ToxCast Data Research Articles

Predictive value of the ToxCast/Tox21 high throughput toxicity screening data for approximating in vivo ecotoxicity endpoints and ecotoxicological risk in eco- surveillance applications

Ecotoxicology has long relied on assessing the hazard potential of chemicals through traditional in vivo testing methods to understand the possible risk exposure could pose to ecological taxa. In the past decade, the development of non-animal new approach methods (NAMs) for assessing chemical hazard and risk has quickly grown. These methods are often cheaper and faster than traditional toxicity testing, and thus are amenable to high-throughput toxicity testing (HTT), resulting in large datasets. The ToxCast/Tox21 HTT programs have produced in vitro data for thousands of chemicals covering a large space of biological activity. The relevance of these data to in vivo mammalian toxicity has been much explored. Interest has also grown in using these data to evaluate the risk of environmental exposures to taxa of ecological importance such as fish, aquatic invertebrates, etc.; particularly for the purpose of estimating the risk of exposure from real-world complex mixtures. Understanding the relationship and relative sensitivity of NAMs versus standardized ecotoxicological whole organism models is a key component of performing reliable read-across from mammalian in vitro data to ecotoxicological in vivo data. In this work, we explore the relationship between in vivo ecotoxicity data from several publicly available databases and the ToxCast/Tox21 data. We also performed several case studies in which we compare how using different ecotoxicity datasets, whether traditional or ToxCast-based, affects risk conclusions based on exposure to complex mixtures derived from existing large-scale chemical monitoring data. Generally, predictive value of ToxCast data for traditional in vivo endpoints (EPs) was poor (r ≤ 0.3). Risk conclusions, including identification of different chemical risk drivers and prioritized monitoring sites, were different when using HTT data vs. traditional in vivo data.

The ToxCast pipeline: updates to curve-fitting approaches and database structure.

Introduction: The US Environmental Protection Agency Toxicity Forecaster (ToxCast) program makes in vitro medium- and high-throughput screening assay data publicly available for prioritization and hazard characterization of thousands of chemicals. The assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets, from distinct proteins to more complex cellular processes like mitochondrial toxicity, nuclear receptor signaling, immune responses, and developmental toxicity. The ToxCast data pipeline (tcpl) is an open-source R package that stores, manages, curve-fits, and visualizes ToxCast data and populates the linked MySQL Database, invitrodb. Methods: Herein we describe major updates to tcpl and invitrodb to accommodate a new curve-fitting approach. The original tcpl curve-fitting models (constant, Hill, and gain-loss models) have been expanded to include Polynomial 1 (Linear), Polynomial 2 (Quadratic), Power, Exponential 2, Exponential 3, Exponential 4, and Exponential 5 based on BMDExpress and encoded by the R package dependency, tcplfit2. Inclusion of these models impacted invitrodb (beta version v4.0) and tcpl v3 in several ways: (1) long-format storage of generic modeling parameters to permit additional curve-fitting models; (2) updated logic for winning model selection; (3) continuous hit calling logic; and (4) removal of redundant endpoints as a result of bidirectional fitting. Results and discussion: Overall, the hit call and potency estimates were largely consistent between invitrodb v3.5 and 4.0. Tcpl and invitrodb provide a standard for consistent and reproducible curve-fitting and data management for diverse, targeted in vitro assay data with readily available documentation, thus enabling sharing and use of these data in myriad toxicology applications. The software and database updates described herein promote comparability across multiple tiers of data within the US Environmental Protection Agency CompTox Blueprint.

Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment.

