Endocrine Disruptor Screening Program Research Articles

Optimizing androgen receptor prioritization using high-throughput assay-based activity models.

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.

The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening. Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical. Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1. Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Evaluation of multigenerational effects of 2-ethylhexyl 4-hydroxybenzoate in Japanese medaka.

The Japanese medaka (Oryzias latipes) extended one-generation reproduction test (MEOGRT) (Test Guideline 890.2200) is a Tier 2 test within the Endocrine Disruptor Screening Program of the US Environmental Protection Agency (US EPA). A modified MEOGRT was used to evaluate multigenerational effects of 2-ethylhexyl 4-hydroxybenzoate (2-EHHB) under flow-through conditions starting with adults (parent generation, F0) through a 3-week reproductive phase of the second generation (F2). Fish were exposed to one of five 2-EHHB test concentrations or a dechlorinated tap water control. Fecundity was affected at the lowest exposure (5.32 μg/L) and greater sensitivity occurred in the F1 and F2 generations. Percent fertility was also diminished from no effect level observed in the F0 generation to 101 and 48.8μg/L in the F1 and F2 generations, respectively. Growth indices were decreased for F0 adult females and F1 subadults and adults at 48.8μg/L 2-EHHB. Histopathologic examination of gonads, liver, kidney, and thyroid yielded possible delayed reproductive tract development in F1 subadult males, masculinization of the renal phenotype in F1 adult females (renal tubular eosinophilia) and reduced hepatic energy storage (liver glycogen vacuoles) in F1 (11.3 and 48.8μg/L) and F2 (48.8 and 101 μg/L) males and females, respectively. Endocrine-related findings included a decrease in anal fin papillae in F2 adult males at 101 μg/L. Results of this study demonstrate effects on growth, development, and reproduction that may be mediated by endocrine (weak estrogenic) and nonendocrine mechanisms. Duration of the MEOGRT should not be routinely extended beyond the OCSPP 890 guideline study design.

EPA to adopt nonanimal testing for endocrine effects

The US Environmental Protection Agency plans to revamp its Endocrine Disruptor Screening Program (EDSP), a 25-year-old effort to screen chemicals for their ability to disrupt estrogen, androgen, and thyroid hormonal systems. In a draft white paper released Jan. 19, the EPA says certain nonanimal methods can be used as alternatives to vertebrate animal screening tests. The EPA estimates that it costs industry about $1 million per chemical and takes up to 6 years to conduct the current battery of assays in the first tier of the EDSP. Six of the assays involve the use of animals. T he agency has so far ordered first-tier testing for a small fraction of the thousands of chemicals subject to the EDSP. It issued the first set of screening orders for 67 pesticides in 2009, and 4 years later it finalized a second set of 109 chemicals. Lawyers who work closely with the chemical

Towards replacement of animal tests with in vitro assays: a gene expression biomarker predicts in vitro and in vivo estrogen receptor activity

High-throughput transcriptomics (HTTr) has the potential to support efforts to reduce or replace some animal tests. In past studies, we described a computational approach utilizing a gene expression biomarker consisting of 46 genes to predict estrogen receptor (ER) activity after chemical exposure in ER-positive human breast cancer cells including the MCF-7 cell line. We hypothesized that the biomarker model could identify ER activities of chemicals examined by Endocrine Disruptor Screening Program (EDSP) Tier 1 screening assays in which transcript profiles of the same chemicals were examined in MCF-7 cells. For the 62 chemicals examined including 5 chemicals examined in this study using RNA-Seq, the ER biomarker model accuracy was 1) 97% for in vitro reference chemicals, 2) 76–85% for guideline uterotrophic assays, and 3) 87–88% for guideline and nonguideline uterotrophic assays. For the same chemicals, these accuracies were similar or slightly better than those of the ToxCast ER model based on 18 in vitro assays. The performance of the ER biomarker model indicates that HTTr interpreted using the ER biomarker correctly identifies active and inactive ER reference chemicals. As part of the HTTr screening program the approach could rapidly identify chemicals with potential ER bioactivities for additional screening and testing.

