Real-Time Growth Kinetics Measuring Hormone Mimicry for ToxCast Chemicals in T-47D Human Ductal Carcinoma Cells

Screening 800 putative endocrine disrupting chemicals in a representative mammal, bird, and fish using a neurochemical cell-free testing platform

There is global demand for novel ecotoxicity testing tools that are based on alternative to animal models, have high throughput potential, and may be applicable to a wide diversity of taxa. Here we scaled up a microplate-based cell-free neurochemical testing platform to screen 800 putative endocrine disrupting chemicals from the U.S. Environmental Protection Agency's ToxCast e1k library against the glutamate (NMDA), muscarinic acetylcholine (mACh), and dopamine (D2) receptors. Each assay was tested in cellular membranes isolated from brain tissues from a representative bird (zebra finch = Taeniopygia castanotis), mammal (mink = Neogale vison), and fish (rainbow trout = Oncorhynchus mykiss). The primary objective of this short communication was to make the results database accessible, while also summarising key attributes of assay performance and presenting some initial observations. In total, 7200 species-chemical-assay combinations were tested, of which 453 combinations were classified as a hit (radioligand binding changed by at least 3 standard deviations). There were some differences across species, and most hits were found for the D2 and NMDA receptors. The most active chemical was C.I. Solvent Yellow 14 followed by Diphenhydramine hydrochloride, Gentian Violet, SR271425, and Zamifenacin. Nine chemicals were tested across multiple plates with a mean relative standard deviation of the specific radioligand binding data being 24.6%. The results demonstrate that cell-free assays may serve as screening tools for large chemical libraries especially for ecological species not easily studied using traditional methods.

A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS)

Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.

Re-evaluation of the risks to public health related to the presence of bisphenol A (BPA) in foodstuffs

In 2015, EFSA established a temporary tolerable daily intake (t-TDI) for BPA of 4 μg/kg body weight (bw) per day. In 2016, the European Commission mandated EFSA to re-evaluate the risks to public health from the presence of BPA in foodstuffs and to establish a tolerable daily intake (TDI). For this re-evaluation, a pre-established protocol was used that had undergone public consultation. The CEP Panel concluded that it is Unlikely to Very Unlikely that BPA presents a genotoxic hazard through a direct mechanism. Taking into consideration the evidence from animal data and support from human observational studies, the immune system was identified as most sensitive to BPA exposure. An effect on Th17 cells in mice was identified as the critical effect; these cells are pivotal in cellular immune mechanisms and involved in the development of inflammatory conditions, including autoimmunity and lung inflammation. A reference point (RP) of 8.2 ng/kg bw per day, expressed as human equivalent dose, was identified for the critical effect. Uncertainty analysis assessed a probability of 57-73% that the lowest estimated Benchmark Dose (BMD) for other health effects was below the RP based on Th17 cells. In view of this, the CEP Panel judged that an additional uncertainty factor (UF) of 2 was needed for establishing the TDI. Applying an overall UF of 50 to the RP, a TDI of 0.2 ng BPA/kg bw per day was established. Comparison of this TDI with the dietary exposure estimates from the 2015 EFSA opinion showed that both the mean and the 95th percentile dietary exposures in all age groups exceeded the TDI by two to three orders of magnitude. Even considering the uncertainty in the exposure assessment, the exceedance being so large, the CEP Panel concluded that there is a health concern from dietary BPA exposure.

Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing

Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.

The antiviral drug telaprevir induces cell death by reducing FOXA1 expression in estrogen receptor α ( ERα )‐positive breast cancer cells

