New approach methods for testing chemicals for endocrine disruption potential

Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data

Transthyretin (TTR) is a key transporter of the thyroid hormone thyroxine, and chemicals that bind to TTR, displacing the hormone, can disrupt the endocrine system, even at low concentrations. This study evaluates computational modeling strategies developed during the Tox24 Challenge, using a data set of 1512 compounds tested for TTR binding affinity. Individual models from nine top-performing teams were analyzed for performance and uncertainty using regression metrics and applicability domains (AD). Consensus models were developed by averaging predictions across these models, with and without consideration of their ADs. While applying AD constraints in individual models generally improved external prediction accuracy (at the expense of reduced chemical space coverage), it had limited additional benefit for consensus models. Results showed that consensus models outperformed individual models, achieving a root-mean-square error (RMSE) of 19.8% on the test set, compared to an average RMSE of 20.9% for the nine individual models. Outliers consistently identified in several of these models indicate potential experimental artifacts and/or activity cliffs, requiring further investigation. Substructure importance analysis revealed that models prioritized different chemical features, and consensus averaging harmonized these divergent perspectives. These findings highlight the value of consensus modeling in improving predictive performance and addressing model limitations. Future work should focus on expanding chemical space coverage and refining experimental data sets to support public health protection.

Rapid determination of chemical losses in a microplate bioassay using fluorescence spectroscopy

The increase in production and innovation of chemicals that humans interface with has enhanced the need for rapid toxicity testing of new and existing chemicals. This need, along with efforts to reduce animal testing, has led to the development of high-throughput bioassays typically conducted in microplates. These bioassays offer time and resource advantages over traditional animal models; however, significant chemical losses can occur in microplates. Current methods for measuring chemical losses in microplates require extensive sample preparation and highly sensitive instruments. We propose the use of fluorescence spectroscopy to measure chemical losses in high-throughput bioassays as a low resource alternative to the existing methods. A fluorescent plate reader was used to develop methods for quantifying the aqueous concentrations of two chemicals, 2-hydroxynaphthalene and acridine, in microwells of a 96-well microplate. A high-throughput, 5 day embryonic zebrafish bioassay was used as the model bioassay for method development. Chemical losses were attributed to a combination of photodegradation, sorption, and uptake by the zebrafish embryos, kinetics of which were derived from a pseudo-first order model. Chemical uptake amount was calculated to be approximately 50% and 21% of the total chemical amount added for 2-hydroxynaphthalene and acridine, respectively. Unexpected cranial deformities were observed in the embryonic zebrafish, suggesting further investigation of potential additive toxicity of the ultraviolet radiation exposure from fluorescence measurements and chemical exposure is needed. Nonetheless, this novel method provides a rapid, low resource approach to measuring chemical losses in microplates that can be extended to a variety of autofluorescent chemicals and microplate-based bioassays.

Comparison of in vitro thyroxine (T4) metabolism between Wistar rat and human hepatocyte cultures

In vitro assays remain relatively new in exploring human relevance of liver, in particular nuclear receptor-mediated perturbations of the hypothalamus-pituitary-thyroid axis seen in rodents, mainly in the rat. Consistent with in vivo data, we confirm that thyroid hormone thyroxine metabolism was 9 times higher in primary rat hepatocytes (PRH) than in primary human hepatocytes (PHH) cultured in a 2D sandwich (2Dsw) configuration. In addition, thyroxine glucuronide (T4-G) was by far the major metabolite formed in both species (99.1% in PRH and 69.7% in PHH) followed by thyroxine sulfate (T4-S, 0.7% in PRH and 18.1% in PHH) and triiodothyronine/reverse triiodothyronine (T3/rT3, 0.2% in PRH and 12.2% in PHH). After a 7-day daily exposure to orphan receptor-mediated liver inducers, T4 metabolism was strongly increased in PRH, almost exclusively through increased T4-G formation. These results were consistent with the inductions of glucuronosyltransferase Ugt2b1 and canalicular transporter Mrp2. PHH also responded to activation of the three nuclear receptors, with mainly induction of glucuronosyltransferase UGT1A1 and canalicular transporter MRP2. Despite this, T4 disappearance rate and secreted T4 metabolites were only slightly increased in PHH. Overall, our data highlight that cryopreserved hepatocytes in 2Dsw culture allowing long-term exposure and species comparison are of major interest in improving liver-mediated human safety assessment.

