Assessing the chemical-induced estrogenicity using in silico and in vitro methods.

Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC's effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds.

Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert.

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.

Liquid chromatography-time-of-flight high-resolution mass spectrometry study and determination of the dansylated products of estrogens and their hydroxylated metabolites in water and wastewater.

A method combining liquid chromatography with a dual-probe ultraspray electrospray ionization (ESI) source and time-of-flight high-resolution mass spectrometry (LC-ESI-TOF/MS) was developed for the simultaneous determination of four steroidal sex hormones, estrone (E1), 17ß-estradiol (E2), 17α-ethinyl estradiol (EE2), and estriol (E3), as well as five of their hydroxylated metabolites, 2-hydroxyestrone (2-OHE1), 4-hydroxyestrone (4-OHE1), 16α-hydroxyestrone (16-OHE1), 2-hydroxyestradiol (2-OHE2), and 4-hydroxyestradiol (4-OHE2), in water samples in a short chromatographic run of 10 min. Derivatization of the analytes was optimized using dansyl chloride as the derivatizing agent. Under optimal positive ionization conditions, the following signals, which had not been previously reported, were observed (with theoretical values of m/z 377.1373 for 2- and 4-OHE1 and 378.1452 for 2- and 4-OHE2), corresponding to doubly derivatized catechol estrogens in the form of [M+2H]2+. These mass spectrometric signals were more abundant than those reported previously for the [M+H]+ forms of these hydroxylated metabolites. Solid-phase extraction (SPE) with an octadecyl-endcapped sorbent was used to pretreat tap water and effluent from a wastewater treatment plant (WWTP) in Santiago, Chile. The method achieved the simple, fast, and sensitive measurement of nine estrogens with quantitative recoveries (higher than 85.4%). Detection and quantification limits were between 1 and 17 ng L-1 and between 3 and 58 ng L-1, respectively, for all compounds in water. The estrogens E1 and E2 were found in WWTP effluent at concentrations of 7 ± 1 and 41 ± 1 ng L-1, respectively, and EE2 was detected at a concentration below the limit of quantitation. This study shows that the proposed method is suitable for the accurate, rapid, and selective determination of all these analytes at trace levels. Graphical abstract ᅟ.

Classification of natural estrogen-like isoflavonoids and diphenolics by QSAR tools.

This work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied compounds were calculated from the optimized molecular geometries. Both the LDA and SVMs models contain four descriptors, however, the SVMs model (total accuracy 89.1%) was found to be superior to the LDA model (total accuracy 80.0%). The analysis of molecular descriptors within our models provided essential insights towards a better understanding of the estrogenic mechanisms of natural estrogen-like phytoestrogens. Furthermore, the derived models can be applied in the future screening of other natural estrogen-like compounds.

Tumor benigno de vulva en paciente postnenopáusica. presentación de caso y revisión de bibliografía / Benign tumor of vulva in post menopause patient. A case report and literature review

Introducción: Durante la transición, y especialmente tras la menopausia, la vulva experimenta una serie de cambios regresivos debido a la disminución de los niveles de estrógenos y al mismo tiempo aumentan determinadas patologías relacionadas con el trofismo epitelial. Así mismo, a partir de la menopausia aumenta el riesgo de cáncer de vulva. Caso Clínico: Presentamos el caso de una mujer de 63 años de edad que consultó por una tumoración vulvar de 12 años de evolución, decrecimiento lento, sin ningun otro sintoma acompañante, y sin ninguna particularidad en sus antecedenets patológicos o ginecológicos; se le practicó escisión quirúrgica y posterior estudio anatomopatológico el cual clasifica a la tumoración como una neoplasia fusocelular fibrosa benigna. Discusion: Los tumores benignos no son tumoraciones específicas de la vulva y son poco frecuentes, aunque globalmente pueda decirse que son muy frecuentes a nivel de toda la economía, no lo son tanto en la vulva y menos durante la menopausia. Este caso se informa debido a su rara ocurrencia, no obstante, es conveniente tener conocimiento de ellas para el diagnóstico diferencial con el cáncer de vulva. Conclusión: El diagnóstico de tumores de vulva es un hallazgo raro, y lo hace mas raro aún en pacientes postmenopausicas, lo cual sugiere la necesidad de realizar un adecuado diagnóstico diferencial...(AU)

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

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.