Previously, we found that telaprevir (Tel), the inhibitor of hepatitis C virus NS3/4A serine protease, reduces estrogen receptor α (ERα) content at the transcriptional level without binding to the receptor, prevents ERα transcriptional activity, and inhibits basal and 17β‐estradiol (E2)‐dependent cell proliferation in different breast cancer (BC) cell lines. Here, we further characterize the Tel action mechanisms on ERα levels and function, identify a possible molecular target of Tel in BC cells, and evaluate Tel as an antiproliferative agent for BC treatment. Tel‐dependent reduction in ERα levels and function depends on a Tel‐dependent decrease in FOXA1 levels and activity. The effect of Tel is transduced by the IGF1‐R/AKT/FOXA1 pathway, with the antiviral compound interacting with IGF1‐R. Tel prevents the proliferation of several BC cell lines, while it does not affect the proliferation of normal nontransformed cell lines, and its antiproliferative effect is correlated with the ratio of FOXA1/IGF1‐R expression. In conclusion, Tel interferes with the IGF1‐R/AKT/FOXA1 pathway and induces cell death in ERα‐expressing BC cells. Thus, we propose that this antiviral could be repurposed for the treatment of ERα‐expressing BC.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Real-Time Challenging of ERα Y537S Mutant Transcriptional Activity in Living Cells

Metastatic estrogen receptor α (ERα)-expressing breast cancer (BC) occurs after prolonged patient treatment with endocrine therapy (ET) (e.g., aromatase inhibitors—AI; 4OH-tamoxifen—4OH-Tam). Often these metastatic BCs express a mutated ERα variant (e.g., Y537S), which is transcriptionally hyperactive, sustains uncontrolled proliferation, and renders tumor cells insensitive to ET drugs. Therefore, new molecules blocking hyperactive Y537S ERα mutation transcriptional activity are requested. Here we generated an MCF-7 cell line expressing the Y537S ERα mutation stably expressing an estrogen-responsive element (ERE) promoter, which activity can be monitored in living cells. Characterization of this cell line shows both hyperactive basal transcriptional activity with respect to normal MCF-7 cells, which stably express the same ERE-based promoter and a decreased effect of selective ER downregulators (SERDs) in reducing Y537S ERα mutant transcriptional activity with respect to wild type ERα transcriptional activity. Kinetic profiles of Y537S ERα mutant-based transcription produced by both drugs inducing receptor degradation and siRNA-mediated depletion of specific proteins (e.g., FOXA1 and caveolin1) reveals biphasic dynamics of the inhibition of the receptor-regulated transcriptional effects. Overall, we report a new model where to study the behavior of the Y537S ERα mutant that can be used for the identification of new targets and pathways regulating the Y537S ERα transcriptional activity.

Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays

Cell morphology features, such as those from the Cell Painting assay, can be generated at relatively low costs and represent versatile biological descriptors of a system and thereby compound response. In this study, we explored cell morphology descriptors and molecular fingerprints, separately and in combination, for the prediction of cytotoxicity- and proliferation-related in vitro assay endpoints. We selected 135 compounds from the MoleculeNet ToxCast benchmark data set which were annotated with Cell Painting readouts, where the relatively small size of the data set is due to the overlap of required annotations. We trained Random Forest classification models using nested cross-validation and Cell Painting descriptors, Morgan and ErG fingerprints, and their combinations. While using leave-one-cluster-out cross-validation (with clusters based on physicochemical descriptors), models using Cell Painting descriptors achieved higher average performance over all assays (Balanced Accuracy of 0.65, Matthews Correlation Coefficient of 0.28, and AUC-ROC of 0.71) compared to models using ErG fingerprints (BA 0.55, MCC 0.09, and AUC-ROC 0.60) and Morgan fingerprints alone (BA 0.54, MCC 0.06, and AUC-ROC 0.56). While using random shuffle splits, the combination of Cell Painting descriptors with ErG and Morgan fingerprints further improved balanced accuracy on average by 8.9% (in 9 out of 12 assays) and 23.4% (in 8 out of 12 assays) compared to using only ErG and Morgan fingerprints, respectively. Regarding feature importance, Cell Painting descriptors related to nuclei texture, granularity of cells, and cytoplasm as well as cell neighbors and radial distributions were identified to be most contributing, which is plausible given the endpoint considered. We conclude that cell morphological descriptors contain complementary information to molecular fingerprints which can be used to improve the performance of predictive cytotoxicity models, in particular in areas of novel structural space.