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.

A framework for establishing scientific confidence in new approach methodologies

Robust and efficient processes are needed to establish scientific confidence in new approach methodologies (NAMs) if they are to be considered for regulatory applications. NAMs need to be fit for purpose, reliable and, for the assessment of human health effects, provide information relevant to human biology. They must also be independently reviewed and transparently communicated. Ideally, NAM developers should communicate with stakeholders such as regulators and industry to identify the question(s), and specified purpose that the NAM is intended to address, and the context in which it will be used. Assessment of the biological relevance of the NAM should focus on its alignment with human biology, mechanistic understanding, and ability to provide information that leads to health protective decisions, rather than solely comparing NAM-based chemical testing results with those from traditional animal test methods. However, when NAM results are compared to historical animal test results, the variability observed within animal test method results should be used to inform performance benchmarks. Building on previous efforts, this paper proposes a framework comprising five essential elements to establish scientific confidence in NAMs for regulatory use: fitness for purpose, human biological relevance, technical characterization, data integrity and transparency, and independent review. Universal uptake of this framework would facilitate the timely development and use of NAMs by the international community. While this paper focuses on NAMs for assessing human health effects of pesticides and industrial chemicals, many of the suggested elements are expected to apply to other types of chemicals and to ecotoxicological effect assessments.

Using alternative test methods to predict endocrine disruption and reproductive adverse outcomes: do we have enough knowledge?

Endocrine disrupting chemicals (EDCs) are a matter of great concern. They are ubiquitous in the environment, are considered harmful to humans and wildlife, yet remain challenging to identify based on current international test guidelines and regulatory frameworks. For a compound to be identified as an EDC within the EU regulatory system, a plausible link between an endocrine mode-of-action and an adverse effect outcome in an intact organism must be established. This requires in-depth knowledge about molecular pathways regulating normal development and function in animals and humans in order to elucidate causes for disease. Although our knowledge about the role of the endocrine system in animal development and function is substantial, it remains challenging to predict endocrine-related disease outcomes in intact animals based on non-animal test data. A main reason for this is that our knowledge about mechanism-of-action are still lacking for essential causal components, coupled with the sizeable challenge of mimicking the complex multi-organ endocrine system by methodological reductionism. Herein, we highlight this challenge by drawing examples from male reproductive toxicity, which is an area that has been at the forefront of EDC research since its inception. We discuss the importance of increased focus on characterizing mechanism-of-action for EDC-induced adverse health effects. This is so we can design more robust and reliable testing strategies using non-animal test methods for predictive toxicology; both to improve chemical risk assessment in general, but also to allow for considerable reduction and replacement of animal experiments in chemicals testing of the 21st Century.

Predicting the binding of small molecules to nuclear receptors using machine learning

Nuclear receptors (NRs) are important biological targets of endocrine-disrupting chemicals (EDCs). Identifying chemicals that can act as EDCs and modulate the function of NRs is difficult because of the time and cost of in vitro and in vivo screening to determine the potential hazards of the 100 000s of chemicals that humans are exposed to. Hence, there is a need for computational approaches to prioritize chemicals for biological testing. Machine learning (ML) techniques are alternative methods that can quickly screen millions of chemicals and identify those that may be an EDC. Computational models of chemical binding to multiple NRs have begun to emerge. Recently, a Nuclear Receptor Activity (NuRA) dataset, describing experimentally derived small-molecule activity against various NRs has been created. We have used the NuRA dataset to develop an ensemble of ML-based models to predict the agonism, antagonism, binding and effector binding of small molecules to nine different human NRs. We defined the applicability domain of the ML models as a measure of Tanimoto similarity to the molecules in the training set, which enhanced the performance of the developed classifiers. We further developed a user-friendly web server named 'NR-ToxPred' to predict the binding of chemicals to the nine NRs using the best-performing models for each receptor. This web server is freely accessible at http://nr-toxpred.cchem.berkeley.edu. Users can upload individual chemicals using Simplified Molecular-Input Line-Entry System, CAS numbers or sketch the molecule in the provided space to predict the compound's activity against the different NRs and predict the binding mode for each.