Failure to Launch: The Endocrine Disruptor Screening Program at the U.S. Environmental Protection Agency

It has been 25 years since the U.S. Congress passed the Food Quality Protection Act of 1996, an amendment to the Food Drug and Cosmetic Act, which mandated that the US Environmental Protection Agency (EPA) test all pesticide chemicals used in food for endocrine disruption. Soon after the law passed, EPA established the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) to provide recommendations to the agency on how its Endocrine Disruptor Screening Program (EDSP) should work. Among them, the committee recommended that EDSP screening should 1) evaluate both human and ecological effects; 2) test for disruption of the estrogen, androgen, and thyroid systems; 3) evaluate pesticide and non-pesticide chemicals; and 4) implement a tiered approach. EPA adopted the recommendations and the EDSP was created in 1998. To date, the EPA has yet to fully implement the law; in other words, it has failed to test all pesticide chemicals for endocrine disruption. Of the small number that have been tested, not a single pesticide chemical has been determined to be an endocrine disruptor, and no regulatory actions have been taken. Here, we review the missed opportunities EPA had to make the EDSP a functional and effective program aimed at protecting human health and the environment. Two reports by the EPA’s Office of Inspector General from 2011 to 2021 provide the framework for our discussion.

The Extended Amphibian Metamorphosis Assay: A Thyroid-Specific and Less Animal-Intensive Alternative to the Larval Amphibian Growth and Development Assay.

The amphibian metamorphosis assay (AMA; US Environmental Protection Agency [USEPA] test guideline 890.1100 and Organisation for Economic Co‐Operation and Development test guideline 231) has been used for more than a decade to assess the potential thyroid‐mediated endocrine activity of chemicals. In 2013, in the context of the Endocrine Disruptor Screening Program of the USEPA, a Scientific Advisory Panel reviewed the results from 18 studies and recommended changes to the AMA test guideline, including a modification to a fixed‐stage design rather than a fixed‐time (i.e., 21‐d) design. We describe an extended test design for the AMA (or EAMA) that includes thyroid histopathology and time to metamorphosis (Nieuwkoop–Faber [NF] stage 62), to address both the issues with the fixed‐time design and the specific question of thyroid‐mediated adversity in a shorter assay than the larval amphibian growth and development assay (LAGDA; Organisation for Economic Co‐Operation and Development test guideline 241), using fewer animals and resources. A demonstration study was conducted with the EAMA (up to NF stage 58) using sodium perchlorate. Data analyses and interpretation of the fixed‐stage design of the EAMA are more straightforward than the fixed‐time design because the fixed‐stage design avoids confounded morphometric measurements and thyroid histopathology caused by varying developmental stages at test termination. It also results in greater statistical power to detect metamorphic delays than the fixed‐time design. By preferentially extending the AMA to NF stage 62, suitable data can be produced to evaluate thyroid‐mediated adversity and preclude the need to perform a LAGDA for thyroid mode of action analysis. The LAGDA remains of further interest should investigations of longer term effects related to sexual development modulated though the hypothalamus–pituitary–gonadal axis be necessary. However, reproduction assessment or life cycle testing is currently not addressed in the LAGDA study design. This is better addressed by higher tier studies in fish, which should then include specific thyroid‐related endpoints. Environ Toxicol Chem 2021;40:2135–2144. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Toxicity testing and endocrine disrupting chemicals.

Regulatory agencies around the world depend on standardized testing approaches to evaluate environmental chemicals for endocrine disrupting properties. The US Environmental Protection Agency (EPA) has developed a two-tiered testing approach within its Endocrine Disruptor Screening Program (EDSP). The eleven Tier 1 and three Tier 2 EDSP assays can be used to identify chemicals that act as agonists or antagonists of estrogen receptor, androgen receptor, or thyroid hormone receptor, or chemicals that interfere with steroidogenesis. Additional assays have been developed in the context of Tox21, and others have been validated by the OECD. In spite of the availability of validated toxicity tests, problems have been identified with the approaches and methods used to identify endocrine disrupting chemicals (EDCs). This chapter will provide an overview of several of these issues including: (1) The way an EDC is defined by an agency impacts whether a specific test can be used to determine if a chemical is an EDC. This is especially important when considering which assays examine outcomes that are considered "adverse effects." (2) Some assumptions about the validated studies used to identify EDCs may not be true (e.g., their reproducibility has been questioned). (3) Many of the validated assays are less sensitive than other methods that have not yet been validated. Ultimately, these and other problems contribute to the current landscape, where testing approaches have failed to protect the public from known EDCs. The chapter concludes with a review of approaches that have been taken to improve current guideline studies.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

The Alginate Immobilization of Metabolic Enzymes Platform Retrofits an Estrogen Receptor Transactivation Assay With Metabolic Competence.

