Identifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform

Occurrence and Prioritization of Human Androgen Receptor Disruptors in Sewage Sludges Across China

The global practice of reusing sewage sludge in agriculture and its landfill disposal reintroduces environmental contaminants, posing risks to human and ecological health. This study screened sewage sludge from 30 Chinese cities for androgen receptor (AR) disruptors, utilizing a disruptor list from the Toxicology in the 21st Century program (Tox21), and identified 25 agonists and 33 antagonists across diverse use categories. Predominantly, natural products 5α-dihydrotestosterone and thymidine emerged as agonists, whereas the industrial intermediate caprolactam was the principal antagonist. In-house bioassays for identified disruptors displayed good alignment with Tox21 potency data, validating employing Tox21 toxicity data for theoretical toxicity estimations. Potency calculations revealed 5α-dihydrotestosterone and two pharmaceuticals (17β-trenbolone and testosterone isocaproate) as the most potent AR agonists and three dyes (rhodamine 6G, Victoria blue BO, and gentian violet) as antagonists. Theoretical effect contribution evaluations prioritized 5α-dihydrotestosterone and testosterone isocaproate as high-risk AR agonists and caprolactam, rhodamine 6G, and 8-hydroxyquinoline (as a biocide and a preservative) as key antagonists. Notably, 16 agonists and 20 antagonists were newly reported in the sludge, many exhibiting significant detection frequencies, concentrations, and/or toxicities, demanding future scrutiny. Our study presents an efficient strategy for estimating environmental sample toxicity and identifying key toxicants, thereby supporting the development of appropriate sludge management strategies.

Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction

Environmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health.

Effect-Based Trigger Values Are Essential for the Uptake of Effect-Based Methods in Water Safety Planning

Effect-based methods (EBMs) using in vitro bioassays and well plate-based in vivo assays are recommended for water quality monitoring because they can capture the mixture effects of the many chemicals present in water. Many in vitro bioassays are highly sensitive, so an effect in a bioassay does not necessarily indicate poor chemical water quality. Consequently, effect-based trigger values (EBTs) have been introduced to differentiate between acceptable and unacceptable chemical water quality and are required for the wider acceptance of EBMs by the water sector and regulatory bodies. These EBTs have been derived for both drinking water and surface water to protect human and ecological health, respectively, and are available for assays indicative of specific receptor-mediated effects, as well as assays indicative of adaptive stress responses, apical effects, and receptor-mediated effects triggered by many chemicals. An overview of currently available EBTs is provided, and a simple approach is proposed to predict interim EBTs for assays currently without an EBT based on the effect concentration of the assay reference compound. There was good agreement between EBTs predicted using this simplistic approach and EBTs from the literature derived using more robust methods. Finally, an interpretation framework that outlines the steps to take if the effect of a sample exceeds the EBT was developed to help facilitate the uptake of EBMs in routine water quality monitoring and water safety planning for drinking water production. Environ Toxicol Chem 2023;42:714-726. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Screening Method for the Identification of Compounds That Activate Pregnane X Receptor

The pregnane X receptor (PXR) is a nuclear receptor found mainly in the liver and intestine, whose main function is to regulate the expression of drug-metabolizing enzymes and transporters. Recently, it has been noted that PXR plays critical roles in energy homeostasis, immune response, and cancer. Therefore, identifying chemicals or compounds that can modulate PXR is of great interest, as these can result in downstream toxicity or, alternatively, may have therapeutic potential. Testing one compound at a time for PXR activity would be inefficient and take thousands of hours for large compound libraries. Here, we describe a high-throughput screening method that encompasses plating and treating HepG2-CYP3A4-hPXR cells in a 1536-well plate, as well as reading and interpreting assay (e.g., luciferase reporter gene activity) endpoints. These cells are stably transfected with a human PXR expression vector and CYP3A4-promoter-driven luciferase reporter vector, allowing the identification of compounds that activate PXR through cytochrome 450 3A4. We also describe how to analyze the data from each assay and explain follow-up steps, namely pharmacological characterization and quantitative polymerase chain reaction (qPCR) assays, which can be performed to confirm results from the original screen. These methods can be used to identify and confirm hPXR activators after completion of a compound screening. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of a high-throughput assay to identify hPXR activators Basic Protocol 2: Quantitative high-throughput screening a compound library to classify hPXR activators Basic Protocol 3: Performing pharmacological characterization and qPCR assays to confirm hPXR activators.