Rethinking chronic toxicity and carcinogenicity assessment for agrochemicals project (ReCAAP): A reporting framework to support a weight of evidence safety assessment without long-term rodent bioassays

Rodent cancer bioassays have been long-required studies for regulatory assessment of human cancer hazard and risk. These studies use hundreds of animals, are resource intensive, and certain aspects of these studies have limited human relevance. The past 10 years have seen an exponential growth of new technologies with the potential to effectively evaluate human cancer hazard and risk while reducing, refining, or replacing animal use. To streamline and facilitate uptake of new technologies, a workgroup comprised of scientists from government, academia, non-governmental organizations, and industry stakeholders developed a framework for waiver rationales of rodent cancer bioassays for consideration in agrochemical safety assessment. The workgroup used an iterative approach, incorporating regulatory agency feedback, and identifying critical information to be considered in a risk assessment-based weight of evidence determination of the need for rodent cancer bioassays. The reporting framework described herein was developed to support a chronic toxicity and carcinogenicity study waiver rationale, which includes information on use pattern(s), exposure scenario(s), pesticidal mode-of-action, physicochemical properties, metabolism, toxicokinetics, toxicological data including mechanistic data, and chemical read-across from similar registered pesticides. The framework could also be applied to endpoints other than chronic toxicity and carcinogenicity, and for chemicals other than agrochemicals.

A Fluorometric CYP19A1 (Aromatase) Activity Assay in Live Cells

Inhibition of estrogen synthesis is an integral component of the frontline pharmacologic therapy for the treatment of estrogen receptor positive cancers. However, there is currently no direct, high-throughput-ready assay for aromatase (also known as CYP19A1) that can be performed in live cells. Herein we present a cell-based assay that allows for multiplexed assessment of enzyme activity, protein half-life, cell viability, and identification of inhibitors with slow off-rates.

A review of species differences in the control of, and response to, chemical-induced thyroid hormone perturbations leading to thyroid cancer

This review summarises the current state of knowledge regarding the physiology and control of production of thyroid hormones, the effects of chemicals in perturbing their synthesis and release that result in thyroid cancer. It does not consider the potential neurodevelopmental consequences of low thyroid hormones. There are a number of known molecular initiating events (MIEs) that affect thyroid hormone synthesis in mammals and many chemicals are able to activate multiple MIEs simultaneously. AOP analysis of chemical-induced thyroid cancer in rodents has defined the key events that predispose to the development of rodent cancer and many of these will operate in humans under appropriate conditions, if they were exposed to high enough concentrations of the affecting chemicals. There are conditions however that, at the very least, would indicate significant quantitative differences in the sensitivity of humans to these effects, with rodents being considerably more sensitive to thyroid effects by virtue of differences in the biology, transport and control of thyroid hormones in these species as opposed to humans where turnover is appreciably lower and where serum transport of T4/T3 is different to that operating in rodents. There is heated debate around claimed qualitative differences between the rodent and human thyroid physiology, and significant reservations, both scientific and regulatory, still exist in terms of the potential neurodevelopmental consequences of low thyroid hormone levels at critical windows of time. In contrast, the situation for the chemical induction of thyroid cancer, through effects on thyroid hormone production and release, is less ambiguous with both theoretical, and actual data, showing clear dose-related thresholds for the key events predisposing to chemically induced thyroid cancer in rodents. In addition, qualitative differences in transport, and quantitative differences in half life, catabolism and turnover of thyroid hormones, exist that would not operate under normal situations in humans.

Towards a systematic use of effect biomarkers in population and occupational biomonitoring

