High-Throughput H295R Steroidogenesis Assay: Utility as an Alternative and a Statistical Approach to Characterize Effects on Steroidogenesis

Novel Approach to Screen Endocrine-Disrupting Chemicals via Endocrine-Enhanced Reduced Human Transcriptome

Endocrine-disrupting chemicals (EDCs) can interfere with multiple pathways and trigger different modes of action. Thus, the traditional EDC in vitro screening processes often require a battery of bioassays to cover multiple target pathways. Here we developed an endocrine-enhanced reduced human transcriptome (ERHT) focused on hormone receptor signaling induced by the EDCs regulating specific genes. ERHT was developed based on 1200 prioritized genes covering 110 endocrine-related biological pathways across eight potential adverse outcomes. The ability of this approach to identify EDCs was derived from machine learning of 1068 dose-dependent transcriptome profiles and enhanced by quantifying chemical-induced critical pathway responses, and thus, it demonstrated excellent classification performance (AUC = 0.84 ± 0.03) in internal cross-validation. We ultimately applied this approach to known EDCs and inactive substances to validate the reliability of this approach. Through external validation on 210 chemicals, the extrapolation accuracy exceeded 80%, demonstrating the outstanding practical performance of this approach. Meanwhile, the pathway responses induced by the same chemical were consistent with the experimental results reported by multiple sequencing platforms, highlighting the robustness of this approach. The above results demonstrate that this approach can provide novel insights for EDCs' high-throughput screening and comprehensive toxic mechanisms through biological pathways.

High-throughput screening to identify endocrine disruptors: Contribution of low-resolution tandem MS and high-resolution MS

BACKGROUND

Considering the large diversity of chemicals present in the environment and the need to study their effects (alone or as mixtures), the development of high-throughput in vitro assays in line with the Replacement, Reduction, Refinement (3R) strategy is essential for chemical risk assessments.

RESULTS

We developed a robust analytical workflow based on both low resolution tandem mass spectrometry (MS/MS) and high-resolution mass spectrometry (HRMS) to quantify 13 steroids in NCI-H295R cell culture medium, human plasma and serum. The workflow was validated by screening media from the NCI-H295R cell line exposed in dose-response experiments to 5 endocrine disruptors (EDs) such as bisphenol A, prochloraz, ketoconazole, atrazine and forskolin. Absolute quantifications of the 13 steroids performed on a triple quadrupole (QqQ) MS/MS demonstrated that the performances obtained were in line with OECD recommendations. HRMS (MS1-HRMS) provided measurements nearly as sensitive and as reproducible as those obtained using multiple reaction monitoring (MRM) and ELISA. A bioinformatics workflow, using HRMS, was implemented to detect and annotate disrupted metabolites. HRMS allowed to detect disruptions in pathways associated to fatty acids, purines and amino acids metabolisms after exposure to the EDs tested, in addition to that linked to steroidogenesis.

SIGNIFICANCE

We developed a robust MS1-HRMS workflow, from sample preparation to compound quantification or annotation, compatible with absolute steroid quantification, to screen NCI-H295R cell media exposed to potential EDs. Using only 200 μL of medium, the method integrates MS/MS and HRMS analyses, 96-well plate solid-phase extraction for high throughput, and automated pre-annotation for cost efficiency. This optimized workflow identifies EDs in cell assays by detecting disruptions in steroidogenesis and other biological pathways.

Minor changes to circulating steroid hormones in female rats after perinatal exposure to diethylstilbestrol or ketoconazole

Current chemical test strategies lack sensitive markers for detecting female reproductive toxicity caused by endocrine disrupting chemicals (EDCs). In search of a potentially sensitive readout, the steroidogenic disrupting effects of the well-known EDCs ketoconazole (KTZ) and diethylstilbestrol (DES) were investigated in vitro and on circulating steroid hormones in perinatally exposed female Sprague-Dawley rats. Twenty-one steroid hormones were analysed using LC-MS/MS in plasma from female rat offspring at postnatal day (PD) 6, 14, 22, 42 and 90. Most circulating steroid hormone levels increased with age except for estrone (E1), estradiol (E2) and backdoor pathway androsterone (ANDROST), which decreased after PD 22. Perinatal exposure to DES did not affect circulating steroid hormone levels at any dose or age compared to controls. KTZ exposure resulted in dose-dependent increase of corticosterone (CORTICO) at PD 6 and PD 14, with statistical significance only at PD 14. In the in vitro gold standard H295R steroidogenesis assay, twenty-one steroid hormones were measured instead of only T and E2. DES had subtle effects on steroidogenesis, whereas KTZ decreased most steroid hormones, but increased CORTICO, progesterone (P4), estriol (E3) initially (around 0.1-1 µM) before decreasing. Our data suggests that circulating steroidomic profiling may not be a sensitive readout for EDC-induced female reproductive toxicity. Further studies are needed to associate H295R assay steroidomic profiles with in vivo profiles, especially in target tissues such as adrenals or gonads. Expanding the H295R steroidogenic assay to include a comprehensive steroidomic profile may enhance its regulatory applicability.