Large repositories of in vitro bioactivity data such as US EPA's Toxicity Forecaster (ToxCast) provide a wealth of publicly accessible toxicity information for thousands of chemicals. These data can be used to calculate point-of-departure (POD) estimates via concentration-response modeling that may serve as lower bound, protective estimates of in vivo effects. However, the data are predominantly based on mammalian models and discussions to date about their utility have largely focused on potential integration into human hazard assessment, rather than application to ecological risk assessment. The goal of the present study was to compare PODs based on (1) quantitative structure-activity relationships (QSARs), (2) the 5th centile of the activity concentration at cutoff (ACC), and (3) lower-bound cytotoxic burst (LCB) from ToxCast, with the distribution of in vivo PODs compiled in the Ecotoxicology Knowledgebase (ECOTOX). While overall correlation between ToxCast ACC5 and ECOTOX PODs for 649 chemicals was weak, there were significant associations among PODs based on LCB and ECOTOX, LCB and QSARs, and ECOTOX and QSARs. Certain classes of compounds showed moderate correlation across datasets (eg, antimicrobials/disinfectants), while others, such as organophosphate insecticides, did not. Unsurprisingly, more precise classifications of the data based on ECOTOX effect and endpoint type (eg, apical vs biochemical; acute vs chronic) had a significant effect on overall relationships. Results of this research help to define appropriate roles for data from new approach methodologies in chemical prioritization and screening of ecological hazards.

Exploring the potential of ToxCast™ data for mechanism-based prioritization of chemicals in regulatory context: Case study with priority existing chemicals (PECs) under K-REACH

Recent studies have highlighted the potential of the ToxCast™ database for mechanism-based prioritization of chemicals. To explore the applicability of ToxCast data in the context of regulatory inventory chemicals, we screened 510 priority existing chemicals (PECs) regulated under the Act on the Registration and Evaluation, etc. of Chemical Substances (K-REACH) using ToxCast bioassays. In our analysis, a hit-call data matrix containing 298984 chemical-gene interactions was computed for 949 bioassays with the intended target genes, which enabled the identification of the putative toxicity mechanisms. Based on the reactivity to the chemicals, we analyzed 412 bioassays whose intended target gene families were cytochrome P450, oxidoreductase, transporter, nuclear receptor, steroid hormone, and DNA-binding. We also identified 141 chemicals based on their reactivity in the bioassays. These chemicals are mainly in consumer products including colorants, preservatives, air fresheners, and detergents. Our analysis revealed that in vitro bioactivities were involved in the relevant mechanisms inducing in vivo toxicity; however, this was not sufficient to predict more hazardous chemicals. Overall, the current results point to a potential and limitation in using ToxCast data for chemical prioritization in regulatory context in the absence of suitable in vivo data.

Occurrence of emerging contaminants in pet hair and indoor air: integrative health risk assessment using multiple ToxCast endpoints.

Indoor exposome is a growing concern, including a mixture of legacy and emerging contaminants. Recent studies suggest that indoor pollutants may accumulate in pet hair, a part of indoor exposome, increasing health risks to pet owners; however, the source and hazards of pollutants associated with pet hair are largely unknown. Here, we found that hydrophobic pollutants often had higher indoor concentrations than hydrophilic ones, and polycyclic aromatic hydrocarbons (PAHs) were the most dominant fractions (61.1%) in indoor air exposome while polycyclic musks (PCMs) had the highest concentrations among all contaminant classes in indoor dust (1559 ± 1598 ng g-1 dw) and pet hair (2831 ± 2458 ng g-1 dw). The levels of hygiene-related contaminants (PCMs, current-use pesticides (CUPs), and antibiotics) were higher in pet hair than dust due to direct contact during applications. Health risk assessment using toxicity thresholds from high-throughput screening data showed that human health risks from the five classes of indoor contaminants (PAHs, PCMs, organophosphate esters, CUPs, and antibiotics) via inhalation, ingestion, and dermal contact were within acceptable limits, but the children may be exposed to a higher risk than the adults. The thresholds estimated from the ToxCast data using endpoint sensitivity distribution make the exposome risk assessment feasible in the absence of benchmarks, which is beneficial for including a mixture of emerging pollutants in risk assessment.

Prioritizing Pesticides of Potential Concern and Identifying Potential Mixture Effects in Great Lakes Tributaries Using Passive Samplers.

