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

Urban Endocrine Disruptors Targeting Breast Cancer Proteins

Humans are exposed to a huge amount of environmental pollutants called endocrine disrupting chemicals (EDCs). These molecules interfere with the homeostasis of the body, usually through mimicking natural hormones leading to activation or blocking of their receptors. Many of these compounds have been associated with a broad range of diseases including the development or increased susceptibility to breast cancer, the most prevalent cancer in women worldwide, according to the World Health Organization. Thus, this article presents a virtual high-throughput screening (vHTS) to evaluate the affinity of proteins related to breast cancer, such as ESR1, ERBB2, PGR, BCRA1, and SHBG, among others, with EDCs from urban sources. A blind docking strategy was employed to screen each protein-ligand pair in triplicate in AutoDock Vina 2.0, using the computed binding affinities as ranking criteria. The three-dimensional structures were previously obtained from EDCs DataBank and Protein Data Bank, prepared and optimized by SYBYL X-2.0. Some of the chemicals that exhibited the best affinity scores for breast cancer proteins in each category were 1,3,7,8-tetrachlorodibenzo-p-dioxin, bisphenol A derivatives, perfluorooctanesulfonic acid, and benzo(a)pyrene, for catalase, several proteins, sex hormone-binding globulin, and cytochrome P450 1A2, respectively. An experimental validation of this approach was performed with a complex that gave a moderate binding affinity in silico, the sex hormone binding globulin (SHBG), and bisphenol A (BPA) complex. The protein was obtained using DNA recombinant technology and the physical interaction with BPA assessed through spectroscopic techniques. BPA binds on the recombinant SHBG, and this results in an increase of its α helix content. In short, this work shows the potential of several EDCs to bind breast cancer associated proteins as a tool to prioritize compounds to perform in vitro analysis to benefit the regulation or exposure prevention by the general population.

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Determination of four paraben-type preservatives and three benzophenone-type ultraviolet light filters in seafoods by LC-QqLIT-MS/MS

For the first time, an efficient and sensitive analytical method based on liquid chromatography-quadrupole linear ion trap-tandem mass spectrometry (LC-QqLIT-MS/MS) was developed for the simultaneous determination of four paraben-type preservatives and three benzophenone-type ultraviolet light filters in both plant (Sargassum fusiforme, porphyra, kelp) and animal (hairtail, yellow croaker, shrimp) seafood. The samples were extracted in methanol by pressurized liquid extraction (PLE), and the extracts were then cleaned up by mixed-mode cationic exchange (MCX) solid-phase extraction cartridges. Both isotope-labeled internal standards and matrix-matched calibration standards were used to alleviate and correct for the matrix effects, and the limits of quantification (LOQs) were 10.0μg kg(-1) for all target compounds. The average recoveries were in the range of 80.6-107.8% at three spiked concentration levels (10, 50 and 100μgkg(-1)) with relative standard deviations (RSDs) below 8.5%. The results suggest that very limited contamination of these seven emerging contaminants, mainly associated with PCPs, occurred in these common seafoods.

Induced Pluripotent Stem Cell Models to Enable In Vitro Models for Screening in the Central Nervous System

There is great need to develop more predictive drug discovery tools to identify new therapies to treat diseases of the central nervous system (CNS). Current nonpluripotent stem cell-based models often utilize non-CNS immortalized cell lines and do not enable the development of personalized models of disease. In this review, we discuss why in vitro models are necessary for translational research and outline the unique advantages of induced pluripotent stem cell (iPSC)-based models over those of current systems. We suggest that iPSC-based models can be patient specific and isogenic lines can be differentiated into many neural cell types for detailed comparisons. iPSC-derived cells can be combined to form small organoids, or large panels of lines can be developed that enable new forms of analysis. iPSC and embryonic stem cell-derived cells can be readily engineered to develop reporters for lineage studies or mechanism of action experiments further extending the utility of iPSC-based systems. We conclude by describing novel technologies that include strategies for the development of diversity panels, novel genomic engineering tools, new three-dimensional organoid systems, and modified high-content screens that may bring toxicology into the 21st century. The strategic integration of these technologies with the advantages of iPSC-derived cell technology, we believe, will be a paradigm shift for toxicology and drug discovery efforts.

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

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

Comparative endpoint sensitivity of in vitro estrogen agonist assays

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

Occupational exposure to endocrine disruptors and lymphoma risk in a multi-centric European study

BACKGROUND

Incidence rates of lymphoma are usually higher in men than in women, and oestrogens may protect against lymphoma.

METHODS

We evaluated occupational exposure to endocrine disrupting chemicals (EDCs) among 2457 controls and 2178 incident lymphoma cases and subtypes from the European Epilymph study.

RESULTS

Over 30 years of exposure to EDCs compared to no exposure was associated with a 24% increased risk of mature B-cell neoplasms (P-trend=0.02). Associations were observed among men, but not women.

CONCLUSIONS

Prolonged occupational exposure to endocrine disruptors seems to be moderately associated with some lymphoma subtypes.

Elucidating mechanisms of toxicity using phenotypic data from primary human cell systems--a chemical biology approach for thrombosis-related side effects

Here we describe a chemical biology approach for elucidating potential toxicity mechanisms for thrombosis-related side effects. This work takes advantage of a large chemical biology data set comprising the effects of known, well-characterized reference agents on the cell surface levels of tissue factor (TF) in a primary human endothelial cell-based model of vascular inflammation, the BioMAP^® 3C system. In previous work with the Environmental Protection Agency (EPA) for the ToxCast™ program, aryl hydrocarbon receptor (AhR) agonists and estrogen receptor (ER) antagonists were found to share an usual activity, that of increasing TF levels in this system. Since human exposure to compounds in both chemical classes is associated with increased incidence of thrombosis-related side effects, we expanded this analysis with a large number of well-characterized reference compounds in order to better understand the underlying mechanisms. As a result, mechanisms for increasing (AhR, histamine H1 receptor, histone deacetylase or HDAC, hsp90, nuclear factor kappa B or NFκB, MEK, oncostatin M receptor, Jak kinase, and p38 MAPK) and decreasing (vacuolar ATPase or V-ATPase) and mTOR) TF expression levels were uncovered. These data identify the nutrient, lipid, bacterial, and hypoxia sensing functions of autophagy as potential key regulatory points controlling cell surface TF levels in endothelial cells and support the mechanistic hypothesis that these functions are associated with thrombosis-related side effects in vivo.