Screening persistent organic pollutants for effects on testosterone and estrogen synthesis at human-relevant concentrations using H295R cells in 96-well plates

Many persistent organic pollutants (POPs) are suspected endocrine disruptors and it is important to investigate their effects at low concentrations relevant to human exposure. Here, the OECD test guideline #456 steroidogenesis assay was downscaled to a 96-well microplate format to screen 24 POPs for their effects on viability, and testosterone and estradiol synthesis using the human adrenocortical cell line H295R. The compounds (six polyfluoroalkyl substances, five organochlorine pesticides, ten polychlorinated biphenyls and three polybrominated diphenyl ethers) were tested at human-relevant levels (1 nM to 10 µM). Increased estradiol synthesis, above the OECD guideline threshold of 1.5-fold solvent control, was shown after exposure to 10 µM PCB-156 (153%) and PCB-180 (196%). Interestingly, the base hormone synthesis varied depending on the cell batch. An alternative data analysis using a linear mixed-effects model that include multiple independent experiments and considers batch-dependent variation was therefore applied. This approach revealed small but statistically significant effects on estradiol or testosterone synthesis for 17 compounds. Increased testosterone levels were demonstrated even at 1 nM for PCB-74 (18%), PCB-99 (29%), PCB-118 (16%), PCB-138 (19%), PCB-180 (22%), and PBDE-153 (21%). The MTT assay revealed significant effects on cell viability after exposure to 1 nM of perfluoroundecanoic acid (12%), 3 nM PBDE-153 (9%), and 10 µM of PCB-156 (6%). This shows that some POPs can interfere with endocrine signaling at concentrations found in human blood, highlighting the need for further investigation into the toxicological mechanisms of POPs and their mixtures at low concentrations relevant to human exposure.

Prediction of Endocrine-Disrupting Chemicals Related to Estrogen, Androgen, and Thyroid Hormone (EAT) Modalities Using Transcriptomics Data and Machine Learning

Endocrine-disrupting chemicals (EDCs) are chemicals that can interfere with homeostatic processes. They are a major concern for public health, and they can cause adverse long-term effects such as cancer, intellectual impairment, obesity, diabetes, and male infertility. The endocrine system is a complex machinery, with the estrogen (E), androgen (A), and thyroid hormone (T) modes of action being of major importance. In this context, the availability of in silico models for the rapid detection of hazardous chemicals is an effective contribution to toxicological assessments. We developed Qualitative Gene expression Activity Relationship (QGexAR) models to predict the propensities of chemically induced disruption of EAT modalities. We gathered gene expression profiles from the LINCS database tested on two cell lines, i.e., MCF7 (breast cancer) and A549 (adenocarcinomic human alveolar basal epithelial). We optimized our prediction protocol by testing different feature selection methods and classification algorithms, including CATBoost, XGBoost, Random Forest, SVM, Logistic regression, AutoKeras, TPOT, and deep learning models. For each EAT endpoint, the final prediction was made according to a consensus prediction as a function of the best model obtained for each cell line. With the available data, we were able to develop a predictive model for estrogen receptor and androgen receptor binding and thyroid hormone receptor antagonistic effects with a consensus balanced accuracy on a validation set ranging from 0.725 to 0.840. The importance of each predictive feature was further assessed to identify known genes and suggest new genes potentially involved in the mechanisms of action of EAT perturbation.

Application of the Key Characteristics Framework to Identify Potential Breast Carcinogens Using Publicly Available in Vivo, in Vitro, and in Silico Data

BACKGROUND

Chemicals that induce mammary tumors in rodents or activate estrogen or progesterone signaling are likely to increase breast cancer (BC) risk. Identifying chemicals with these activities can prompt steps to protect human health.

OBJECTIVES

We compiled data on rodent tumors, endocrine activity, and genotoxicity to assess the key characteristics (KCs) of rodent mammary carcinogens (MCs), and to identify other chemicals that exhibit these effects and may therefore increase BC risk.

METHODS

Using authoritative databases, including International Agency for Research on Cancer (IARC) Monographs and the US Environmental Protection's (EPA) ToxCast, we selected chemicals that induce mammary tumors in rodents, stimulate estradiol or progesterone synthesis, or activate the estrogen receptor (ER) in vitro. We classified these chemicals by their genotoxicity and strength of endocrine activity and calculated the overrepresentation (enrichment) of these KCs among MCs. Finally, we evaluated whether these KCs predict whether a chemical is likely to induce mammary tumors.

RESULTS

We identified 279 MCs and an additional 642 chemicals that stimulate estrogen or progesterone signaling. MCs were significantly enriched for steroidogenicity, ER agonism, and genotoxicity, supporting the use of these KCs to predict whether a chemical is likely to induce rodent mammary tumors and, by inference, increase BC risk. More MCs were steroidogens than ER agonists, and many increased both estradiol and progesterone. Enrichment among MCs was greater for strong endocrine activity vs. weak or inactive, with a significant trend.