To help meet the objectives of the Great Lakes Restoration Initiative with regard to increasing knowledge about toxic substances, 223 pesticides and pesticide transformation products were monitored in 15 Great Lakes tributaries using polar organic chemical integrative samplers. A screening-level assessment of their potential for biological effects was conducted by computing toxicity quotients (TQs) for chemicals with available US Environmental Protection Agency (USEPA) Aquatic Life Benchmark values. In addition, exposure activity ratios (EAR) were calculated using information from the USEPA ToxCast database. Between 16 and 81 chemicals were detected per site, with 97 unique compounds detected overall, for which 64 could be assessed using TQs or EARs. Ten chemicals exceeded TQ or EAR levels of concern at two or more sites. Chemicals exceeding thresholds included seven herbicides (2,4-dichlorophenoxyacetic acid, diuron, metolachlor, acetochlor, atrazine, simazine, and sulfentrazone), a transformation product (deisopropylatrazine), and two insecticides (fipronil and imidacloprid). Watersheds draining agricultural and urban areas had more detections and higher concentrations of pesticides compared with other land uses. Chemical mixtures analysis for ToxCast assays associated with common modes of action defined by gene targets and adverse outcome pathways (AOP) indicated potential activity on biological pathways related to a range of cellular processes, including xenobiotic metabolism, extracellular signaling, endocrine function, and protection against oxidative stress. Use of gene ontology databases and the AOP knowledgebase within the R-package ToxMixtures highlighted the utility of ToxCast data for identifying and evaluating potential biological effects and adverse outcomes of chemicals and mixtures. Results have provided a list of high-priority chemicals for future monitoring and potential biological effects warranting further evaluation in laboratory and field environments. Environ Toxicol Chem 2023;42:340-366. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Leveraging ToxCast data and protein sequence conservation to complement aquatic life criteria derivation.

The USEPA's 1985 guidelines for the derivation of aquatic life criteria (ALC) are robust but data-intensive. For many chemicals, the extensive in vivo data sets required for ALC derivation are not available. Thus, alternative analyses and processes that can provide provisional values to guide states, tribes, and other stakeholders while data accumulate and more rigorous criteria are derived would be beneficial. The overarching purpose of this study was to assess the feasibility of using data from new approach methodologies (NAMs) like ToxCast to derive first-pass, provisional values to guide chemical prioritization and resource management as a complement to traditional ALC derivation. To address this goal, the study objectives were to (1) estimate chemical potency using data from NAMs for nine compounds with available aquatic benchmarks, (2) evaluate the utility of using NAM data to elucidate potential mechanisms of toxicity to guide problem formulation, and (3) determine the species relevance of toxicity pathways for compounds with clearly defined mechanisms of action as a means to evaluate whether minimum data requirements could potentially be waived when deriving a more formal ALC. Points of departure were derived from ToxCast data based on the fifth percentile of the distribution of activity concentration above cutoff values falling below the cytotoxic burst. Mechanistic inferences were made based on active target hits in ToxCast and, where applicable, assessed for taxonomic conservation using SeqAPASS. ToxCast-based point-of-departure aligned relatively closely (six of nine test chemicals within a factor of 10; eight of nine within a factor of 100) with aquatic benchmarks from the USEPA and US Department of Energy (DOE). Moreover, pathways of toxicity gleaned from NAM data were reflective of in vivo-based findings from the literature. These results, while preliminary, and based on a limited number of substances, support the potential application of NAM data to complement traditional ALC derivation approaches and prioritization. Integr Environ Assess Manag 2023;19:224-238. © 2022 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals.

Background:Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.Objectives:We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.Methods:Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.Results:The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.Discussion:Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779

The Effects of Benoxacor on the Liver and Gut Microbiome of C57BL/6 Mice.

The toxicity of many "inert" ingredients of pesticide formulations, such as safeners, is poorly characterized, despite evidence that humans may be exposed to these chemicals. Analysis of ToxCast data for dichloroacetamide safeners with the ToxPi tool identified benoxacor as the safener with the highest potential for toxicity, especially liver toxicity. Benoxacor was subsequently administered to mice via oral gavage for 3 days at concentrations of 0, 0.5, 5, and 50 mg/kg bodyweight (b.w.). Bodyweight-adjusted liver and testes weights were significantly increased in the 50 mg/kg b.w. group. There were no overt pathologies in either the liver or the intestine. 16S rRNA analysis of the cecal microbiome revealed no effects of benoxacor on α- or β-diversity; however, changes were observed in the abundance of certain bacteria. RNAseq analysis identified 163 hepatic genes affected by benoxacor exposure. Benoxacor exposure expressed a gene regulation profile similar to dichloroacetic acid and the fungicide sedaxane. Metabolomic analysis identified 9 serum and 15 liver metabolites that were affected by benoxacor exposure, changes that were not significant after correcting for multiple comparisons. The activity of antioxidant enzymes was not altered by benoxacor exposure. In vitro metabolism studies with liver microsomes and cytosol from male mice demonstrated that benoxacor is enantioselectively metabolized by cytochrome P450 enzymes, carboxylesterases, and glutathione S-transferases. These findings suggest that the minor toxic effects of benoxacor may be due to its rapid metabolism to toxic metabolites, such as dichloroacetic acid. This result challenges the assumption that inert ingredients of pesticide formulations are safe.