The U.S. EPA Endocrine Disruptor Screening Program utilizes data across the ToxCast/Tox21 high-throughput screening (HTS) programs to evaluate the biological effects of potential endocrine active substances. A potential limitation to the use of in vitro assay data in regulatory decision-making is the lack of coverage for xenobiotic metabolic processes. Both hepatic- and peripheral-tissue metabolism can yield metabolites that exhibit greater activity than the parent compound (bioactivation) or are inactive (bioinactivation) for a given biological target. Interpretation of biological effect data for both putative endocrine active substances, as well as other chemicals, screened in HTS assays may benefit from the addition of xenobiotic metabolic capabilities to decrease the uncertainty in predicting potential hazards to human health. The objective of this study was to develop an approach to retrofit existing HTS assays with hepatic metabolism. The Alginate Immobilization of Metabolic Enzymes (AIME) platform encapsulates hepatic S9 fractions in alginate microspheres attached to 96-well peg lids. Functional characterization across a panel of reference substrates for phase I cytochrome P450 enzymes revealed substrate depletion with expected metabolite accumulation. Performance of the AIME method in the VM7Luc estrogen receptor transactivation assay was evaluated across 15 reference chemicals and 48 test chemicals that yield metabolites previously identified as estrogen receptor active or inactive. The results demonstrate the utility of applying the AIME method for identification of false-positive and false-negative target assay effects, reprioritization of hazard based on metabolism-dependent bioactivity, and enhanced in vivo concordance with the rodent uterotrophic bioassay. Integration of the AIME metabolism method may prove useful for future biochemical and cell-based HTS applications.

Review and analysis of the potential for glyphosate to interact with the estrogen, androgen and thyroid pathways.

Glyphosate was recently evaluated for its potential to interact with the estrogen, androgen and thyroid (EAT) hormone pathways, including steroidogenesis, under the United States Environmental Protection Agency's (USEPA) Endocrine Disruptor Screening Program (EDSP), then by Germany, the rapporteur Member State who led the European Annex 1 renewal for glyphosate, and then by the European Food Protection Agency (EFSA) also as part of the Annex 1 renewal for glyphosate. Under the EDSP, 11 Tier 1 assays were run following the USEPA's validated 890-series test guidelines and included five in vitro and six in vivo assays to evaluate the EAT pathways. Steroidogenesis was evaluated as part of the estrogen and androgen pathways. An up-to-date critical review has been conducted that considered results from the EDSP Tier 1 battery, guideline regulatory studies and an in-depth analysis of the literature studies that informed an endocrine assessment. A strength of this evaluation was that it included data across multiple levels of biological organization, and mammalian and nonmammalian test systems. There was strong agreement across the in vitro and in vivo Tier 1 battery, guideline studies and relevant literature studies, demonstrating that glyphosate does not interact with EAT pathways including steroidogenesis. Based on an analysis of the comprehensive toxicology database for glyphosate and the literature, this review has concluded that glyphosate does not have endocrine-disrupting properties through estrogen, androgen, thyroid and steroidogenic modes of action. © 2020 Society of Chemical Industry.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

Background:Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.Objectives:In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).Methods:The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.Results:The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.Discussion:The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program’s Integrated Chemical Environment. https://doi.org/10.1289/EHP5580

Development of an In Vitro Human Thyroid Microtissue Model for Chemical Screening.

Thyroid hormones (TH) are essential for regulating a number of diverse physiological processes required for normal growth, development, and metabolism. The US EPA Endocrine Disruptor Screening Program (EDSP) has identified several molecular thyroid targets relevant to hormone synthesis dynamics that have been adapted to high-throughput screening (HTS) assays to rapidly evaluate the ToxCast/Tox21 chemical inventories for potential thyroid disrupting chemicals (TDCs). The uncertainty surrounding the specificity of active chemicals identified in these screens and the relevance to phenotypic effects on in vivo human TH synthesis are notable data gaps for hazard identification of TDCs. The objective of this study was to develop a medium-throughput organotypic screening assay comprised of reconstructed human thyroid microtissues to quantitatively evaluate the disruptive effects of chemicals on TH production and secretion. Primary human thyroid cells procured from qualified euthyroid donors were analyzed for retention of NK2 homeobox 1 (NKX2-1), Keratin 7 (KRT7), and Thyroglobulin (TG) protein expression by high-content image analysis to verify enrichment of follicular epithelial cells. A direct comparison of 2-dimensional (2D) and 3-dimensional (3D) 96-well culture formats was employed to characterize the morphology, differential gene expression, TG production, and TH synthesis over the course of 20 days. The results indicate that modeling human thyroid cells in the 3D format was sufficient to restore TH synthesis not observed in the 2D culture format. Inhibition of TH synthesis in an optimized 3D culture format was demonstrated with reference chemicals for key molecular targets within the thyroid gland. Implementation of the assay may prove useful for interpreting phenotypic effects of candidate TDCs identified by HTS efforts currently underway in the EDSP.