DISCUSSION

We identified hundreds of compounds that have biological activities that could increase BC risk and demonstrated that these activities are enriched among MCs. We argue that many of these should not be considered low hazard without investigating their ability to affect the breast, and chemicals with the strongest evidence can be targeted for exposure reduction. We describe ways to strengthen hazard identification, including improved assessments for mammary effects, developing assays for more KCs, and more comprehensive chemical testing. https://doi.org/10.1289/EHP13233.

Comparison of the Effect of BPA and Related Bisphenols on Membrane Integrity, Mitochondrial Activity, and Steroidogenesis of H295R Cells In Vitro

Bisphenol A (BPA) is an endocrine-disruptive chemical that is widely utilized in the production of polycarbonate plastic and epoxy resin, which are used to make a wide range of consumer products, food and drink containers, and medical equipment. When the potential risk of BPA emerged, it was substituted by allegedly less harmful substitutes such as bisphenols S, F, B, and AF. However, evidence suggests that all bisphenols can have endocrine-disruptive effects, while the extent of these effects is unknown. This study aimed to determine effect of BPA, BPAF, BPB, BPF, and BPS on viability and steroidogenesis in human adrenocortical carcinoma cell line in vitro. The cytotoxicity of bisphenols was shown to be considerable at higher doses. However, at low concentrations, it improved viability as well as steroid hormone secretion, indicating that bisphenols have a biphasic, hormetic effect in biological systems. The results are consistent with the hypothesis that bisphenols selectively inhibit some steroidogenic enzymes. These findings suggest that bisphenols have the potential to disrupt cellular steroidogenesis in humans, but substantially more detailed and systematic research is needed to gain a better understanding of the risks associated with bisphenols and their endocrine-disrupting effect on humans and wildlife.

Single and mixture toxicity evaluation of avobenzone and homosalate to male zebrafish and H295R cells

Avobenzone and homosalate are widely used in sunscreens to provide ultraviolet (UV) protection, either as single compounds or in combination. Some UV filters exhibit estrogenic or anti-androgenic activities, however, studies regarding their interactions and toxicity in mixtures are limited. In this study, the effect of the toxicity of a binary mixture comprising avobenzone (0.72 μg L-1) and homosalate (1.02 and 103 μg L-1) on steroid hormone biosynthesis were investigated using male zebrafish and human adrenocortical carcinoma (H295R) cells. In fish exposed to homosalate, a significant decrease in the gonadosomatic index, testosterone level, and transcription of several genes (e.g, hsd3b2, cyp17a1, and hsd17b1) and a significant increase in the hepatosomatic index, liver steatosis, 17β-estradiol level, and transcription of vtg gene were observed. These results suggest that estrogenic and anti-androgenic effects of homosalate were mediated by the steroidogenic pathway. The presence of 0.72 μg L-1 of avobenzone augmented the anti-androgenic responses in male fish. The testosterone level in the H295R cells were significantly decreased after they were exposed to homosalate alone or in combination with avobenzone, which is consistent with observations in male zebrafish. Further studies need to be conducted to understand the endocrine disrupting properties of long-term exposure to substances typically used in sunscreens.

Evaluation of a high-throughput H295R homogenous time resolved fluorescence assay for androgen and estrogen steroidogenesis screening

The H295R test guideline assay evaluates the effect of test substances on synthesis of 17β-estradiol (E2) and testosterone (T). The objective of this study was to leverage commercial immunoassay technology to develop a more efficient H295R assay to measure E2 and T levels in 384-well format. The resulting Homogenous Time Resolved Fluorescence assay platform (H295R-HTRF) was evaluated against a training set of 36 chemicals derived from the OECD inter-laboratory validation study, EPA guideline 890.1200 aromatase assay, and azole fungicides active in the HT-H295R assay. Quality control performance criteria were met for all conditions except E2 synthesis inhibition where low basal hormone synthesis was observed. Five proficiency chemicals were active for both the E2 and T endpoints, consistent with guideline classifications. Of the nine OECD core reference chemicals, 9/9 were concordant with outcomes for E2 and 7/9 for T. Likewise, 9/13 and 11/13 OECD supplemental chemicals were concordant with anticipated effects for E2 and T, respectively. Of the 10 azole fungicides screened, 7/10 for E2 and 8/10 for T exhibited concordant outcomes for inhibition. Generally, all active chemicals in the training set demonstrated equivalent or greater potency in the H295R-HTRF assay, supporting the sensitivity of the platform. The adaptation of HTRF technology to the H295R model provides an efficient way to evaluate E2 and T modulators in accordance with guideline specifications.