Combining short-term bioassays using fish and crustacean model organisms with ToxCast in vitro data and broad-spectrum chemical analysis for environmental risk assessment of the river water (Sava, Croatia)

This study focused on the short-term whole organism bioassays (WOBs) on fish (Danio rerio) and crustaceans (Gammarus fossarum and Daphnia magna) to assess the negative biological effects of water from the major European River Sava and the comparison of the obtained results with in vitro toxicity data (ToxCast database) and Risk Quotient (RQ) methodology. Pollution profiles of five sampling sites along the River Sava were assessed by simultaneous chemical analysis of 562 organic contaminants (OCs) of which 476 were detected. At each sampling site, pharmaceuticals/illicit drugs category was mostly represented by their cumulative concentration, followed by categories industrial chemicals, pesticides and hormones. An exposure-activity ratio (EAR) approach based on ToxCast data highlighted steroidal anti-inflammatory drugs, antibiotics, antiepileptics/neuroleptics, industrial chemicals and hormones as compounds with the highest biological potential. Summed EAR-based prediction of toxicity showed a good correlation with the estimated toxicity of assessed sampling sites using WOBs. WOBs did not exhibit increased mortality but caused various sub-lethal biological responses that were dependant relative to the sampling site pollution intensity as well as species sensitivity. Exposure of G. fossarum and D. magna to river water-induced lower feeding rates increased GST activity and TBARS levels. Zebrafish D. rerio embryo exhibited a significant decrease in heartbeat rate, failure in pigmentation formation, as well as inhibition of ABC transporters. Nuclear receptor activation was indicated as the biological target of greatest concern based on the EAR approach. A combined approach of short-term WOBs, with a special emphasis on sub-lethal endpoints, and chemical characterization of water samples compared against in vitro toxicity data from the ToxCast database and RQs can provide a comprehensive insight into the negative effect of pollutants on aquatic organisms.

Neurodevelopmental toxicity assessment of flame retardants using a human DNT in vitro testing battery

Due to their neurodevelopmental toxicity, flame retardants (FRs) like polybrominated diphenyl ethers are banned from the market and replaced by alternative FRs, like organophosphorus FRs, that have mostly unknown toxicological profiles. To study their neurodevelopmental toxicity, we evaluated the hazard of several FRs including phased-out polybrominated FRs and organophosphorus FRs: 2,2′,4,4′-tetrabromodiphenylether (BDE-47), 2,2′,4,4′,5-pentabromodiphenylether (BDE-99), tetrabromobisphenol A, triphenyl phosphate, tris(2-butoxyethyl) phosphate and its metabolite bis-(2-butoxyethyl) phosphate, isodecyl diphenyl phosphate, triphenyl isopropylated phosphate, tricresyl phosphate, tris(1,3-dichloro-2-propyl) phosphate, tert-butylphenyl diphenyl phosphate, 2-ethylhexyl diphenyl phosphate, tris(1-chloroisopropyl) phosphate, and tris(2-chloroethyl) phosphate. Therefore, we used a human cell–based developmental neurotoxicity (DNT) in vitro battery covering a large variety of neurodevelopmental endpoints. Potency according to the respective most sensitive benchmark concentration (BMC) across the battery ranked from <1 μM (5 FRs), 1<10 μM (7 FRs) to the >10 μM range (3 FRs). Evaluation of the data with the ToxPi tool revealed a distinct ranking (a) than with the BMC and (b) compared to the ToxCast data, suggesting that DNT hazard of these FRs is not well predicted by ToxCast assays. Extrapolating the DNT in vitro battery BMCs to human FR exposure via breast milk suggests low risk for individual compounds. However, it raises a potential concern for real-life mixture exposure, especially when different compounds converge through diverse modes-of-action on common endpoints, like oligodendrocyte differentiation in this study. This case study using FRs suggests that human cell–based DNT in vitro battery is a promising approach for neurodevelopmental hazard assessment and compound prioritization in risk assessment.Graphical abstract

Exploring Potential Carcinogenic Activity of Per- and Polyfluorinated Alkyl Substances Utilizing High-Throughput Toxicity Screening Data.