Medaka (Oryzias latipes) Multigeneration Test with Triclosan.

The medaka extended one-generation reproduction test (MEOGRT) is a tier-2 study in the US Environmental Protection Agency's Endocrine Disruptor Screening Program and a level-5 study in the Organisation for Economic Co-operation and Development's conceptual framework. Integrating nonspecific apical and endocrine-specific mechanistic endpoints, results of a MEOGRT can be used, with other data, in a weight-of-evidence evaluation to establish a dose-response relationship for risk assessment and identify potential causal relationships between an endocrine mode of action and adverse effects. The MEOGRT test design was used to evaluate the multigenerational effects of the antimicrobial agent triclosan. Japanese medaka were exposed to nominal concentrations of 1.4, 2.8, 5.6, 11, and 23 μg/L triclosan and a dilution water control starting with adult medaka (F0) through hatch in the second generation (F2). No consistent or concentration-related responses occurred in the 182-d test that suggested an endocrine-mediated effect. There were no impacts on hepatic vitellogenin, secondary sex characteristics, or sex ratio that were linked to an adverse reproductive outcome. Histopathological responses were consistent with a toxic or stress effect, particularly when considered in context with observed reductions in growth. The overall population-relevant no-observed-effect concentration was 11 µg/L based on effects on growth. The results of the present study support a previously conducted weight-of-evidence evaluation concluding that triclosan does not act as an agonist or antagonist within estrogen, androgen, thyroid, or steroidogenic pathways. Environ Toxicol Chem 2019;38:1770-1783. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Control performance of fish short term reproduction assays with fathead minnow (Pimephales promelas)

The fish short-term reproduction assay (FSTRA) is an in vivo screen to assess potential interactions with the fish endocrine system. After a 21-day exposure period vitellogenin (VTG) and secondary sexual characteristics are measured in males and females. Egg production and fertility are also monitored daily throughout the test. This paper presents data from 49 studies performed to satisfy test orders from the United States Environmental Protection Agency's Endocrine Disruptor Screening Program. Data Evaluation Records were used to collate the typical control variability and performance of test parameters in FSTRAs conducted in different laboratories with fathead minnow (Pimephales promelas). We also examine the statistical power of FSTRA endpoints and assess whether available historical control data (HCD) assist evidence-based interpretation of the endpoints. Statistically significant inter-laboratory differences were found for all endpoints except survival. HCD could therefore be usefully developed on a laboratory-by-laboratory basis to aid interpretation of new study data. Reliable HCD ranges could be developed for survival, body weight/length, gonadal somatic index, fertilisation success, and male tubercle score, and used in association with stated test acceptability criteria to interpret FSTRA data. In contrast, high intra- and inter-laboratory control variability for VTG and fecundity means that HCD for these endpoints are of limited use during study interpretation.

Development of an in vitro diagnostic method to determine the genotypic sex ofXenopus laevis

A genotypic sex determination assay provides accurate gender information of individuals with well-developed phenotypic characters as well as those with poorly developed or absent of phenotypic characters. Determination of genetic sex for Xenopus laevis can be used to validate the outcomes of Tier 2 amphibian assays, and is a requirement for conducting the larval amphibian growth and development assay (LAGDA), in the endocrine disruptor screening program (EDSP), test guidelines. The assay we developed uses a dual-labeled TaqMan probe-based real-time polymerase chain reaction (real-time PCR) method to determine the genotypic sex. The reliability of the assay was tested on 37 adult specimens of X. laevis collected from in-house cultures in Eurofins EAG Agroscience, Easton. The newly designed X. laevis-specific primer pair and probe targets the DM domain gene linked-chromosome W as a master female-determining gene. Accuracy of the molecular method was assessed by comparing with phenotypic sex, determined by necropsy and histological examination of gonads for all examined specimens. Genotypic sex assignments were strongly concordant with observed phenotypic sex, confirming that the 19 specimens were male and 18 were female. The results indicate that the TaqMan® assay could be practically used to determine the genetic sex of animals with poorly developed or no phenotypic sex characteristics with 100% precision. Therefore, the TaqMan® assay is confirmed as an efficient and feasible method, providing a diagnostic molecular sex determination approach to be used in the amphibian endocrine disrupting screening programs conducted by regulatory industries. The strength of an EDSP is dependent on a reliable method to determine genetic sex in order to identify reversals of phenotypic sex in animals exposed to endocrine active compounds.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

High-throughput screening and chemotype-enrichment analysis of ToxCast phase II chemicals evaluated for human sodium-iodide symporter (NIS) inhibition