Ouabain and Digoxin Activate the Proteasome and the Degradation of the ERα in Cells Modeling Primary and Metastatic Breast Cancer

Simple Summary

Breast cancer (BC) treatment relies on the detection of the estrogen receptor α (ERα). ERα-expressing BC patients are treated with anti-estrogen drugs (i.e., tamoxifen and fulvestrant). Despite their proven efficacy, these drugs cause serious side effects in a significant fraction of the patients, including both tumor insurgence in secondary organs, and resistant phenotypes, which result in a relapsing disease with scarce treatment options. Thus, new drugs for treatment of primary and metastatic BC (MBC) are needed. Here, we report the characterization of two cardiac glycosides (CGs) (i.e., ouabain and digoxin), approved by the FDA for treatment of heart disease, as novel ‘anti-estrogen’-like drugs. We found that these drugs induce ERα degradation, and prevent the proliferation of cellular models of primary and metastatic BC cells. Remarkably, we discovered that these CGs are activators of the proteasome, and therefore may be repurposed for treatment not only of BC, but also for other proteasome-based diseases.

Abstract

Estrogen receptor α expressing breast cancers (BC) are classically treated with endocrine therapy. Prolonged endocrine therapy often results in a metastatic disease (MBC), for which a standardized effective therapy is still lacking. Thus, new drugs are required for primary and metastatic BC treatment. Here, we report that the Food and Drug Administration (FDA)-approved drugs, ouabain and digoxin, induce ERα degradation and prevent proliferation in cells modeling primary and metastatic BC. Ouabain and digoxin activate the cellular proteasome, instigating ERα degradation, which causes the inhibition of 17β-estradiol signaling, induces the cell cycle blockade in the G2 phase, and triggers apoptosis. Remarkably, these effects are independent of the inhibition of the Na/K pump. The antiproliferative effects of ouabain and digoxin occur also in diverse cancer models (i.e., tumor spheroids and xenografts). Additionally, gene profiling analysis reveals that these drugs downregulate the expression of genes related to endocrine therapy resistance. Therefore, ouabain and digoxin behave as ‘anti-estrogen’-like drugs, and are appealing candidates for the treatment of primary and metastatic BCs.

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

Unexpected Impact of a Hepatitis C Virus Inhibitor on 17β-Estradiol Signaling in Breast Cancer

17β-Estradiol (E2) controls diverse physiological processes, including cell proliferation, through its binding to estrogen receptor α (ERα). E2:ERα signaling depends on both the receptor subcellular localization (e.g., nucleus, plasma membrane) and intracellular ERα abundance. Indeed, the control of ERα levels is necessary for the effects of E2, and E2 itself induces ERα degradation and cell proliferation in parallel. Thus, the modulation of intracellular ERα levels is a critical parameter for E2-induced cell proliferation. Therefore, we used this parameter as a bait to identify compounds that influence ERα levels and E2-dependent proliferation in breast cancer (BC) cells from a library of Food and Drug Administration (FDA)-approved drugs. We found that telaprevir (Tel) reduces ERα levels and inhibits BC cell proliferation. Tel is an inhibitor of the hepatitis C virus (HCV) NS3/4A serine protease, but its effect on E2:ERα signaling has not been investigated. Here, for the first time, we analyzed the effects of Tel on intracellular ERα levels and E2:ERα signaling to cell proliferation in different ERα-expressing BC cell lines. Overall, our findings demonstrate that Tel reduces intracellular ERα levels, deregulates E2:ERα signaling and inhibits E2-induced proliferation in BC cells and suggest the potential drug repurposing of Tel for the treatment of BC.

Environmental exposure to nonylphenol and cancer progression Risk-A systematic review

Environmental exposure to nonylphenol (NP) can adversely affect human and wildlife health. A systematic review was conducted to evaluate the relationship between environmental NP exposure and cancer progression risk. Literature surveys were conducted within several international databases using appropriate keywords. A comprehensive search yielded 58 eligible studies involving a wide range of adverse effects, exposure assessment methods, study designs, and experimental models. Most studies reported that NP strongly induced breast cancer progression in intended experiments. Positive associations between NP exposure and ovarian, uterine, pituitary, and testicular cancers were also reported. Although some studies reported no relation between environmental NP exposure and tumour and/or cancer progression, NP (a known endocrine disrupting chemical) induced action mechanisms in multiple experimental models and may interfere with/hyper-activate oestrogen signalling. Secretion of oestrogen and development of reproductive tissues like breasts, uteruses, and ovaries showed strong associations with possible neoplasia (i.e., uncontrolled development of tumours and/or malignant cancers). Findings of this study are important for informing policymakers to pass legislation limiting the use of environmental contaminants such as NP before all adverse effects of exposure have been determined.