Per- and polyfluorinated alkyl substances (PFAS) are ubiquitous, persistent, and toxic chemicals that pose public health risks. Recent carcinogenicity concerns have arisen based on epidemiological studies, animal tumor findings, and mechanistic data. Thousands of PFAS exist; however, current understanding of their toxicity is informed by studies of a select few, namely, perfluorooctanoic acid and perfluorooctanesulfonic acid. Hence, the computational, high-throughput screening tool, the US EPA CompTox Chemical Dashboard's ToxCast, was utilized to explore the carcinogenicity potential of PFAS. Twenty-three major PFAS that had sufficient in vitro ToxCast data and covered a range of structural subclasses were analyzed with the visual analytics software ToxPi, yielding a qualitative and quantitative assessment of PFAS activity in realms closely linked with carcinogenicity. A comprehensive literature search was also conducted to check the consistency of analyses with other mechanistic data streams. The PFAS were found to induce a vast range of biological perturbations, in line with several of the International Agency for Research on Cancer-defined key carcinogen characteristics. Patterns observed varied by length of fluorine-bonded chains and/or functional group within and between each key characteristic, suggesting some structure-based variability in activity. In general, the major conclusions drawn from the analysis, that is, the most notable activities being modulation of receptor-mediated effects and induction of oxidative stress, were supported by literature findings. The study helps enhance understanding of the mechanistic pathways that underlie the potential carcinogenicity of various PFAS and hence could assist in hazard identification and risk assessment for this emerging and relevant class of environmental toxicants.

Investigating Molecular Mechanisms of Immunotoxicity and the Utility of ToxCast for Immunotoxicity Screening of Chemicals Added to Food.

The development of high-throughput screening methodologies may decrease the need for laboratory animals for toxicity testing. Here, we investigate the potential of assessing immunotoxicity with high-throughput screening data from the U.S. Environmental Protection Agency ToxCast program. As case studies, we analyzed the most common chemicals added to food as well as per- and polyfluoroalkyl substances (PFAS) shown to migrate to food from packaging materials or processing equipment. The antioxidant preservative tert-butylhydroquinone (TBHQ) showed activity both in ToxCast assays and in classical immunological assays, suggesting that it may affect the immune response in people. From the PFAS group, we identified eight substances that can migrate from food contact materials and have ToxCast data. In epidemiological and toxicological studies, PFAS suppress the immune system and decrease the response to vaccination. However, most PFAS show weak or no activity in immune-related ToxCast assays. This lack of concordance between toxicological and high-throughput data for common PFAS indicates the current limitations of in vitro screening for analyzing immunotoxicity. High-throughput in vitro assays show promise for providing mechanistic data relevant for immune risk assessment. In contrast, the lack of immune-specific activity in the existing high-throughput assays cannot validate the safety of a chemical for the immune system.

Implementing in vitro bioactivity data to modernize priority setting of chemical inventories.

Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioac­tivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioac­tivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.

Exploring the Potential of ToxCast Data in Supporting Read-Across for Evaluation of Food Chemical Safety.

