Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals

Increased Endocrine Activity of Xenobiotic Chemicals as Mediated by Metabolic Activation

The US Environmental Protection Agency (USEPA) is faced with long lists of chemicals that require hazard assessment. The present study is part of a larger effort to develop in vitro assays and quantitative structure-activity relationships applicable to untested chemicals on USEPA inventories through study of estrogen receptor (ER) binding and estrogen-mediated gene expression in fish. The present effort investigates metabolic activation of chemicals resulting in increased estrogenicity. Phenolphthalin (PLIN) was shown not to bind rainbow trout (Oncorhynchus mykiss) ER (rtER) in a competitive binding assay, but vitellogenin (Vtg) expression was induced in trout liver slices exposed to 10-4 and 10-3.7 M PLIN. Phenolphthalein (PLEIN), a metabolite of PLIN, was subsequently determined to be formed when slices were exposed to PLIN. It binds rtER with a relative binding affinity to 17β-estradiol of 0.020%. Slices exposed to PLEIN expressed Vtg messenger RNA (mRNA) at 10-4.3 , 10-4 , and 10-3.7 M, with no detectable PLIN present. Thus, Vtg expression noted in PLIN slice exposures was explained by metabolism to PLEIN in trout liver slices. A second model chemical, 4,4'-methylenedianiline (MDA), was not shown to bind rtER but did induce Vtg mRNA production in tissue slices at 10-4.3 , 10-4 , and 10-3.7 M in amounts nearly equal to reference estradiol induction, thus indicating metabolic activation of MDA. A series of experiments were performed to identify a potential metabolite responsible for the observed increase in activity. Potential metabolites hydroxylamine-MDA, nitroso-MDA, azo-MDA, and azoxy-MDA were not observed. However, acetylated MDA was observed and tested in both ER-binding and tissue slice Vtg induction assays. Environ Toxicol Chem 2023;42:2747-2757. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Estrogenic Activity of Perfluoro Carboxylic and Sulfonic Acids in Rainbow Trout Estrogen Receptor Binding and Liver Slice Vtg mRNA Expression Assays

Perfluoroalkylated substances (PFAS) such as carboxylic acids, and sulfonic acids were manufactured in high quantities and are ubiquitous environmental contaminants. These chemicals persist in the environment and tend to bioaccumulate. In the current study, the estrogenic potential of a series of perfluoro carboxylic acids and select perfluoro sulfonic acids were assessed in an in vitro rainbow trout estrogen receptor (rtER) binding assay and an ex vivo rtER dependent vitellogenin (Vtg) expression rainbow trout liver slice assay. Perfluoro carboxylic acids with perfluoroalkyl chain lengths of four to six did not significantly bind to the rtER or induce Vtg expression in liver slices. Perfluoro carboxylic acids with chain lengths of seven to ten, and sulfonic acids with seven and eight carbon chains bound to the rtER, but with low relative binding affinities. While affinity for the rtER increased with increasing chain length the highest affinity measured was only 0.0025% relative to the endogenous hormone 17ß-estradiol at 100%. Both the eight-carbon carboxylic acid and eight-carbon sulfonic acid induced Vtg expression in ex vivo liver slices. However, toxicity did not allow expression to achieve maximum efficacy relative to estradiol.

Epigenetic Modifications by Estrogen and Androgen in Alzheimer's Disease

For the development and maintenance of neuron networks in the brain, epigenetic mechanisms are necessary, as indicated by recent findings. This includes some of the high-order brain processes, such as behavior and cognitive functions. Epigenetic mechanisms could influence the pathophysiology or etiology of some neuronal diseases, altering disease susceptibility and therapy responses. Recent studies support epigenetic dysfunctions in neurodegenerative and psychiatric conditions, such as Alzheimer's disease (AD). These dysfunctions in epigenetic mechanisms also play crucial roles in the transgenerational effects of the environment on the brain and subsequently in the inheritance of pathologies. The possible role of gonadal steroids in the etiology and progression of neurodegenerative diseases, including Alzheimer's disease, has become the subject of a growing body of research over the last 20 years. Recent scientific findings suggest that epigenetic changes, driven by estrogen and androgens, play a vital role in brain functioning. Therefore, exploring the role of estrogen and androgen-based epigenetic changes in the brain is critical for the deeper understanding of AD. This review highlights the epigenetic modifications caused by these two gonadal steroids and the possible therapeutic strategies for AD.