Effect biomarkers can be used to elucidate relationships between exposure to environmental chemicals and their mixtures with associated health outcomes, but they are often underused, as underlying biological mechanisms are not understood. We aim to provide an overview of available effect biomarkers for monitoring chemical exposures in the general and occupational populations, and highlight their potential in monitoring humans exposed to chemical mixtures. We also discuss the role of the adverse outcome pathway (AOP) framework and physiologically based kinetic and dynamic (PBK/D) modelling to strengthen the understanding of the biological mechanism of effect biomarkers, and in particular for use in regulatory risk assessments. An interdisciplinary network of experts from the European chapter of the International Society for Exposure Science (ISES Europe) and the Organization for Economic Co-operation and Development (OECD) Occupational Biomonitoring activity of Working Parties of Hazard and Exposure Assessment group worked together to map the conventional framework of biomarkers and provided recommendations for their systematic use. We summarized the key aspects of this work here, and discussed these in three parts. Part I, we inventory available effect biomarkers and promising new biomarkers for the general population based on the H2020 Human Biomonitoring for Europe (HBM4EU) initiative. Part II, we provide an overview AOP and PBK/D modelling use that improved the selection and interpretation of effect biomarkers. Part III, we describe the collected expertise from the OECD Occupational Biomonitoring subtask effect biomarkers in prioritizing relevant mode of actions (MoAs) and suitable effect biomarkers. Furthermore, we propose a tiered risk assessment approach for occupational biomonitoring. Several effect biomarkers, especially for use in occupational settings, are validated. They offer a direct assessment of the overall health risks associated with exposure to chemicals, chemical mixtures and their transformation products. Promising novel effect biomarkers are emerging for biomonitoring of the general population. Efforts are being dedicated to prioritizing molecular and biochemical effect biomarkers that can provide a causal link in exposure-health outcome associations. This mechanistic approach has great potential in improving human health risk assessment. New techniques such as in silico methods (e.g. QSAR, PBK/D modelling) as well as 'omics data will aid this process. Our multidisciplinary review represents a starting point for enhancing the identification of effect biomarkers and their mechanistic pathways following the AOP framework. This may help in prioritizing the effect biomarker implementation as well as defining threshold limits for chemical mixtures in a more structured way. Several ex vivo biomarkers have been proposed to evaluate combined effects including genotoxicity and xeno-estrogenicity. There is a regulatory need to derive effect-based trigger values using the increasing mechanistic knowledge coming from the AOP framework to address adverse health effects due to exposure to chemical mixtures. Such a mechanistic strategy would reduce the fragmentation observed in different regulations. It could also stimulate a harmonized use of effect biomarkers in a more comparable way, in particular for risk assessments to chemical mixtures.

Molecular interactions of vinclozolin metabolites with human estrogen receptors 1GWR-α and 1QKM and androgen receptor 2AM9-β: Implication for endocrine disruption

Background: Vinclozolin (VCZ) is a widely used antifungal agent with capability to enter into the human food chain. VCZ metabolizes into seven metabolites M1-M7. Several studies have shown its effects on reprotoxicity. However, there is limited information available on the interaction of VCZ metabolites with nuclear receptors. In silico studies aimed at identifying interaction of endocrine disruptor with nuclear receptors serve a prescreening framework in risk assessment.Methods: We studied interactive potential of VCZ and its metabolites with human estrogen (ER) and androgen receptor (AR) using molecular docking method. Binding potential of VCZ and its metabolites with estrogen receptors 1GWR-α, 1QKM and androgen receptor 2AM9-β was checked by using Schrodinger Maestro 10.5. Estradiol (E2), a natural ligand of ER and AR was taken as a reference.Results: VCZ and its metabolites showed higher or similar binding efficiency on interaction with target proteins when compared with E2. VCZ and its metabolites also exhibited agonistic effect against 1GWR-α, 1QKM and 2AM9-β with strong binding potential to them.Conclusion: Some VCZ metabolites such as M4 and M5 showed higher binding potencies with 1GWR-α, 1QKM and 2AM9-β than E2. Toxicity data of VCZ is well endowed. However, endocrine disrupting potential of VCZ via nuclear receptor mediated pathway is less understood. This in silico study revealing that not only VCZ but its metabolites have potential to interact with 1GWR-α, 1QKM and 2AM9-β offers a platform for further exploration of VCZ in this direction.

Mechanisms of action of agrochemicals acting as endocrine disrupting chemicals

Agrochemicals represent a significant class of endocrine disrupting chemicals that humans and animals around the world are exposed to constantly. Agrochemicals can act as endocrine disrupting chemicals through a variety of mechanisms. Recent studies have shown that several mechanisms of action involve the ability of agrochemicals to mimic the interaction of endogenous hormones with nuclear receptors such as estrogen receptors, androgen receptors, peroxisome proliferator activated receptors, the aryl hydrocarbon receptor, and thyroid hormone receptors. Further, studies indicate that agrochemicals can exert toxicity through non-nuclear receptor-mediated mechanisms of action. Such non-genomic mechanisms of action include interference with peptide, steroid, or amino acid hormone response, synthesis and degradation as well as epigenetic changes (DNA methylation and histone modifications). This review summarizes the major mechanisms of action by which agrochemicals target the endocrine system.

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.

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.