The intention of this study was to determine the utility of high-throughput screening (HTS) data, as exemplified by ToxCast and Tox21, for application in toxicological read-across in food-relevant chemicals. Key questions were addressed on the extent to which the HTS data could provide information enabling (1) the elucidation of underlying bioactivities associated with apical toxicological outcomes, (2) the closing of existing toxicological data gaps, and (3) the definition of the boundaries of chemical space across which bioactivity could reliably be extrapolated. Results revealed that many biological targets apparently activated within the chemical groupings lack, at this time, validated toxicity pathway associations. Therefore, as means of providing proof-of-principle, a comparatively well-characterized end point-estrogenicity-was selected for evaluation. This was facilitated through the preparation of two exploratory case studies, focusing upon groupings of paraben-gallates and pyranone-type compounds (notably flavonoids). Within both, the HTS data were seen to reflect estrogenic potencies in a manner which broadly corresponded to established structure-activity group relationships, with parabens and flavonoids displaying greater estrogen receptor affinity than benzoate esters and alternative pyranone-containing molecules, respectively. As such, utility in the identification of out-of-domain compounds was demonstrated, indicating potential for application in addressing point (3) as detailed above.

Addressing Data Gaps in Toxicity of Food Additive Chemicals using High-throughput Screening Technology

Background: More than 8,000 chemical additives are permitted in food in the United States, however, most lack adequate health and safety information. High-throughput screening tools, such as Toxicity Forecaster (ToxCast), provide opportunities to quickly assess and prioritize thousands of food additives for further investigation. Given the significant lack of in vivo data for most chemicals, we aimed to characterize the toxicity data available for chemical food additives using ToxCast.Methods: We developed an up-to-date inventory of chemical food additives utilizing information from the U.S. Food and Drug Administration, the Environmental Protection Agency, and private manufacturers. We quantified the number of chemicals with ToxCast data available, and conducted a preliminary analysis of toxicity data for selected phthalates and phthalate alternatives (di(2-ethylhexyl) phthalate (DEHP), diisononyl cyclohexane-1,2-dicarboxylate (DINCH), N,N-Diethylhydroxylamine (DEHA), and bis (2-ethylhexyl) terephthalate (DEHT)).Results: Our updated inventory indicates that there are more than 9,000 chemicals allowed in contact with food, including direct and indirect additives, food packaging chemicals, pesticide residues, and substances generally recognized as safe. Preliminary analyses suggest that approximately 23% of these have ToxCast data available. Comparing bioassay data in ToxCast for selected phthalates and phthalate alternatives, we found DEHP to be the most toxic chemical, with 40 active endpoints related to cell morphology, DNA binding, and nuclear receptors. DEHT had seven bioactive endpoints, followed by DINCH and DEHA each with 3 active endpoints. DEHA was the only plasticizer with no bioactivity for nuclear receptor assays, one important biological pathway for toxicity in humans.Conclusions: ToxCast may be a useful tool for characterizing the toxicity of chemical food additives. Future analyses will focus on more diverse biological endpoints and compare ToxCast results to available in vivo data. These analyses will help advance understanding on the appropriateness of ToxCast for regulatory decision-making.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays.

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

A Next-Generation Risk Assessment Case Study for Coumarin in Cosmetic Products.

Next-Generation Risk Assessment is defined as an exposure-led, hypothesis-driven risk assessment approach that integrates new approach methodologies (NAMs) to assure safety without the use of animal testing. These principles were applied to a hypothetical safety assessment of 0.1% coumarin in face cream and body lotion. For the purpose of evaluating the use of NAMs, existing animal and human data on coumarin were excluded. Internal concentrations (plasma Cmax) were estimated using a physiologically based kinetic model for dermally applied coumarin. Systemic toxicity was assessed using a battery of in vitro NAMs to identify points of departure (PoDs) for a variety of biological effects such as receptor-mediated and immunomodulatory effects (Eurofins SafetyScreen44 and BioMap Diversity 8 Panel, respectively), and general bioactivity (ToxCast data, an in vitro cell stress panel and high-throughput transcriptomics). In addition, in silico alerts for genotoxicity were followed up with the ToxTracker tool. The PoDs from the in vitro assays were plotted against the calculated in vivo exposure to calculate a margin of safety with associated uncertainty. The predicted Cmax values for face cream and body lotion were lower than all PoDs with margin of safety higher than 100. Furthermore, coumarin was not genotoxic, did not bind to any of the 44 receptors tested and did not show any immunomodulatory effects at consumer-relevant exposures. In conclusion, this case study demonstrated the value of integrating exposure science, computational modeling and in vitro bioactivity data, to reach a safety decision without animal data.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals.

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174–182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called “DNA binding” and “nuclear receptor” target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.