Towards a microfluidic H295R steroidogenesis assay-biocompatibility study and steroid detection on a thiol-ene-based chip

The development of cell-based microfluidic assays offers exciting new opportunities in toxicity testing, allowing for integration of new functionalities, automation, and high throughput in comparison to traditional well-plate assays. As endocrine disruption caused by environmental chemicals and pharmaceuticals represents a growing global health burden, the purpose of the current study was to contribute towards the miniaturization of the H295R steroidogenesis assay, from the well-plate to the microfluidic format. Microfluidic chip fabrication with the established well-plate material polystyrene (PS) is expensive and complicated; PDMS and thiol-ene were therefore tested as potential chip materials for microfluidic H295R cell culture, and evaluated in terms of cell attachment, cell viability, and steroid synthesis in the absence and presence of collagen surface modification. Additionally, spike-recovery experiments were performed, to investigate potential steroid adsorption to chip materials. Cell aggregation with poor steroid recoveries was observed for PDMS, while cells formed monolayer cultures on the thiol-ene chip material, with cell viability and steroid synthesis comparable to cells grown on a PS surface. As thiol-ene overall displayed more favorable properties for H295R cell culture, a microfluidic chip design and corresponding cell seeding procedure were successfully developed, achieving repeatable and uniform cell distribution in microfluidic channels. Finally, H295R perfusion culture on thiol-ene chips was investigated at different flow rates (20, 10, and 2.5 µL/min), and 13 steroids were detected in eluting cell medium over 48 h at the lowest flow rate. The presented work and results pave the way for a time-resolved microfluidic H295R steroidogenesis assay.

Evaluating H295R steroidogenesis assay data for robust interpretation

The in vitro H295R steroidogenesis assay (OECD TG 456) is used to determine a chemical's potential to interfere with steroid hormone synthesis/metabolism. As positive outcomes in this assay can trigger significant higher tiered testing, we compiled a stakeholder database of reference and test item H295R data to characterize assay outcomes. Information concerning whether a Level 5 reproductive toxicity study was triggered due to a positive outcome in the H295R assay was also included. Quality control acceptance criteria were not always achieved, suggesting this assay is challenging to conduct within the guideline specifications. Analysis of test item data demonstrated that pairwise significance testing to controls allowed for overly sensitive statistically significant positive outcomes, which likely contribute to the assay's high positive hit rate. Complementary interpretation criteria (e.g., 1.5-fold change threshold) markedly reduced the rate of equivocal and positive outcomes thus improving identification of robust positive effects in the assay. Finally, a case study (positive H295R outcome and no endocrine adversity in vivo) is presented, which suggests that stricter data interpretation criteria could refine necessary in vivo follow-up testing. Overall, the described additional criteria could improve H295R data interpretation and help inform on how to best leverage this assay for regulatory purposes.

Identification of a novel class of cortisol biosynthesis inhibitors and its implications in a therapeutic strategy for hypercortisolism

AIMS

Dysregulation of adrenocortical steroid (corticosteroids) biosynthesis leads to pathological conditions such as Cushing's syndrome. Although several classes of steroid biosynthesis inhibitors have been developed to treat cortisol overproduction, limitations such as insufficient efficacy, adverse effects, and/or tolerability still remain. The present study aimed to develop a new class of small molecules that inhibit cortisol production, and investigated their putative modes of action.

MAIN METHODS

We screened an in-house chemical library with drug-like chemical scaffolds using human adrenocortical NCI-H295R cells. We then evaluated and validated the effects of the selected compounds at multiple regulatory steps of the adrenal steroidogenic pathway. Finally, genome-wide RNA expression analysis coupled with gene enrichment analysis was conducted to infer possible action mechanisms.

KEY FINDINGS

A subset of benzimidazolylurea derivatives, including a representative compound (designated as CJ28), inhibited both basal and stimulated production of cortisol and related intermediate steroids. CJ28 attenuated the mRNA expression of multiple genes involved in steroidogenesis and cholesterol biosynthesis. Furthermore, CJ28 significantly attenuated de novo cholesterol biosynthesis, which contributed to its suppression of cortisol production.

SIGNIFICANCE

We identified a novel chemical scaffold that exerts inhibitory effects on cortisol and cholesterol biosynthesis via coordinated transcriptional silencing of gene expression networks. Our findings also reveal an additional adrenal-directed pharmacological strategy for hypercortisolism involving a combination of inhibitors targeting steroidogenesis and de novo cholesterol biosynthesis.

New Approach Methodologies for the Endocrine Activity Toolbox: Environmental Assessment for Fish and Amphibians

Multiple in vivo test guidelines focusing on the estrogen, androgen, thyroid, and steroidogenesis pathways have been developed and validated for mammals, amphibians, or fish. However, these tests are resource-intensive and often use a large number of laboratory animals. Developing alternatives for in vivo tests is consistent with the replacement, reduction, and refinement principles for animal welfare considerations, which are supported by increasing mandates to move toward an "animal-free" testing paradigm worldwide. New approach methodologies (NAMs) hold great promise to identify molecular, cellular, and tissue changes that can be used to predict effects reliably and more efficiently at the individual level (and potentially on populations) while reducing the number of animals used in (eco)toxicological testing for endocrine disruption. In a collaborative effort, experts from government, academia, and industry met in 2020 to discuss the current challenges of testing for endocrine activity assessment for fish and amphibians. Continuing this cross-sector initiative, our review focuses on the current state of the science regarding the use of NAMs to identify chemical-induced endocrine effects. The present study highlights the challenges of using NAMs for safety assessment and what work is needed to reduce their uncertainties and increase their acceptance in regulatory processes. We have reviewed the current NAMs available for endocrine activity assessment including in silico, in vitro, and eleutheroembryo models. New approach methodologies can be integrated as part of a weight-of-evidence approach for hazard or risk assessment using the adverse outcome pathway framework. The development and utilization of NAMs not only allows for replacement, reduction, and refinement of animal testing but can also provide robust and fit-for-purpose methods to identify chemicals acting via endocrine mechanisms. Environ Toxicol Chem 2023;42:757-777. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