Computational prediction of photosensitizers’ toxicity

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.

Characterization and analysis of estrogenic cyclic phenone metabolites produced in vitro by rainbow trout liver slices using GC-MS, LC-MS and LC-TOF-MS

Cyclic phenones are chemicals of interest to the USEPA and international organizations due to their potential for endocrine disruption to aquatic and terrestrial species. The metabolic conversion of cyclic phenones by liver hepatocytes and the structure of main metabolites yielded have not been assessed in fish species. As part of a larger project, in this study we investigated the structure of metabolites produced in vitro by rainbow trout (rt) liver slices after exposure to the model cyclic phenones benzophenone (DPK), cyclobutyl phenyl ketone (CBP) and cyclohexyl phenyl ketone (CPK). While only one distinct metabolite was detected for DPK and CBP (benzhydrol and CBPOH, respectively), CPK yielded nine positional isomers (M1-M9) as products. In absence of standards, improved inference of CPK metabolites tentative structures was achieved by combining GC-MS with and without derivatization, LC with tandem MS, LC with high resolution time of flight (TOF) MS and LC fractionation data with CPK phase II conjugative metabolism information. Data supported that CPK is metabolized by phase I oxidation of the cyclohexyl ring and not the phenyl group as predicted by metabolism simulators. CPK metabolites M1 and M2 (MW 186), were proposed to be cyclohexenyl-derivatives. Also, M6-M9 were proposed to be hydroxylated metabolites (MW 204), with the potential for undergoing phase II conjugative metabolism to glucuronides and sulfates. Finally, M3, M4 and M5 were proposed as cyclohexanone-derivatives of CPK (MW 202), resulting from the limited redox-interconversion of their hydroxylated pairs M8, M6 and M7, respectively. Assessment of metabolite role in biological responses associated with endocrine disruption will advance the development of methods for species extrapolation and the understanding of differential sensitivity of species to chemical exposure.

Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.

Estrogenic activity of multicyclic aromatic hydrocarbons in rainbow trout (Oncorhynchus mykiss) in vitro assays

A representative group of multicyclic aromatic hydrocarbons (MAHC) which can be further classified as bridged-ring (bridged-MAHC) or fused-ring (fused-MAHC) were examined for their ability to interact with the estrogen receptor of rainbow trout (rtER) in a hepatic cytosolic estrogen receptor competitive binding assay (cyto rtERαβ) and the vitellogenin (Vtg) mRNA gene activation liver slice assay. All five fused-MAHCs; naphthalene (NAFT), fluorene (FE), Fluoranthene (FAT), pyrene (PY), and 9,10-dihydroanthracene (DAC) had no estrogenic activity in the in vitro assays used. Five of the eight bridged-MAHCs; triphenylethylene (3PE), o-terphenyl (OTP), triphenylmethane (TPM), 1,1-diphenylethylene (DPE), and cis-stilbene (CSB) were positive in the rtER-binding assay. The additional three bridged-MAHC's; trans-stilbene (TSB), tetraphenylethylene (4PE), and 4,4-di-tertbutylphenyl (DtBB) were determined to be non-binders due to isomeric configuration, solubility limitation, and possible steric hinderance. It is possible that the bridged-MAHCs bind to the rtER through a proposed aromatic-aromatic stacking (π-π interaction) facilitated by perpendicular ring orientation achieved through free rotation of the bridged rings. The fused-ring structures are locked in a planar configuration which doesn't allow for rotation of rings perpendicular to one another. This first report of the rtER-binding of bridged-MAHCs in fish demonstrates binding for a class of chemicals normally not thought of as having an affinity for the estrogen receptor and further supports the versatility or promiscuity of ER ligand selectivity.