In support of the Endocrine Disruptor Screening Program (EDSP), the U.S.EPA's Office of Research and Development (ORD) is developing high-throughput screening (HTS) approaches to identify chemicals that alter target sites in the thyroid hormone (TH) pathway. The sodium iodide symporter (NIS) is a transmembrane glycoprotein that mediates iodide uptake into the thyroid as the initial step of TH biosynthesis. Previously, we screened 293 ToxCast chemicals (ph1v2) using a HEK293T cell line expressing human NIS in parallel radioactive iodide uptake (RAIU) and cell viability assays to identify potential environmental NIS inhibitors. Here, we expanded NIS inhibitor screening for a set of 768 ToxCast Phase II (ph2) chemicals, and applied a novel computational toxicology approach based on the ToxPrint chemotype to identify chemical substructures associated with NIS inhibition. Following single-concentration screening (at 1 × 10−4 M with a 20% inhibition cutoff), 235 samples (228 chemicals) were further tested in multiple-concentration (1 × 10−9 - 1 × 10−4 M) format in both RAIU and cell viability assays. The 167 chemicals that exhibited significant RAIU inhibition were then prioritized using combined RAIU and cell viability responses that were normalized relative to the known NIS inhibitor sodium perchlorate. Some of the highest ranked chemicals, such as PFOS, tributyltin chloride, and triclocarban, have been previously reported to be thyroid disruptors. In addition, several novel chemicals were identified as potent NIS inhibitors. The present results were combined with the previous ph1v2 screening results to produce two sets of binary hit-calls for 1028 unique chemicals, consisting of 273 positives exhibiting significant RAIU inhibition, and 63 positives following application of a cell viability filter. A ToxPrint chemotype-enrichment analysis identified >20 distinct chemical substructural features, represented in >60% of the active chemicals, as significantly enriched in each NIS inhibition hit-call space. A shared set of 9 chemotypes enriched in both hit-call sets indicates stable chemotype signals (insensitive to cytotoxicity filters) that can help guide structure-activity relationship (SAR) investigations and inform future research.

Development, validation and integration of in silico models to identify androgen active chemicals

Humans are exposed to large numbers of environmental chemicals, some of which potentially interfere with the endocrine system. The identification of potential endocrine disrupting chemicals (EDCs) has gained increasing priority in the assessment of environmental hazards. The U.S. Environmental Protection Agency (U.S. EPA) has developed the Endocrine Disruptor Screening Program (EDSP) which aims to prioritize and screen potential EDCs. The Toxicity Forecaster (ToxCast) program has generated data using in vitro high-throughput screening (HTS) assays measuring activity of chemicals at multiple points along the androgen receptor (AR) activity pathway. In the present study, using a large and diverse data set of 1667 chemicals provided by the U.S. EPA from the combined ToxCast AR assays in the framework of the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA). Two models were built using ADMET Predictor™; one is based on Artificial Neural Networks (ANNs) technology and the other uses a Support Vector Machine (SVM) algorithm; one model is a Decision Tree (DT) developed in R; and two models make use of differently combined sets of structural alerts (SAs) automatically extracted by SARpy. We used two strategies to integrate predictions from single models; one is based on a majority vote approach and the other on prediction convergence. These strategies led to enhanced statistical performance in most cases. Moreover, the majority vote approach improved prediction coverage when one or more single models were not able to provide any estimations. This study integrates multiple in silico approaches as a virtual screening tool for use in risk assessment of endocrine disrupting chemicals.

Evidence for Cross Species Extrapolation of Mammalian-Based High-Throughput Screening Assay Results.

High-throughput screening (HTS) and computational technologies have emerged as important tools for chemical hazard identification. The US Environmental Protection Agency (EPA) launched the Toxicity ForeCaster (ToxCast) Program, which has screened thousands of chemicals in hundreds of mammalian-based HTS assays for biological activity. The data are being used to prioritize toxicity testing on those chemicals likely to lead to adverse effects. To use HTS assays in predicting hazard to both humans and wildlife, it is necessary to understand how broadly these data may be extrapolated across species. The US EPA Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/ ) tool was used to assess conservation of the 484 protein targets represented in the suite of ToxCast assays and other HTS assays. To demonstrate the utility of the SeqAPASS data for guiding extrapolation, case studies were developed which focused on targets of interest to the US Endocrine Disruptor Screening Program and the Organisation for Economic Cooperation and Development. These case studies provide a line of evidence for conservation of endocrine targets across vertebrate species, with few exceptions, and demonstrate the utility of SeqAPASS for defining the taxonomic domain of applicability for HTS results and identifying organisms for suitable follow-up toxicity tests.