Nongenomic mechanisms of physiological estrogen-mediated dopamine efflux.

BACKGROUND: Neurological diseases and neuropsychiatric disorders that vary depending on female life stages suggest that sex hormones may influence the function of neurotransmitter regulatory machinery such as the dopamine transporter (DAT). RESULTS: In this study we tested the rapid nongenomic effects of several physiological estrogens [estradiol (E2), estrone (E1), and estriol (E3)] on dopamine efflux via the DAT in a non-transfected, NGF-differentiated, rat pheochromocytoma (PC12) cell model that expresses membrane estrogen receptors (ERs) alpha, beta, and GPR30. We examined kinase, ionic, and physical interaction mechanisms involved in estrogenic regulation of the DAT function. E2-mediated dopamine efflux is DAT-specific and not dependent on extracellular Ca2+-mediated exocytotic release from vesicular monoamine transporter vesicles (VMATs). Using kinase inhibitors we also showed that E2-mediated dopamine efflux is dependent on protein kinase C and MEK activation, but not on PI3K or protein kinase A. In plasma membrane there are ligand-independent associations of ERalpha and ERbeta (but not GPR30) with DAT. Conditions which cause efflux (a 9 min 10(-9) M E2 treatment) cause trafficking of ERalpha (stimulatory) to the plasma membrane and trafficking of ERbeta (inhibitory) away from the plasma membrane. In contrast, E1 and E3 can inhibit efflux with a nonmonotonic dose pattern, and cause DAT to leave the plasma membrane. CONCLUSION: Such mechanisms explain how gender biases in some DAT-dependent diseases can occur.

Estrogenic compounds, estrogen receptors and vascular cell signaling in the aging blood vessels.

The cardiovascular benefits of menopausal hormone therapy (MHT) remain controversial. The earlier clinical observations that cardiovascular disease (CVD) was less common in MHT users compared to non-users suggested cardiovascular benefits of MHT. Also, experimental studies have identified estrogen receptors ERalpha, ERbeta and GPR30, which mediate genomic or non-genomic effects in vascular endothelium, smooth muscle, and extracellular matrix (ECM). However, data from randomized clinical trials (RCTs), most notably the Women's Health Initiative (WHI) study, have challenged the cardiovascular benefits and highlighted adverse cardiovascular events with MHT. The discrepancies have been attributed to the design of RCTs, the subjects' advanced age and preexisting CVD, and the form of estrogen used. The discrepancies may also stem from age-related changes in vascular ER amount, distribution, integrity, and post-receptor signaling pathways as well as structural changes in the vasculature. Age-related changes in other sex hormones such as testosterone may also alter the hormonal environment and influence the cardiovascular effects of estrogen. Investigating the chemical properties, structure-activity relationship and pharmacology of natural and synthetic estrogens should improve the effectiveness of conventional MHT. Further characterization of phytoestrogens, selective estrogen-receptor modulators (SERMs), and specific ER agonists may provide substitutes to conventional MHT. Conditions with excess or low estrogen levels such as polycystic ovary syndrome (PCOS) and Turner syndrome may provide insight into the development and regulation of ER and the mechanisms of aberrant estrogen-ER interactions. The lessons learned from previous RCTs have led to more directed studies such as the Kronos Early Estrogen Prevention Study (KEEPS). Careful design of experimental models and RCTs, coupled with the development of specific ER modulators, hold the promise of improving the actions of estrogen in the aging blood vessels and thereby enhancing the efficacy and safety of MHT in postmenopausal CVD.

Estrogen carcinogenesis: specific identification of estrogen-modified nucleobase in breast tissue from women.

Prolonged exposure to estrogens correlates with an increased risk for breast cancer. One explanation is that estrogen metabolites cause mutations by reacting with DNA, leading to depurination. We describe an extraction procedure and a liquid chromatographic tandem mass spectrometric (LC/MS/MS) assay to detect estrone-metabolite-modified adenine (Ade) in 100-200 mg samples of human breast tissue. To ensure reliable analyses, we used a synthetic estrone-metabolite-modified, U-(15)N-labeled Ade as an internal standard (IS). Appropriate high-pressure liquid chromatography gives sharp (approximately 5 s at half-height) and identical retention times for the analyte and the IS. In breast tissue from women with and without cancer, we found a coeluting material with similar MS/MS fragmentation as the IS, providing high specificity in the identification of the modified Ade; the recovery was approximately 50%. For women with and without breast cancer, the levels of the modified Ade are in the range of 20-70 fmol/g of breast tissue from five women and not detectable in tissue from another woman. The sample size and detection limits are not yet sufficient to permit distinctions between cancer and noncancer patients.