Use of Tox21 Screening Data to Evaluate the COVID-19 Drug Candidates for Their Potential Toxic Effects and Related Pathways

Currently, various potential therapeutic agents for coronavirus disease-2019 (COVID-19), a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are being investigated worldwide mainly through the drug repurposing approach. Several anti-viral, anti-bacterial, anti-malarial, and anti-inflammatory drugs were employed in randomized trials and observational studies for developing new therapeutics for COVID-19. Although an increasing number of repurposed drugs have shown anti-SARS-CoV-2 activities in vitro, so far only remdesivir has been approved by the US FDA to treat COVID-19, and several other drugs approved for Emergency Use Authorization, including sotrovimab, tocilizumab, baricitinib, paxlovid, molnupiravir, and other potential strategies to develop safe and effective therapeutics for SARS-CoV-2 infection are still underway. Many drugs employed as anti-viral may exert unwanted side effects (i.e., toxicity) via unknown mechanisms. To quickly assess these drugs for their potential toxicological effects and mechanisms, we used the Tox21 in vitro assay datasets generated from screening ∼10,000 compounds consisting of approved drugs and environmental chemicals against multiple cellular targets and pathways. Here we summarize the toxicological profiles of small molecule drugs that are currently under clinical trials for the treatment of COVID-19 based on their in vitro activities against various targets and cellular signaling pathways.

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.

Endocrine disruptors of sex hormone activities

Sex hormones, such as androgens, estrogens and progestins are naturally occurring compounds that tightly regulate endocrine systems in a variety of living organisms. Uncontrolled environmental exposure to these hormones or their biological and synthetic mimetics has been widely documented. Furthermore, water contaminants penetrate soil to affect flora, fauna and ultimately humans. Because endocrine systems evolved to respond to very small changes in hormone levels, the low levels found in the environment cannot be ignored. The combined actions of sex hormones with glucocorticoids and other nuclear receptors disruptors creates additional level of complexity including the newly described "dynamic assisted loading" mechanism. We reviewed the extensive literature pertaining to world-wide detection of these disruptors and created a detailed Table on the development and current status of methods used for their analysis.

In vivo and in silico analyses of estrogenic potential of equine estrogens in medaka (Oryzias latipes)

Equine estrogens (EEs) are widely used in hormone replacement therapy pharmaceuticals for postmenopausal women. Previous studies have shown that EEs occur in the aquatic environment; however, the potential estrogenicity and risk of EEs in aquatic organisms, including fish, have yet to be studied in detail. Therefore, we evaluated the estrogenic potential of major EEs, namely equilin (Eq), 17α-dihydroequilin (17α-Eq), 17β-dihydroequilin (17β-Eq), equilenin (Eqn), 17α-dihydroequilenin (17α-Eqn), and 17β-dihydroequilenin (17β-Eqn), on medaka (Oryzias latipes) using in vivo and in silico assays. Quantitative real-time RT-PCR analyses revealed that expression levels of choriogenin L (ChgL) and choriogenin H (ChgH) in medaka embryos responded to various types and concentrations of EEs in a concentration-dependent manner, whereas transcription levels of vitellogenin 1 were not significantly affected by any of the EEs in the concentration range tested. The order of the in vivo estrogenic potencies of EEs was as follows: 17β-Eq > Eq > 17β-Eqn > Eqn > 17α-Eqn > 17α-Eq. Additionally, the 50% effective concentrations (EC₅₀) of 17β-Eq was lower than that of 17β-estradiol. We also investigated the interaction potential of EEs with medaka estrogen receptor (ER) subtypes in silico using a three-dimensional model of the ligand-binding domain (LBD) for each ER and docking simulations. All six EEs were found to interact with the LBDs of ERα, ERβ1, and ERβ2. The order of the in silico interaction potentials of EEs with each ER LBD was as follows: 17β-Eq > 17α-Eq > Eq > 17β-Eqn > 17α-Eqn > Eqn. Furthermore, we identified the key amino acids that interact with EEs in each ER LBD; our findings suggest that amino acids and/or their hydrogen bonding may be responsible for the ligand-specific interactions with each ER. This study is the first to comprehensively analyze the estrogenic potential of EEs in medaka both in vivo and in silico.

Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions

Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.

Receptor-based in vitro activities to assess human exposure to chemical mixtures and related health impacts

Humans are exposed to a large number of chemicals from sources such as the environment, food, and consumer products. There is growing concern that human exposure to chemical mixtures, especially during critical periods of development, increases the risk of adverse health effects in newborns or later in life. Historically, the one-chemical-at-a-time approach has been applied both for exposure assessment and hazard characterisation, leading to insufficient knowledge about human health effects caused by exposure to mixtures of chemicals that have the same target. To circumvent this challenge researchers can apply in vitro assays to analyse both exposure to and human health effects of chemical mixtures in biological samples. The advantages of using in vitro assays are: (i) that an integrated effect is measured, taking combined mixture effects into account and (ii) that in vitro assays can reduce complexity in identification of Chemicals of Emerging Concern (CECs) in human tissues. We have reviewed the state-of-the-art on the use of receptor-based in vitro assays to assess human exposure to chemical mixtures and related health impacts. A total of 43 studies were identified, in which endpoints for the arylhydrocarbon receptor (AhR), the estrogen receptor (ER), and the androgen receptor (AR) were used. The majority of studies reported biological activities that could be associated with breast cancer incidence, male reproductive health effects, developmental toxicities, human demographic characteristics or lifestyle factors such as dietary patterns. A few studies used the bioactivities to check the coverage of the chemical analyses of the human samples, whereas in vitro assays have so far not regularly been used for identifying CECs in human samples, but rather in environmental matrices or food packaging materials. A huge field of novel applications using receptor-based in vitro assays for mixture toxicity assessment on human samples and effect-directed analysis (EDA) using high resolution mass spectrometry (HRMS) for identification of toxic compounds waits for exploration. In the future this could lead to a paradigm shift in the way we unravel adverse human health effects caused by chemical mixtures.

Examining the evidence of non-monotonic dose-response in Androgen Receptor agonism high-throughput screening assay

Modern High-Throughput Screening (HTS) techniques allow to determine in vitro bioactivity of tens of thousands of chemicals within a relatively short period of time and tested compounds are usually interpreted as either active or inactive. The interpretation is mostly based on the assumption of monotonic dose-response. This approach ignores potential abnormal dose-response relationships, such as non-monotonic dose-response (NMDR). NMDR presents a serious challenge to toxicologists and pharmacologists, since they undermine the usefulness of such concepts as lowest-observed-adverse-effect level (LOAEL) and no-observed-adverse-effect level (NOAEL). The possible presence of the NMDR in Androgen receptor (AR) agonism was examined for a structurally diverse set of chemicals (~8 300 unique compounds) from Tox21 project library. The source of activity data is Tox21 AR agonism luciferase-based HTS on the MDA-MB-453 cell line. The examination of curve fitting for 35,328 dose-response data entries was based on modified version of existing criteria for determination of NMDR. The bias that arises from compounds' cytotoxicity and interference with firefly luciferase protein was also studied. The examination has shown evidence of NMDR for several compounds, including known AR antagonists (e. g. Cyproterone acetate) and other known endocrine disruptors (e. g. Tranilast). Compounds were divided into 3 groups based on chemical class, known biological activity profile and the shape of dose-response curve. The challenges of using HTS data to determine NMDR and benefits of this analysis are discussed.

2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor

BACKGROUND

Abnormal activation of human nuclear hormone receptors disrupts endocrine systems and thereby affects human health. There have been machine learning-based models to predict androgen receptor agonist activity. However, the models were constructed based on limited numerical features such as molecular descriptors and fingerprints.