AOP Report: Adverse Outcome Pathways for Aromatase Inhibition or Androgen Receptor Agonism Leading to Male-Biased Sex Ratio and Population Decline in Fish

Screening and testing of potential endocrine-disrupting chemicals for ecological effects are examples of risk assessment/regulatory activities that can employ adverse outcome pathways (AOPs) to establish linkages between readily measured alterations in endocrine function and whole organism- and population-level responses. Of particular concern are processes controlled by the hypothalamic-pituitary-gonadal/thyroidal (HPG/T) axes. However, the availability of AOPs suitable to meet this need is currently limited in terms of species and life-stage representation relative to the diversity of endpoints influenced by HPG/T function. In our report we describe two novel AOPs that comprise a simple AOP network focused on the effects of chemicals on sex differentiation during early development in fish. The first AOP (346) documents events starting with inhibition of cytochrome P450 aromatase (CYP19), resulting in decreased availability of 17β-estradiol during gonad differentiation, which increases the occurrence of testis formation, resulting in a male-biased sex ratio and consequent population-level declines. The second AOP (376) is initiated by activation of the androgen receptor (AR), also during sexual differentiation, again resulting in a male-biased sex ratio and population-level effects. Both AOPs are strongly supported by existing physiological and toxicological evidence, including numerous fish studies with model CYP19 inhibitors and AR agonists. Accordingly, AOPs 346 and 376 provide a basis for more focused screening and testing of chemicals with the potential to affect HPG function in fish during early development. Environ Toxicol Chem 2023;42:747-756. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Platelet-derived growth factor D expression in adrenal cells is modulated by corticosteroids: putative role in adrenal suppression

BACKGROUND

Adrenal suppression is a clinically concerning side effect of inhaled corticosteroid (ICS) treatment in patients with asthma. Increased susceptibility to ICS-induced adrenal suppression has previously been identified in those with the rs591118 polymorphism in platelet-derived growth factor D (PDGFD). The mechanism underpinning this relationship is not known.

METHODS

H295R cells were genotyped for rs591118 using a validated Taqman PCR allelic discrimination assay. H295R cell viability was determined after treatment with beclometasone and fluticasone (range 0-330 μM). Cortisol was measured in cell culture medium using competitive enzyme immunoassay.

RESULTS

PDGFD protein expression in H295R cells was confirmed using Western blotting. When ACTH and forskolin were added to H295R cells, a reduction in PDGFD expression was seen, which was then restored by incubation with prochloraz, a known inhibitor of steroidogenesis. A dose-dependent, decrease in PDGFD expression was observed with beclometasone (over a 24 h incubation period) but not with beclometasone incubations beyond 24 h nor with fluticasone (at 24 or 48 h).

CONCLUSIONS

H295R cells express PDGFD protein, which can be modulated by incubation with steroidogenesis agonists and antagonists and additionally with exogenous beclometasone.

IMPACT

PDGFD is expressed in the human adrenal cell line, H295R, and expression can be modulated by beclometasone as well as agonists/antagonists of steroidogenesis. This builds on previous research that identified a SNP in PDGFD (rs591118) as an independent risk factor for adrenal suppression in adults and children with obstructive airway disease treated with inhaled corticosteroids. First in vitro experiments to support a link between the PDGF and cortisol production pathways, supporting the hypothesis that PDGFD variants can affect an individual's sensitivity to corticosteroid-induced adrenal suppression.

Evaluation of Endocrine Related Adverse Effects of Non-Endocrine Targeted Pharmaceuticals in Cellular Systems

BACKGROUND

Prenatal period is a critical developmental phase that is sensitive to hormonal disruption by natural and/or exogenous hormones. Some pharmaceuticals frequently prescribed and used safely during pregnancy are shown to interact with the developmental programming of fetus, resulting in endocrine-related adverse effects.

OBJECTIVE

In this research, we aimed to determine the endocrine disrupting potential of paracetamol, indomethacin, alpha-methyldopa and pantoprazole which are frequently prescribed pharmaceuticals during pregnancy.

METHODS

In vitro aromatase inhibitory, estrogen receptor (ER) agonist/antagonist (E-Screen assay) and hormone biosynthesis modulatory effects (H295R steroidogenesis assay) of the selected pharmaceuticals were evaluated. Furthermore, their effects on viability of MCF-7/BUS and H295R cells were also evaluated by MTT assay.