A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health

The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.

Real-time measurement of E2: ERα transcriptional activity in living cells

Kinetic analyses of diverse physiological processes have the potential to unveil new aspects of the molecular regulation of cell biology at temporal levels. 17β-estradiol (E2) regulates diverse physiological effects by binding to the estrogen receptor α (ERα), which primarily works as a transcription factor. Although many molecular details of the modulation of ERα transcriptional activity have been discovered including the impact of receptor plasma membrane localization and its relative E2-evoked signaling, the knowledge of real-time ERα transcriptional dynamics in living cells is lacking. Here, we report the generation of MCF-7 and HeLa cells stably expressing a modified luciferase under the control of an E2-sensitive promoter, which activity can be continuously monitored in living cells and show that E2 induces a linear increase in ERα transcriptional activity. Ligand-independent (e.g., epidermal growth factor) receptor activation was also detected in a time-dependent manner. Kinetic profiles of ERα transcriptional activity measured in the presence of both receptor antagonists and inhibitors of ERα plasma membrane localization reveal a biphasic dynamic of receptor behavior underlying novel aspects of receptor-regulated transcriptional effects. Finally, analysis of the rate of the dose-dependent E2 induction of ERα transcriptional activity demonstrates that low doses of E2 induce an effect identical to that determined by high concentrations of E2 as a function of the duration of hormone administration. Overall, we present the characterization of sensitive stable cell lines were to study the kinetic of E2 transcriptional signaling and to identify new aspects of ERα function in different physiological or pathophysiological conditions.

Evidence for Bisphenol B Endocrine Properties: Scientific and Regulatory Perspectives

BACKGROUND

The substitution of bisphenol A (BPA) by bisphenol B (BPB), a very close structural analog, stresses the need to assess its potential endocrine properties.

OBJECTIVE

This analysis aimed to investigate whether BPB has endocrine disruptive properties in humans and in wildlife as defined by the World Health Organization (WHO) definition used in the regulatory field, that is, a) adverse effects, b) endocrine activity, and c) plausible mechanistic links between the observed endocrine activity and adverse effects.

METHODS

We conducted a systematic review to identify BPB adverse effects and endocrine activities by focusing on animal models and in vitro mechanistic studies. The results were grouped by modality (estrogenic, androgenic, thyroid hormone, steroidogenesis-related, or other endocrine activities). After critical analysis of results, lines of evidence were built using a weight-of-evidence approach to establish a biologically plausible link. In addition, the ratio of BPA to BPB potency was reported from studies investigating both bisphenols.

RESULTS

Among the 36 articles included in the analysis, 3 subchronic studies consistently reported effects of BPB on reproductive function. In rats, the 28-d and 48-week studies showed alteration of spermatogenesis associated with a lower height of the seminiferous tubules, the alteration of several sperm parameters, and a weight loss for the testis, epididymis, and seminal vesicles. In zebrafish, the results of a 21-d reproductive study demonstrated that exposed fish had a lower egg production and a lower hatching rate and viability. The in vitro and in vivo mechanistic data consistently demonstrated BPB's capacity to decrease testosterone production and to exert an estrogenic-like activity similar to or greater than BPA's, both pathways being potentially responsible for spermatogenesis impairment in rats and fish.

CONCLUSION

The available in vivo, ex vivo, and in vitro data, although limited, coherently indicates that BPB meets the WHO definition of an endocrine disrupting chemical currently used in a regulatory context. https://doi.org/10.1289/EHP5200.

Recombinational DSBs-intersected genes converge on specific disease- and adaptability-related pathways

Motivation

The budding yeast Saccharomyces cerevisiae is a model species powerful for studying the recombination of eukaryotes. Although many recombination studies have been performed for this species by experimental methods, the population genomic study based on bioinformatics analyses is urgently needed to greatly increase the range and accuracy of recombination detection. Here, we carry out the population genomic analysis of recombination in S.cerevisiae to reveal the potential rules between recombination and evolution in eukaryotes.