RESULT

In this study, instead of the numerical features, 2-D chemical structure images of compounds were used to build an androgen receptor toxicity prediction model. The images may provide unknown features that were not represented by conventional numerical features. As a result, the new strategy resulted in a construction of highly accurate prediction model: Mathews correlation coefficient (MCC) of 0.688, positive predictive value (PPV) of 0.933, sensitivity of 0.519, specificity of 0.998, and overall accuracy of 0.981 in 10-fold cross-validation. Validation on a test dataset showed MCC of 0.370, sensitivity of 0.211, specificity of 0.991, PPV of 0.882, and overall accuracy of 0.801. Our chemical image-based prediction model outperforms conventional models based on numerical features.

CONCLUSION

Our constructed prediction model successfully classified molecular images into androgen receptor agonists or inactive compounds. The result indicates that 2-D molecular mimetic diagram would be used as another feature to construct molecular activity prediction models.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

High-throughput screen identifies 5-HT receptor as a modulator of AR and a therapeutic target for prostate cancer

BACKGROUND

Eradication of persistent androgen receptor (AR) activity in castration-resistant prostate cancer may be a promising strategy to overcome castration resistance. We aimed to identify novel compounds that inhibit AR activity and could be potential therapeutic agents for prostate cancer.

METHODS

A high-throughput screening system involving cell lines stably expressing AR protein and AR-responsive luciferase was employed for the 1260 compound library. Molecular and antitumor effects on candidate pathways that interacted with AR signaling were examined in prostate cancer cells expressing AR.

RESULTS

The high-throughput screening identified various potential compounds that interfered with AR signaling through known and novel pathways. Among them, a 5-hydroxytryptamine 5A (5-HT5A) receptor antagonist suppressed AR activity through protein kinase A signaling, which was confirmed by 5-HT5A receptor knockdown. Consistently, 5-HT5A receptor inhibitors showed cytotoxic effects toward prostate cancer cells.

CONCLUSIONS

Taken together, this study identifies 5-HT5A receptor as a promising therapeutic target for prostate cancer via its interaction with AR signaling.

Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals

Cell-to-cell variation of protein expression in genetically homogeneous populations is a common biological trait often neglected during analysis of high-throughput (HT) screens and is rarely used as a metric to characterize chemicals. We have captured single-cell distributions of androgen receptor (AR) nuclear levels after perturbations as a means to evaluate assay reproducibility and characterize a small subset of chemicals. AR, a member of the nuclear receptor family of transcription factors, is the central regulator of male reproduction and is involved in many pathophysiological processes. AR protein levels and nuclear localization often increase following ligand binding, with dihydrotestosterone (DHT) being the natural agonist. HT AR immunofluorescence imaging was used in multiple cell lines to define single-cell nuclear values extracted from thousands of cells per condition treated with DHT or DMSO (control). Analysis of numerous biological replicates led to a quality control metric that takes into account the distribution of single-cell data, and how it changes upon treatments. Dose-response experiments across several cell lines showed a large range of sensitivity to DHT, prompting us to treat selected cell lines with 45 Environmental Protection Agency (EPA)-provided chemicals that include many endocrine-disrupting chemicals (EDCs); data from six of the compounds were then integrated with orthogonal assays. Our comprehensive results indicate that quantitative single-cell distribution analysis of AR protein levels is a valid method to detect the potential androgenic and antiandrogenic actions of environmentally relevant chemicals in a sensitive and reproducible manner.

Identification of Androgen Receptor Modulators in a Prostate Cancer Cell Line Microarray Compendium

High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to identify molecular targets and provide mechanistic context for regulatory testing. Here, we describe the development and validation of a novel gene expression biomarker to identify androgen receptor (AR)-modulating chemicals using a pattern matching method. Androgen receptor biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and 4 AR antagonists and included only those genes that were regulated by AR. The 51 gene biomarker was evaluated as a predictive tool using the fold-change, rank-based Running Fisher algorithm. Using 158 comparisons from cells treated with 95 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker correctly classified 16 out of the 17 AR reference antagonists including those that are "weak" and "very weak". Predictions based on microarray profiles from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared with those from an AR pathway model which used 11 in vitro HT assays. The balanced accuracy for suppression was 93%. Using our approach, we identified conditions in which AR was modulated in a large collection of microarray profiles from prostate cancer cell lines including (1) constitutively active mutants or knockdown of AR, (2) decreases in availability of androgens by castration or removal from media, and (3) exposure to chemical modulators that work through indirect mechanisms including suppression of AR expression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators.

Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures

The Toxicology in the 21st Century (Tox21) project seeks to develop and test methods for high-throughput examination of the effect certain chemical compounds have on biological systems. Although primary and toxicity assay data were readily available for multiple reporter gene modified cell lines, extensive annotation and curation was required to improve these datasets with respect to how FAIR (Findable, Accessible, Interoperable, and Reusable) they are. In this study, we fully annotated the Tox21 published data with relevant and accepted controlled vocabularies. After removing unreliable data points, we aggregated the results and created three sets of signatures reflecting activity in the reporter gene assays, cytotoxicity, and selective reporter gene activity, respectively. We benchmarked these signatures using the chemical structures of the tested compounds and obtained generally high receiver operating characteristic (ROC) scores, suggesting good quality and utility of these signatures and the underlying data. We analyzed the results to identify promiscuous individual compounds and chemotypes for the three signature categories and interpreted the results to illustrate the utility and re-usability of the datasets. With this study, we aimed to demonstrate the importance of data standards in reporting screening results and high-quality annotations to enable re-use and interpretation of these data. To improve the data with respect to all FAIR criteria, all assay annotations, cleaned and aggregate datasets, and signatures were made available as standardized dataset packages (Aggregated Tox21 bioactivity data, 2019).

Identification of Modulators That Activate the Constitutive Androstane Receptor From the Tox21 10K Compound Library

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor involved in all phases of drug metabolism and disposition. However, recently it's been implicated in energy metabolism, tumor progression, and cancer therapy as well. It is, therefore, important to identify compounds that induce human CAR (hCAR) activation to predict drug-drug interactions and potential therapeutic usage. In this study, we screen the Tox21 10,000 compound collection to characterize hCAR activators. A potential novel structural cluster of compounds was identified, which included nitazoxanide and tenonitrozole, whereas known structural clusters, such as flavones and prazoles, were also detected. Four compounds, neticonazole, diphenamid, phenothrin, and rimcazole, have been identified as novel hCAR activators, one of which, rimcazole, shows potential selectivity toward hCAR over its sister receptor, the pregnane X receptor (PXR). All 4 compounds translocated hCAR from the cytoplasm into the nucleus demonstrating the first step to CAR activation. Profiling these compounds as hCAR activators would enable an estimation of drug-drug interactions, as well as identify prospective therapeutically beneficial drugs.

Potential Health Risk of Endocrine Disruptors in Construction Sector and Plastics Industry: A New Paradigm in Occupational Health

Endocrine disruptors (EDs) belong to large and diverse groups of agents that may cause multiple biological effects associated with, for example, hormone imbalance and infertility, chronic diseases such as diabetes, genome damage and cancer. The health risks related with the exposure to EDs are typically underestimated, less well characterized, and not regulated to the same extent as, for example, carcinogens. The increased production and utilization of identified or suspected EDs in many different technological processes raises new challenges with respect to occupational exposure settings and associated health risks. Due to the specific profile of health risk, occupational exposure to EDs demands a new paradigm in health risk assessment, redefinition of exposure assessment, new effects biomarkers for occupational health surveillance and definition of limit values. The construction and plastics industries are among the strongest economic sectors, employing millions of workers globally. They also use large quantities of chemicals that are known or suspected EDs. Focusing on these two industries, this short communication discusses: (a) why occupational exposure to EDs needs a more specific approach to occupational health risk assessments, (b) identifies the current knowledge gaps, and (c) identifies and gives a rationale for a future occupational health paradigm, which will include ED biomarkers as a relevant parameter in occupational health risk assessment, surveillance and exposure prevention.

Development and Validation of a Computational Model for Androgen Receptor Activity

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.