RESULTS

None of the pharmaceuticals affected H295R cell viability. Only indomethacin reduced MCF- 7/BUS cell viability at 100μM and 300μM. Among the tested pharmaceuticals, only paracetamol and indomethacin showed aromatase inhibitory activity with IC50 values of 14.7 x 10-5 M and 57.6 x 10-5 M, respectively. Moreover, indomethacin displayed a biphasic ER agonist effect. ER antagonist effects of indomethacin and pantoprazole were confirmed by performing two stepped E-Screen assay. After the partial validation of the H295R steroidogenesis assay with forskolin and prochloraz, the effects of pharmaceuticals on synthesis of testosterone (T) and estradiol (E2) levels were tested. Alpha-methyldopa increased E2 at all tested concentrations and T at 1.48 and 4.4μM. Contrarily other tested pharmaceuticals did not affect steroidogenesis.

CONCLUSION

Present data suggest that all tested pharmaceuticals may have potential endocrine disrupting effect, which should be considered when used in pregnancy.

Cheminformatics analysis of chemicals that increase estrogen and progesterone synthesis for a breast cancer hazard assessment

Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes.

Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for >2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure-activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85-98%) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71% with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81% using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study

Next generation risk assessment is defined as a knowledge-driven system that allows for cost-efficient assessment of human health risk related to chemical exposure, without animal experimentation. One of the key features of next generation risk assessment is to facilitate prioritization of chemical substances that need a more extensive toxicological evaluation, in order to address the need to assess an increasing number of substances. In this case study focusing on chemicals in food, we explored how exposure data combined with the Threshold of Toxicological Concern (TTC) concept could be used to prioritize chemicals, both for existing substances and new substances entering the market. Using a database of existing chemicals relevant for dietary exposure we calculated exposure estimates, followed by application of the TTC concept to identify substances of higher concern. Subsequently, a selected set of these priority substances was screened for toxicological potential using high-throughput screening (HTS) approaches. Remarkably, this approach resulted in alerts for a selection of substances that are already on the market and represent relevant exposure in consumers. Taken together, the case study provides proof-of-principle for the approach taken to identify substances of concern, and this approach can therefore be considered a supportive element to a next generation risk assessment strategy.

Towards regulation of Endocrine Disrupting chemicals (EDCs) in water resources using bioassays - A guide to developing a testing strategy

Endocrine disrupting chemicals (EDCs) are found in every environmental medium and are chemically diverse. Their presence in water resources can negatively impact the health of both human and wildlife. Currently, there are no mandatory screening mandates or regulations for EDC levels in complex water samples globally. Bioassays, which allow quantifying in vivo or in vitro biological effects of chemicals are used commonly to assess acute toxicity in water. The existing OECD framework to identify single-compound EDCs offers a set of bioassays that are validated for the Estrogen-, Androgen-, and Thyroid hormones, and for Steroidogenesis pathways (EATS). In this review, we discussed bioassays that could be potentially used to screen EDCs in water resources, including in vivo and in vitro bioassays using invertebrates, fish, amphibians, and/or mammalians species. Strengths and weaknesses of samples preparation for complex water samples are discussed. We also review how to calculate the Effect-Based Trigger values, which could serve as thresholds to determine if a given water sample poses a risk based on existing quality standards. This work aims to assist governments and regulatory agencies in developing a testing strategy towards regulation of EDCs in water resources worldwide. The main recommendations include 1) opting for internationally validated cell reporter in vitro bioassays to reduce animal use & cost; 2) testing for cell viability (a critical parameter) when using in vitro bioassays; and 3) evaluating the recovery of the water sample preparation method selected. This review also highlights future research avenues for the EDC screening revolution (e.g., 3D tissue culture, transgenic animals, OMICs, and Adverse Outcome Pathways (AOPs)).

Innovation in regulatory approaches for endocrine disrupting chemicals: The journey to risk assessment modernization in Canada

Globally, regulatory authorities grapple with the challenge of assessing the hazards and risks to human and ecosystem health that may result from exposure to chemicals that disrupt the normal functioning of endocrine systems. Rapidly increasing number of chemicals in commerce, coupled with the reliance on traditional, costly animal experiments for hazard characterization - often with limited sensitivity to many important mechanisms of endocrine disruption -, presents ongoing challenges for chemical regulation. The consequence is a limited number of chemicals for which there is sufficient data to assess if there is endocrine toxicity and hence few chemicals with thorough hazard characterization. To address this challenge, regulatory assessment of endocrine disrupting chemicals (EDCs) is benefiting from a revolution in toxicology that focuses on New Approach Methodologies (NAMs) to more rapidly identify, prioritize, and assess the potential risks from exposure to chemicals using novel, more efficient, and more mechanistically driven methodologies and tools. Incorporated into Integrated Approaches to Testing and Assessment (IATA) and guided by conceptual frameworks such as Adverse Outcome Pathways (AOPs), emerging approaches focus initially on molecular interactions between the test chemical and potentially vulnerable biological systems instead of the need for animal toxicity data. These new toxicity testing methods can be complemented with in silico and computational toxicology approaches, including those that predict chemical kinetics. Coupled with exposure data, these will inform risk-based decision-making approaches. Canada is part of a global network collaborating on building confidence in the use of NAMs for regulatory assessment of EDCs. Herein, we review the current approaches to EDC regulation globally (mainly from the perspective of human health), and provide a perspective on how the advances for regulatory testing and assessment can be applied and discuss the promises and challenges faced in adopting these novel approaches to minimize risks due to EDC exposure in Canada, and our world.