Results

By population genomic analysis, we discover significantly more and longer recombination events in clinical strains, which indicates that adverse environmental conditions create an obviously wider range of genetic combination in response to the selective pressure. Based on the analysis of recombinational double strand breaks (DSBs)-intersected genes (RDIGs), we find that RDIGs significantly converge on specific disease- and adaptability-related pathways, indicating that recombination plays a biologically key role in the repair of DSBs related to diseases and environmental adaptability, especially the human neurological disorders. By evolutionary analysis of RDIGs, we find that the RDIGs highly prevailing in populations of yeast tend to be more evolutionarily conserved, indicating the accurate repair of DSBs in these RDIGs is critical to ensure the eukaryotic survival or fitness.

Supplementary information

Supplementary data are available at Bioinformatics online.

Uncertainty quantification in ToxCast high throughput screening

High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.

Label-free and dynamic monitoring of cytotoxicity to the blood-brain barrier cells treated with nanometre copper oxide

A cytotoxicity study was conducted with a primary culture of the nervous system cells, including brain microvascular endothelial cells (BMECs) and astrocytes, which are important components of the blood-brain barrier. The real-time cell analysis (RTCA) was used to determine the cytotoxicity of copper-oxide nanoparticles (CuO NPs). The IC50 values of CuO NPs in astrocytes and BMECs were determined by the RTCA at different exposure times and were used as base values for further research. DNA damage after exposure to CuO NPs for 3 and 24 h was assessed using comet assay at the IC50 obtained from RTCA. The onset time of cytotoxicity induced by CuO NPs was 2 and 2-4 h post-exposure in BMECs and astrocytes, respectively. Furthermore, the degree of cytotoxicity induced by exposure to CuO NPs for 24-48 h in the BMECs and astrocytes was similar. Treatment with CuO NPs at 1/2*IC50 and 1/5*IC50 for 3 h induced genotoxicity in both cells as assessed by a measurement of DNA damage, although no cytotoxicity was observed. However, significant DNA damage was observed at all concentrations of CuO NPs used in this study, when the treatment time was 24 h.

An "EAR" on Environmental Surveillance and Monitoring: A Case Study on the Use of Exposure-Activity Ratios (EARs) to Prioritize Sites, Chemicals, and Bioactivities of Concern in Great Lakes Waters

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure-activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts.

Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

Cytotoxicity is a commonly used in vitro endpoint for evaluating chemical toxicity. In support of the U.S. Tox21 screening program, the cytotoxicity of ~10K chemicals was interrogated at 0, 8, 16, 24, 32, & 40 hours of exposure in a concentration dependent fashion in two cell lines (HEK293, HepG2) using two multiplexed, real-time assay technologies. One technology measures the metabolic activity of cells (i.e., cell viability, glo) while the other evaluates cell membrane integrity (i.e., cell death, flor). Using glo technology, more actives and greater temporal variations were seen in HEK293 cells, while results for the flor technology were more similar across the two cell types. Chemicals were grouped into classes based on their cytotoxicity kinetics profiles and these classes were evaluated for their associations with activity in the Tox21 nuclear receptor and stress response pathway assays. Some pathways, such as the activation of H2AX, were associated with the fast-responding cytotoxicity classes, while others, such as activation of TP53, were associated with the slow-responding cytotoxicity classes. By clustering pathways based on their degree of association to the different cytotoxicity kinetics labels, we identified clusters of pathways where active chemicals presented similar kinetics of cytotoxicity. Such linkages could be due to shared underlying biological processes between pathways, for example, activation of H2AX and heat shock factor. Others involving nuclear receptor activity are likely due to shared chemical structures rather than pathway level interactions. Based on the linkage between androgen receptor antagonism and Nrf2 activity, we surmise that a subclass of androgen receptor antagonists cause cytotoxicity via oxidative stress that is associated with Nrf2 activation. In summary, the real-time cytotoxicity screen provides informative chemical cytotoxicity kinetics data related to their cytotoxicity mechanisms, and with our analysis, it is possible to formulate mechanism-based hypotheses on the cytotoxic properties of the tested chemicals.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

BACKGROUND

The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions.