Metabolism of Diazinon in Rainbow Trout Liver Slices

Introduction

Understanding biotransformation pathways in aquatic species is an integral part of ecological risk assessment with respect to the potential bioactivation of chemicals to more toxic metabolites. The long-range goal is to gain sufficient understanding of fish metabolic transformation reactions to be able to accurately predict fish xenobiotic metabolism. While some metabolism data exist, there are few fish in vivo exposure studies where metabolites have been identified and the metabolic pathways proposed. Previous biotransformation work has focused on in vitro studies which have the advantage of high throughput but may have limited metabolic capabilities, and in vivo studies which have full metabolic capacity but are low throughput. An aquatic model system with full metabolic capacity in which a large number of chemicals could be tested would be a valuable tool.

Materials and Methods

The current study evaluated the ex vivo rainbow trout liver slice model, which has the advantages of high throughput as found in vitro models and non-dedifferentiated cells and cell to cell communication found in in vivo systems. The pesticide diazinon, which has been previously tested both in vitro and in vivo in a number of mammalian and aquatic species including rainbow trout, was used to evaluate the ex vivo slice model as a tool to study biotransformation pathways.

Results/Discussion

While somewhat limited by the analytical chemistry method employed, results of the liver slice model, mainly that hydroxypyrimidine was the major diazinon metabolite, are in line with the results of previous rainbow trout in vivo studies.

Conclusion

Therefore, the rainbow trout liver slice model is a useful tool for the study of metabolism in aquatic species.

Assessing the relevance of ecotoxicological studies for regulatory decision making

Regulatory policies in many parts of the world recognize either the utility of or the mandate that all available studies be considered in environmental or ecological hazard and risk assessment (ERA) of chemicals, including studies from the peer-reviewed literature. Consequently, a vast array of different studies and data types need to be considered. The first steps in the evaluation process involve determining whether the study is relevant to the ERA and sufficiently reliable. Relevance evaluation is typically performed using existing guidance but involves application of "expert judgment" by risk assessors. In the present paper, we review published guidance for relevance evaluation and, on the basis of the practical experience within the group of authors, we identify additional aspects and further develop already proposed aspects that should be considered when conducting a relevance assessment for ecotoxicological studies. From a regulatory point of view, the overarching key aspect of relevance concerns the ability to directly or indirectly use the study in ERA with the purpose of addressing specific protection goals and ultimately regulatory decision making. Because ERA schemes are based on the appropriate linking of exposure and effect estimates, important features of ecotoxicological studies relate to exposure relevance and biological relevance. Exposure relevance addresses the representativeness of the test substance, environmental exposure media, and exposure regime. Biological relevance deals with the environmental significance of the test organism and the endpoints selected, the ecological realism of the test conditions simulated in the study, as well as a mechanistic link of treatment-related effects for endpoints to the protection goal identified in the ERA. In addition, uncertainties associated with relevance should be considered in the assessment. A systematic and transparent assessment of relevance is needed for regulatory decision making. The relevance aspects also need to be considered by scientists when designing, performing, and reporting ecotoxicological studies to facilitate their use in ERA. Integr Environ Assess Manag 2017;13:652-663. © 2016 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Avoiding False Positives and Optimizing Identification of True Negatives in Estrogen Receptor Binding and Agonist/Antagonist Assays

The potential for chemicals to affect endocrine signaling is commonly evaluated via in vitro receptor binding and gene activation, but these assays, especially antagonism assays, have potential artifacts that must be addressed for accurate interpretation. Results are presented from screening 94 chemicals from 54 chemical groups for estrogen receptor (ER) activation in a competitive rainbow trout ER (rtER) binding assay and a trout liver slice vitellogenin mRNA expression assay. Results from true competitive agonists and antagonists, and inactive chemicals with little or no indication of ER binding or gene activation were easily interpreted. However, results for numerous industrial chemicals were more challenging to interpret, including chemicals with: (1) apparent competitive binding curves but no gene activation, (2) apparent binding and gene inhibition with evidence of either cytotoxicity or changes in assay media pH, (3) apparent binding but non-competitive gene inhibition of unknown cause, or (4) no rtER binding and gene inhibition not due to competitive ER interaction but due to toxicity, pH change, or some unknown cause. The use of endpoints such as toxicity, pH, precipitate formation, and determination of inhibitor dissociation constants (Ki) for interpreting the results of antagonism and binding assays for diverse chemicals is presented. Of the 94 chemicals tested for antagonism only two, tamoxifen and ICI-182780, were found to be true competitive antagonists. This report highlights the use of two different concentrations of estradiol tested in combination with graded concentrations of test chemical to provide the confirmatory evidence to distinguish true competitive antagonism from apparent antagonism.