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.

Application of an in Vitro Assay to Identify Chemicals That Increase Estradiol and Progesterone Synthesis and Are Potential Breast Cancer Risk Factors

BACKGROUND

Established breast cancer risk factors, such as hormone replacement therapy and reproductive history, are thought to act by increasing estrogen and progesterone (P4) activity.

OBJECTIVE

We aimed to use in vitro screening data to identify chemicals that increase the synthesis of estradiol (E2) or P4 and evaluate potential risks.

METHOD

Using data from a high-throughput (HT) in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified chemicals that increased estradiol (E2-up) or progesterone (P4-up) in human H295R adrenocortical carcinoma cells. We prioritized chemicals by their activity. We compiled in vivo studies and assessments about carcinogenicity and reproductive/developmental (repro/dev) toxicity. We identified exposure sources and predicted intakes from the U.S. EPA's ExpoCast.

RESULTS

We found 296 chemicals increased E2 (182) or P4 (185), with 71 chemicals increasing both. In vivo data often showed effects consistent with this mechanism. Of the E2- and P4-up chemicals, about 30% were likely repro/dev toxicants or carcinogens, whereas only 5-13% were classified as unlikely. However, most of the chemicals had insufficient in vivo data to evaluate their effects. Of 45 chemicals associated with mammary gland effects, and also tested in the H294R assay, 29 increased E2 or P4, including the well-known mammary carcinogen 7,12-dimethylbenz(a)anthracene. E2- and P4-up chemicals include pesticides, consumer product ingredients, food additives, and drinking water contaminants.

DISCUSSION

The U.S. EPA's in vitro screening data identified several hundred chemicals that should be considered as potential risk factors for breast cancer because they increased E2 or P4 synthesis. In vitro data is a helpful addition to current toxicity assessments, which are not sensitive to mammary gland effects. Relevant effects on the mammary gland are often not noticed or are dismissed, including for 2,4-dichlorophenol and cyfluthrin. Fifty-three active E2-up and 59 active P4-up chemicals that are in consumer products, food, pesticides, or drugs have not been evaluated for carcinogenic potential and are priorities for study and exposure reduction. https://doi.org/10.1289/EHP8608.

Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites

Given the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies that interrogate levels of small molecules in biospecimens have been proposed for discovering causes of chronic diseases. We conducted a study to explore associations between environmental chemicals and endogenous molecules using Gaussian graphical models (GGMs) of non-targeted metabolomics data measured in a cohort of California women firefighters and office workers. GGMs revealed many exposure-metabolite associations, including that exposures to mono-hydroxyisononyl phthalate, ethyl paraben and 4-ethylbenzoic acid were associated with metabolites involved in steroid hormone biosynthesis, and perfluoroalkyl substances were linked to bile acids-hormones that regulate cholesterol and glucose metabolism-and inflammatory signaling molecules. Some hypotheses generated from these findings were confirmed by analysis of data from the National Health and Nutrition Examination Survey. Taken together, our findings demonstrate a novel approach to discovering associations between chemical exposures and biological processes of potential relevance for disease causation.

Primary Cultures and Cell Lines for In Vitro Modeling of the Human Adrenal Cortex

The human adrenal cortex is a complex endocrine organ that produces mineralocorticoids, glucocorticoids and androgens. These steroids are produced in distinct cell types located within the glomerulosa, fasciculata and reticularis of the adrenal cortex. Abnormal adrenal steroidogenesis leads to a variety of diseases that can cause hypertension, metabolic syndrome, infertility and premature adrenarche. The adrenal cortex can also develop steroid-producing adenomas and rarely adrenocortical carcinomas. In vitro cell culture models provide important tools to study molecular and cellular mechanisms controlling both the physiologic and pathologic conditions of the adrenal cortex. In addition, the presence of multiple steroid-metabolizing enzymes within adrenal cells makes it a model for defining possible endocrine disruptors that might block these enzymes. The regulation and dysregulation of human adrenal steroid production and cell division/tumor growth can be studied using freshly isolated cells but this requires access to human adrenal glands, which are not available to most investigators. Immortalized human adrenocortical cell lines have proven to be of considerable value in studying the molecular and biochemical mechanisms controlling adrenal steroidogenesis and tumorigenesis. Current human adrenal cell lines include the original NCI-H295 and its substrains: H295A, H295R, HAC13, HAC15, HAC50 and H295RA as well as the recently established MUC-1, CU-ACC1 and CU-ACC2. The current review will discuss the use of primary cultures of fetal and adult adrenal cells as well as adrenocortical cell lines as in vitro models for the study of human adrenal physiology and pathophysiology.

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

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.

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 ∼875,000 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.