Prediction of estrogenicity: validation of a classification model.

(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.

Evaluation of ligand selectivity using reporter cell lines stably expressing estrogen receptor alpha or beta.

Estrogens control transcriptional responses through binding to two different nuclear receptors, estrogen receptor alpha (ERalpha) and beta (ERbeta). Since these two ER subtypes are thought to mediate different biological effects, there is intense interest in designing subtype-selective ER ligands. In this study, we evaluated the ERalpha and ERbeta selectivity of 19 known estrogens and antiestrogens using reporter cell lines previously developed in our laboratory. The HELN-ERalpha and HELN-ERbeta cells stably express full-length ERalpha and ERbeta, respectively, and are derived from HELN cells (HeLa cells stably transfected with an ERE-driven luciferase plasmid). We report that 16alpha-LE2, PPT and 3beta,5alpha-GSD have a high ERalpha-selective agonist potency while 8beta-VE2, DPN, genistein and biochanin A show ERbeta selectivity with 8beta-VE2 being the most potent and selective ERbeta agonist. We also tested ER antagonists and we showed that raloxifene and RU486 are ERalpha and ERbeta-selective antiestrogens, respectively. In all cases, selectivity is due to differences in binding affinities as indicated by whole-cell ligand-binding assays. Very interestingly, we demonstrate that a combination of genistein and raloxifene produces a full-ERbeta specific response. Together these results demonstrate the usefulness of our stably transfected cell lines to characterize ER ligands and indicate that treatments combining agonist/antagonist ligands produce full-ERbeta selectivity.

Myocardial infarction risk and hormone replacement: differences between products.

OBJECTIVES: To establish the risk of myocardial infarction (MI) in users of hormone replacement therapy (HRT) compared with non-users and to compare the risk between different HRT regimens. METHODS: A population-based cohort and case-control study, and a case-control study nested within a cohort of HRT users, using the UK General Practice Research Database. Differences between HRT regimen, mode of administration and duration and recency of use were examined whilst adjusting for confounding. RESULTS: In the cohort and case-control study, 4537 cases of MI were identified in 2.62 million observed women years, cases were age-matched to 27,220 controls. In both studies, current and past HRT use were associated with reduced risk estimates for MI compared with no prior use. MIs were less likely to be fatal amongst women who had used HRT than amongst never users (OR(adj) 0.58; 95% CI 0.45-0.75). No difference in risk was seen between current and past use, oral and transdermal HRT or between different regimens (p>0.44). In the nested study, no difference was found in the association with MI risk between different oestrogen-progestogen combinations or between different combinations and tibolone. Unopposed oestrogen use was not associated with a decrease in risk compared with combined HRT. CONCLUSIONS: These results are consistent with previous observational studies in supporting the hypothesis that use of postmenopausal HRT is associated with a decrease in risk of acute myocardial infarction (AMI). Case fatality differed between HRT users and non-users, suggesting a protective effect of HRT. This study does not demonstrate a difference between regimens.

Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods.

The performance of decision tree (DT), learning vector quantization (LVQ), and k-nearest neighbour (kNN) methods classifying active and inactive estrogenic compounds in terms of their structure activity relationship (SAR) was evaluated. A set of 311 compounds was used for construction of the models, the predictive power of which was verified with separate training and test sets. Principal components derived from molecular descriptors calculated with DRAGON software were used as variables representing the structures of the compounds. Broadly, kNN had the best classification ability and DT the weakest, although the performance of each method was dependent on the group of compounds used for modelling. The best performance was obtained with kNN for the calf estrogen receptor data, averaging 98.3% of correctly classified compounds in the external tests. Overall, the results indicate that all the methods tested are suitable for the SAR classification of estrogenic compounds, producing models with a predictive power ranging from adequate to excellent.