Evaluation of the scientific underpinnings for identifying estrogenic chemicals in nonmammalian taxa using mammalian test systems

The US Environmental Protection Agency has responsibility for assessing endocrine activity of more than 10 000 chemicals, a task that cannot reasonably be achieved solely through use of available mammalian and nonmammalian in vivo screening assays. Hence, it has been proposed that chemicals be prioritized for in vivo testing using data from in vitro high-throughput assays for specific endocrine system targets. Recent efforts focused on potential estrogenic chemicals-specifically those that activate estrogen receptor-alpha (ERα)-have broadly demonstrated feasibility of the approach. However, a major uncertainty is whether prioritization based on mammalian (primarily human) high-throughput assays accurately reflects potential chemical-ERα interactions in nonmammalian species. The authors conducted a comprehensive analysis of cross-species comparability of chemical-ERα interactions based on information concerning structural attributes of estrogen receptors, in vitro binding and transactivation data for ERα, and the effects of a range of chemicals on estrogen-signaling pathways in vivo. Overall, this integrated analysis suggests that chemicals with moderate to high estrogenic potency in mammalian systems also should be priority chemicals in nonmammalian vertebrates. However, the degree to which the prioritization approach might be applicable to invertebrates is uncertain because of a lack of knowledge of the biological role(s) of possible ERα orthologs found in phyla such as annelids. Further, comparative analysis of in vitro data for fish and reptiles suggests that mammalian-based assays may not effectively capture ERα interactions for low-affinity chemicals in all vertebrate classes. Environ Toxicol Chem 2016;35:2806-2816. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US Government work and, as such, is in the public domain in the United States of America.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

Prediction of the endocrine disruption profile of pesticides

Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to save time and money, (quantitative) structure-activity relationship ((Q)SAR) models are increasingly used as a surrogate for these laboratory assays. However, most of them focus only on a specific target (e.g. estrogenic or androgenic receptor) while, to be more efficient, endocrine disruption modelling should preferentially consider profiles of activities to better gauge this complex phenomenon. In this context, an attempt was made to evaluate the endocrine disruption profile of 220 structurally diverse pesticides using the Endocrine Disruptome simulation (EDS) tool, which simultaneously predicts the probability of binding of chemicals on 12 nuclear receptors. In a first step, the EDS web-based system was successfully applied to 16 pharmaceutical compounds known to target at least one of the studied receptors. About 13% of the studied pesticides were estimated to be potential disruptors of the endocrine system due to their high predicted affinity for at least one receptor. In contrast, about 55% of them were unlikely to be endocrine disruptors. The simulation results are discussed and some comments on the use of the EDS tool are made.

A rule-based expert system for chemical prioritization using effects-based chemical categories

A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically transparent and meet the needs of a specific application, i.e. predict for all chemicals within two well-defined inventories (industrial chemicals used as pesticide inerts and antimicrobial pesticides). These chemicals all lack structural features associated with high affinity binders and thus any binding should be low affinity. Similar to the high-quality fathead minnow database upon which Veith QSARs were built, the ERES was derived from what has been termed gold standard data, systematically collected in assays optimized to detect even low affinity binding and maximizing confidence in the negatives determinations. The resultant logic-based decision tree ERES, determined to be a robust model, contains seven major nodes with multiple effects-based chemicals categories within each. Predicted results are presented in the context of empirical data within local chemical structural groups facilitating informed decision-making. Even using optimized detection assays, the ERES applied to two inventories of >600 chemicals resulted in only ~5% of the chemicals predicted to bind ER.