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 ∼ 875,000 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.

Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

The present study adapted an existing high content imaging-based high-throughput phenotypic profiling (HTPP) assay known as "Cell Painting" for bioactivity screening of environmental chemicals. This assay uses a combination of fluorescent probes to label a variety of organelles and measures a large number of phenotypic features at the single cell level in order to detect chemical-induced changes in cell morphology. First, a small set of candidate phenotypic reference chemicals (n = 14) known to produce changes in the cellular morphology of U-2 OS cells were identified and screened at multiple time points in concentration-response format. Many of these chemicals produced distinct cellular phenotypes that were qualitatively similar to those previously described in the literature. A novel workflow for phenotypic feature extraction, concentration-response modeling and determination of in vitro thresholds for chemical bioactivity was developed. Subsequently, a set of 462 chemicals from the ToxCast library were screened in concentration-response mode. Bioactivity thresholds were calculated and converted to administered equivalent doses (AEDs) using reverse dosimetry. AEDs were then compared to effect values from mammalian toxicity studies. In many instances (68%), the HTPP-derived AEDs were either more conservative than or comparable to the in vivo effect values. Overall, we conclude that the HTPP assay can be used as an efficient, cost-effective and reproducible screening method for characterizing the biological activity and potency of environmental chemicals for potential use in in vitro-based safety assessments.

Determination of Endocrine Disruption Potential of Bisphenol A Alternatives in Food Contact Materials Using In Vitro Assays: State of the Art and Future Challenges

Alternatives to bisphenol A (BPA) are developed for food contact materials as a result of increasing evidence of exposure-correlated harmful effects of BPA. In vitro assays provide the fast, affordable, and mechanism insightful ways to screen endocrine disruption (ED), which is a major concern of new BPA alternatives. In this review, we summarize the safety and regulation information on the alternatives to BPA, review the state of the art of in vitro assays for ED evaluation, highlight their advantages and limitations, and discuss the challenges and future research needs. Our review shows that ligand binding, reporter gene, cell proliferation, and steroidogenesis are four commonly used in vitro assays to determine the ED at the response of receptor, gene transcription, and whole cell level. Major challenges are found from in vitro-in vivo translation and identification of ED chemicals in polymers. More studies on these areas are needed in the future.

Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data

Synthesis of 11 steroid hormones in human adrenocortical carcinoma cells (H295R) was measured in a high-throughput steroidogenesis assay (HT-H295R) for 656 chemicals in concentration-response as part of the US Environmental Protection Agency's ToxCast program. This work extends previous analysis of the HT-H295R dataset and model by examining the utility of a novel prioritization metric based on the Mahalanobis distance that reduced these 11-dimensional data to 1-dimension via calculation of a mean Mahalanobis distance (mMd) at each chemical concentration screened for all hormone measures available. Herein, we evaluated the robustness of mMd values, and demonstrate that covariance and variance of the hormones measured appear independent of the chemicals screened and are inherent to the assay; the Type I error rate of the mMd method is less than 1%; and, absolute fold changes (up or down) of 1.5 to 2-fold have sufficient power for statistical significance. As a case study, we examined hormone responses for aromatase inhibitors in the HT-H295R assay and found high concordance with other ToxCast assays for known aromatase inhibitors. Finally, we used mMd and other ToxCast cytotoxicity data to demonstrate prioritization of the most selective and active chemicals as candidates for further in vitro or in silico screening.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Evaluating Chemicals for Thyroid Disruption: Opportunities and Challenges with in Vitro Testing and Adverse Outcome Pathway Approaches

BACKGROUND

Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented.

OBJECTIVES

We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism.

DISCUSSION

There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297.

Gestational exposure to an epidemiologically defined mixture of phthalates leads to gonadal dysfunction in mouse offspring of both sexes

The increasing concern for the reproductive toxicity of abundantly used phthalates requires reliable tools for exposure risk assessment to mixtures of chemicals, based on real life human exposure and disorder-associated epidemiological evidence. We herein used a mixture of four phthalate monoesters (33% mono-butyl phthalate, 16% mono-benzyl phthalate, 21% mono-ethyl hexyl phthalate, and 30% mono-isononyl phthalate), detected in 1st trimester urine of 194 pregnant women and identified as bad actors for a shorter anogenital distance (AGD) in their baby boys. Mice were treated with 0, 0.26, 2.6 and 13 mg/kg/d of the mixture, corresponding to 0x, 10x, 100x, 500x levels detected in the pregnant women. Adverse outcomes detected in the reproductive system of the offspring in pre-puberty and adulthood included reduced AGD index and gonadal weight, changes in gonadal histology and altered expression of key regulators of gonadal growth and steroidogenesis. Most aberrations were apparent in both sexes, though more pronounced in males, and exhibited a non-monotonic pattern. The phthalate mixture directly affected expression of steroidogenesis as demonstrated in a relevant in vitro model. The detected adversities at exposures close to the levels detected in pregnant women, raise concern on the existing safety limits for early-life human exposures and emphasizes the need for re-evaluation of the exposure risk.