Comparison of in vitro and in vivo screening models for androgenic and estrogenic activities.

Identification of nuclear receptor-mediated endocrine activities is important in a variety of fields, ranging from pharmacological and clinical screening, to food and feed safety, toxicological monitoring, and risk assessment. Traditionally animal studies such as the Hershberger and Allen-Doisy tests are used for the assessment of androgenic and estrogenic potencies, respectively. To allow fast analysis of the activities of new chemicals, food additives, and pharmaceutical compounds, high-throughput screening strategies have been developed. Here, a panel of mainly steroidal compounds, screened in different in vitro assays, was compared with two human U2-OS cell line-based CALUX (Chemically Activated LUciferase eXpression) reporter gene assays for androgens (AR CALUX) and estrogens (ERalpha CALUX). Correlations found between the data of these two CALUX reporter gene assays and data obtained with other in vitro screening assays measuring receptor binding or reporter gene activation (CHO cell line-based) were good (correlation coefficients (r2) between 0.54 and 0.76; p < 0.0001). Good correlations were also found between the in vitro and in vivo data (correlation coefficient r2 = 0.46 for the AR CALUX vs. Hershberger assay and r2 = 0.87 for the ERalpha CALUX vs. Allen-Doisy assay). The variations in the results obtained with the reporter gene assays (CALUX vs. CHO cell line based) were relatively small, showing the robustness of these types of assays. Using hierarchical clustering, bioactivity relationships between compounds but also relationships between various bioassays were determined. The in vitro assays were found to be good predictors of in vivo androgenic or estrogenic activity of a range of compounds, allowing prescreen and/or possible reduction of animal studies.

Tools for making correct decisions regarding hormone therapy. part I: background and drugs.

OBJECTIVE: To review existing scientific data related to the biology of estrogen in the aging process of women, and to understand the pharmacology, physiology, and diversity of sex hormones. DESIGN: A MEDLINE computer search was performed to identify relevant articles. RESULT(S): Estrogen and progesterone have been prescribed for postmenopausal women over the last 60 years. Various formulations have had divergent properties, which are related to dissimilarities in the molecule structure, metabolism, plasma carrier, distribution, receptor binding, and the specific localizations of the different receptors in the various organs. CONCLUSION(S): Extensive data are available on the various characteristics of sex-hormone formulations, which are related to clinical consequences. The drugs and means of application have resulted in fundamental differences in activity, such as variance between oral and transdermal applications resulting in different forms of liver involvement. This information could assist in understanding why certain formulations may either prove harmful or beneficial to specific women.

The compounds from the hollyhock extract (Althaea rosea Cav. var. nigra) affect the aromatization in rat testicular cells in vivo and in vitro.

Among medicinal plants, extract from the hollyhock flowers is a source of antocyanides and flavonoids. The latter compounds belong, among others, to phytoestrogens (plant-derived dietary estrogens). The important role of estrogens in the testis is now well documented, and phytoestrogens, which may act as estrogen agonists or estrogen antagonists can also alter the reproductive function of the male. The aim of this study was to show whether the exposure of male rats to the aqueous hollyhock extract could affect the process of aromatization in their testes and in cultured Leydig cells. This was investigated by immunocytochemistry and radioimmunological assays. Immunoreactivities for aromatase and estrogen receptor beta were weaker both in testicular sections and cultured Leydig cells after hollyhock extract administration when compared to the controls, while the intensity of immunoreaction for estrogen receptor alpha remained unchanged. A lower level of estradiol secreted by cultured Leydig cells from the experimental group positively correlated with a direct inhibition of aromatase activity. Additionally, a quantitative analysis of flavonoid fraction from the hollyhock extract revealed the presence of quercetin and kaempferol. It seems that a weak antiestrogenic activity of flavonoid compounds present in the hollyhock extract is mediated through aromatase and estrogen receptor beta rather than by estrogen receptor alpha.

Poison prevention packaging requirements; exemption of hormone replacement therapy products. Final rule.

The Commission is amending its child-resistant packaging requirements to exempt hormone replacement therapy ("HRT") products containing one or more progestogen or estrogen substances. Current exemptions cover some HRT products, but not others. This rule would uniformly exempt from child resistant packaging requirements all HRT products that rely solely on the activity of one or more progestogen or estrogen substances.