OBJECTIVE

Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures.

METHODS

New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.

DISCUSSION

NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams.

CONCLUSIONS

While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.

In Silico Study of In Vitro GPCR Assays by QSAR Modeling

The US EPA's ToxCast program is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast's chemical library. In silico methods such as quantitative structure-activity relationships (QSARs) are proven and cost-effective approaches to predict biological activity for untested chemicals. However, empirical data is needed to build and validate QSARs. ToxCast has developed datasets for about 2000 chemicals ideal for training and testing QSAR models. The overall goal of the present work was to develop QSAR models to fill the data gaps in larger environmental chemical lists. The specific aim of the current work was to build QSAR models for 18 G-protein-coupled receptor (GPCR) assays, part of the aminergic family. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological, and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least square discriminant analysis), SVMs (support vector machines), kNNs (k-nearest neighbors), and PLSs (partial least squares). Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision-making fitting criteria was used to evaluate the models. Finally, the models were applied to make predictions within the established chemical space limits. The most accurate model was for the bovine nonselective dopamine receptor (bDR_NS) GPCR assay, for which the classification balanced accuracy reached 0.96 in fitting and 0.95 in fivefold CV, with only two latent variables. These results demonstrate the accuracy of QSAR models to predict the biological activity of chemicals specifically for each one of the studied assays.

Screening for Chemical Contributions to Breast Cancer Risk: A Case Study for Chemical Safety Evaluation

BACKGROUND

Current approaches to chemical screening, prioritization, and assessment are being reenvisioned, driven by innovations in chemical safety testing, new chemical regulations, and demand for information on human and environmental impacts of chemicals. To conceptualize these changes through the lens of a prevalent disease, the Breast Cancer and Chemicals Policy project convened an interdisciplinary expert panel to investigate methods for identifying chemicals that may increase breast cancer risk.

METHODS

Based on a review of current evidence, the panel identified key biological processes whose perturbation may alter breast cancer risk. We identified corresponding assays to develop the Hazard Identification Approach for Breast Carcinogens (HIA-BC), a method for detecting chemicals that may raise breast cancer risk. Finally, we conducted a literature-based pilot test of the HIA-BC.

RESULTS

The HIA-BC identifies assays capable of detecting alterations to biological processes relevant to breast cancer, including cellular and molecular events, tissue changes, and factors that alter susceptibility. In the pilot test of the HIA-BC, chemicals associated with breast cancer all demonstrated genotoxic or endocrine activity, but not necessarily both. Significant data gaps persist.

CONCLUSIONS

This approach could inform the development of toxicity testing that targets mechanisms relevant to breast cancer, providing a basis for identifying safer chemicals. The study identified important end points not currently evaluated by federal testing programs, including altered mammary gland development, Her2 activation, progesterone receptor activity, prolactin effects, and aspects of estrogen receptor β activity. This approach could be extended to identify the biological processes and screening methods relevant for other common diseases.

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.

Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs < 1 or < 100, respectively. Diverse bioactivities across a range of assays and concentrations were also noted across the wider chemical space surveyed. The availability of HT exposure estimation and bioactivity screening tools provides an opportunity to incorporate a risk-based strategy for use in testing prioritization.

Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model

The U.S. Environmental Protection Agency (EPA) is considering high-throughput and computational methods to evaluate the endocrine bioactivity of environmental chemicals. Here we describe a multistep, performance-based validation of new methods and demonstrate that these new tools are sufficiently robust to be used in the Endocrine Disruptor Screening Program (EDSP). Results from 18 estrogen receptor (ER) ToxCast high-throughput screening assays were integrated into a computational model that can discriminate bioactivity from assay-specific interference and cytotoxicity. Model scores range from 0 (no activity) to 1 (bioactivity of 17β-estradiol). ToxCast ER model performance was evaluated for reference chemicals, as well as results of EDSP Tier 1 screening assays in current practice. The ToxCast ER model accuracy was 86% to 93% when compared to reference chemicals and predicted results of EDSP Tier 1 guideline and other uterotrophic studies with 84% to 100% accuracy. The performance of high-throughput assays and ToxCast ER model predictions demonstrates that these methods correctly identify active and inactive reference chemicals, provide a measure of relative ER bioactivity, and rapidly identify chemicals with potential endocrine bioactivities for additional screening and testing. EPA is accepting ToxCast ER model data for 1812 chemicals as alternatives for EDSP Tier 1 ER binding, ER transactivation, and uterotrophic assays.

