and multi-endpoint responses that would have been missed by univariate methods, while avoiding putative false-positives that confound traditional methods due to irregular correlation structure. By testing AggE in a variety of high-dimensional real and simulated datasets, we have characterized its performance and suggested implementation parameters that can guide its application across a wide range of experimental scenarios.     

Informing selection of nanomaterial concentrations for ToxCast in vitro testing based on occupational exposure potential

Background: Little justification is generally provided for selection of in vitro assay testing concentrations for engineered nanomaterials (ENMs). Selection of concentration levels for hazard evaluation based on real-world exposure scenarios is desirable.Objectives: Our goal was to use estimates of lung deposition after occupational exposure to nanomaterials to recommend in vitro testing concentrations for the U.S. Environmental Protection Agency’s ToxCast™ program. Here, we provide testing concentrations for carbon nanotubes (CNTs) and titanium dioxide (TiO₂) and silver (Ag) nanoparticles (NPs).Methods: We reviewed published ENM concentrations measured in air in manufacturing and R&D (research and development) laboratories to identify input levels for estimating ENM mass retained in the human lung using the multiple-path particle dosimetry (MPPD) model. Model input parameters were individually varied to estimate alveolar mass retained for different particle sizes (5–1,000 nm), aerosol concentrations (0.1 and 1 mg/m³), aspect ratios (2, 4, 10, and 167), and exposure durations (24 hr and a working lifetime). The calculated lung surface concentrations were then converted to in vitro solution concentrations.Results: Modeled alveolar mass retained after 24 hr is most affected by activity level and aerosol concentration. Alveolar retention for Ag and TiO₂ NPs and CNTs for a working-lifetime (45 years) exposure duration is similar to high-end concentrations (~ 30–400 μg/mL) typical of in vitro testing reported in the literature.Conclusions: Analyses performed are generally applicable for providing ENM testing concentrations for in vitro hazard screening studies, although further research is needed to improve the approach. Understanding the relationship between potential real-world exposures and in vitro test concentrations will facilitate interpretation of toxicological results.     

Computational analysis of the ToxCast estrogen receptor agonist assays to predict vitellogenin induction by chemicals in male fish

Endocrine disruptors, especially estrogen receptor (ER) agonists, have received considerable research attention. While there are several mechanistic endpoints for ER agonism in the Endocrine Disruptor Screening Program, there have been growing efforts to develop high-throughput screening assays and computational models to reduce testing cost, time, and animal use. For example, there are 16 ER agonist assays and an integrated computational model in ToxCast. In the present study, we examined the relationship between ToxCast ER agonist assays and model activity to male vitellogenin induction in the Fish-Short Term Reproduction Assay. It was found 15/16 of the assays significantly predicted potency ranks for 10 common ER agonists, and 7/16 of the assays had a significant linear correlation. The integrated model also provided comparable performance to most assays. Thus, the ToxCast ER agonist assays and model may be useful to identify endocrine disruptors and predict reproductive outcomes in fish.     

An evaluation of 25 selected ToxCast chemicals in medium‐throughput assays to detect genotoxicity

ToxCast is a multiyear effort to develop a cost‐effective approach for the US EPA to prioritize chemicals for toxicity testing. Initial evaluation of more than 500 high‐throughput (HT) microwell‐based assays without metabolic activation showed that most lacked high specificity and sensitivity for detecting genotoxicants. Thus, EPA initiated a pilot project to investigate the use of standard genotoxicity endpoints using medium‐throughput genotoxicity (MTG) assays in the context of a large testing program. Twenty‐five chemicals were selected from the ToxCast program based in part on their known genotoxicity. The two MTG assays used were the Ames II^? assay and 96‐well In Vitro MicroFlow^® Micronucleus (MN) assay. The Ames II assay showed a reasonable correlation with published Ames test data and industry submissions, though specificity was much better than sensitivity due to restraints on top concentrations as prescribed by ToxCast. Overall concordance was 73% both with and without metabolic activation. The flow MN assay had concordances of 71% and 58% with and without metabolic activation, respectively, when compared to published data and submissions. Importantly, a comparison of results without S9 from the MTG assays to an HT ToxCast p53 activation assay showed a fairly good degree of concordance (67%). The results reported here indicate that assays for genotoxicity endpoints can be conducted in a MT format and have the potential to add to the interpretation of results from large‐scale testing programs such as EPA's ToxCast program. Inherent limitations such as the top concentrations used in large scale testing programs are discussed. Environ. Mol. Mutagen. 56:468–476, 2015. © 2014 Wiley Periodicals, Inc.