Risk-Based High-Throughput Chemical Screening and Prioritization using Exposure Models and in Vitro Bioactivity Assays

We present a risk-based high-throughput screening (HTS) method to identify chemicals for potential health concerns or for which additional information is needed. The method is applied to 180 organic chemicals as a case study. We first obtain information on how the chemical is used and identify relevant use scenarios (e.g., dermal application, indoor emissions). For each chemical and use scenario, exposure models are then used to calculate a chemical intake fraction, or a product intake fraction, accounting for chemical properties and the exposed population. We then combine these intake fractions with use scenario-specific estimates of chemical quantity to calculate daily intake rates (iR; mg/kg/day). These intake rates are compared to oral equivalent doses (OED; mg/kg/day), calculated from a suite of ToxCast in vitro bioactivity assays using in vitro-to-in vivo extrapolation and reverse dosimetry. Bioactivity quotients (BQs) are calculated as iR/OED to obtain estimates of potential impact associated with each relevant use scenario. Of the 180 chemicals considered, 38 had maximum iRs exceeding minimum OEDs (i.e., BQs > 1). For most of these compounds, exposures are associated with direct intake, food/oral contact, or dermal exposure. The method provides high-throughput estimates of exposure and important input for decision makers to identify chemicals of concern for further evaluation with additional information or more refined models.

A Comparison of ToxCast Test Results with In Vivo and Other In Vitro Endpoints for Neuro, Endocrine, and Developmental Toxicities: A Case Study Using Endosulfan and Methidathion

INTRODUCTION

The U.S. Environmental Protection Agency's (EPA's) Toxicity Forecaster (ToxCast) is a potential tool for chemical prioritization, hazard identification, and risk assessment. We conducted a case study to compare ToxCast data with endpoints from other in vitro and in vivo studies for two data-rich pesticides: endosulfan and methidathion.

METHODS

ToxCast assays for endocrine disruption, development (zebrafish), and neurotoxicity were qualitatively compared to traditional neurotoxicity, developmental and reproductive toxicity findings. We also used in vitro-in vivo extrapolation to convert half-maximal activity concentrations in active ToxCast assays to rat oral equivalent doses, and quantitatively compared these to the lowest observable effect level (LOEL) from in vivo studies.

RESULTS

Endosulfan was inactive for GABAA R, unlike in vivo; but active with dopamine transporter assays and was neurotoxic in zebrafish as expected. Methidathion was not active for these endpoints in vivo or in vitro. Acetylcholinesterase inhibition was ToxCast-inactive, although both pesticides are inhibitors in vivo. ToxCast results were generally inactive for endosulfan estrogen receptor agonism and androgen receptor antagonism unlike in vivo. Calculated oral equivalent doses for estrogen receptor and androgen receptor pathways and for zebrafish assays for both compounds were generally consistent with in vivo LOELs. Endosulfan showed neurotoxicity and both pesticides showed developmental effects in the zebrafish assays, although methidathion is not developmentally toxic in vivo.

CONCLUSIONS

ToxCast's predictions showed concordance on some endpoints and nonconcordance, consisting mainly of false inactives, in several critical endpoints, likely due to a lack of metabolic activation and limitations in assay design. Zebrafish assays were good predictors of developmental toxicity and neurotoxicity for endosulfan.

Comparative endpoint sensitivity of in vitro estrogen agonist assays

Environmental and human health implications of endocrine disrupting chemicals (EDCs), particularly xenoestrogens, have received extensive study. In vitro assays are increasingly employed as diagnostic tools to comparatively evaluate chemicals, whole effluent toxicity and surface water quality, and to identify causative EDCs during toxicity identification evaluations. Recently, the U.S. Environmental Protection Agency (USEPA) initiated ToxCast under the Tox21 program to generate novel bioactivity data through high throughput screening. This information is useful for prioritizing chemicals requiring additional hazard information, including endocrine active chemicals. Though multiple in vitro and in vivo techniques have been developed to assess estrogen agonist activity, the relative endpoint sensitivity of these approaches and agreement of their conclusions remain unclear during environmental diagnostic applications. Probabilistic hazard assessment (PHA) approaches, including chemical toxicity distributions (CTD), are useful for understanding the relative sensitivity of endpoints associated with in vitro and in vivo toxicity assays by predicting the likelihood of chemicals eliciting undesirable outcomes at or above environmentally relevant concentrations. In the present study, PHAs were employed to examine the comparative endpoint sensitivity of 16 in vitro assays for estrogen agonist activity using a diverse group of compounds from the USEPA ToxCast dataset. Reporter gene assays were generally observed to possess greater endpoint sensitivity than other assay types, and the Tox21 ERa LUC BG1 Agonist assay was identified as the most sensitive in vitro endpoint for detecting an estrogenic response. When the sensitivity of this most sensitive ToxCast in vitro endpoint was compared to the human MCF-7 cell proliferation assay, a common in vitro model for biomedical and environmental monitoring applications, the ERa LUC BG1 assay was several orders of magnitude less sensitive than MCF-7. These observations highlight the importance of employing multiple assays with various molecular initiation and signaling events to inform selection, application, and interpretation of in vitro endpoint responses during future environmental diagnostic applications.

Predictive endocrine testing in the 21st century using in vitro assays of estrogen receptor signaling responses

Thousands of environmental chemicals are subject to regulatory review for their potential to be endocrine disruptors (ED). In vitro high-throughput screening (HTS) assays have emerged as a potential tool for prioritizing chemicals for ED-related whole-animal tests. In this study, 1814 chemicals including pesticide active and inert ingredients, industrial chemicals, food additives, and pharmaceuticals were evaluated in a panel of 13 in vitro HTS assays. The panel of in vitro assays interrogated multiple end points related to estrogen receptor (ER) signaling, namely binding, agonist, antagonist, and cell growth responses. The results from the in vitro assays were used to create an ER Interaction Score. For 36 reference chemicals, an ER Interaction Score >0 showed 100% sensitivity and 87.5% specificity for classifying potential ER activity. The magnitude of the ER Interaction Score was significantly related to the potency classification of the reference chemicals (p < 0.0001). ERα/ERβ selectivity was also evaluated, but relatively few chemicals showed significant selectivity for a specific isoform. When applied to a broader set of chemicals with in vivo uterotrophic data, the ER Interaction Scores showed 91% sensitivity and 65% specificity. Overall, this study provides a novel method for combining in vitro concentration response data from multiple assays and, when applied to a large set of ER data, accurately predicted estrogenic responses and demonstrated its utility for chemical prioritization.

In vitro and modelling approaches to risk assessment from the U.S. Environmental Protection Agency ToxCast programme

A significant challenge in toxicology is the 'too many chemicals' problem. Human beings and environmental species are exposed to tens of thousands of chemicals, only a small percentage of which have been tested thoroughly using standard in vivo test methods. This study reviews several approaches that are being developed to deal with this problem by the U.S. Environmental Protection Agency, under the umbrella of the ToxCast programme (http://epa.gov/ncct/toxcast/). The overall approach is broken into seven tasks: (i) identifying biological pathways that, when perturbed, can lead to toxicity; (ii) developing high-throughput in vitro assays to test chemical perturbations of these pathways; (iii) identifying the universe of chemicals with likely human or ecological exposure; (iv) testing as many of these chemicals as possible in the relevant in vitro assays; (v) developing hazard models that take the results of these tests and identify chemicals as being potential toxicants; (vi) generating toxicokinetics data on these chemicals to predict the doses at which these hazard pathways would be activated; and (vii) developing exposure models to identify chemicals for which these hazardous dose levels could be achieved. This overall strategy is described and briefly illustrated with recent examples from the ToxCast programme.