Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens

Derivation of a bisphenol A oral reference dose (RfD) and drinking-water equivalent concentration

Human exposure to bisphenol A (BPA) is due to that found in the diet, and BPA and its metabolites were detected at parts per billion (or less) concentrations in human urine, milk, saliva, serum, plasma, ovarian follicular fluid, and amniotic fluid. Adverse health effects in mice and rats may be induced after parenteral injection or after massive oral doses. Controlled ingestion trials in healthy adult volunteers with 5 mg d16-BPA were unable to detect parent BPA in plasma despite exquisitely sensitive (limit of detection = 6 nM) methods, but by 96 h 100% of the administered dose was recovered in urine as the glucuronide. The extensive BPA glucuronidation following ingestion is not seen after parenteral injection; only the parent BPA binds plasma proteins and estrogen receptors (ER). The hypothesis that BPA dose-response may be described by a J- or U-shape curve was not supported by toxicogenomic data collected in fetal rat testes and epididymes (after repeated parenteral exposure at 2-400,000 microg/kg-d), where a clear monotonic dose-response both in the numbers of genes and magnitude of individual gene expression was evident. There is no clear indication from available data that the BPA doses normally consumed by humans pose an increased risk for immunologic or neurologic disease. There is no evidence that BPA poses a genotoxic or carcinogenic risk and clinical evaluations of 205 men and women with high-performance liquid chromatography (HPLC)-verified serum or urinary BPA conjugates showed (1) no objective signs, (2) no changes in reproductive hormones or clinical chemistry parameters, and (3) no alterations in the number of children or sons:daughters ratio. Results of benchmark dose (BMD10 and BMDL10) calculations and no-observed-adverse-effect level (NOAEL) inspections of all available and reproducible rodent studies with oral BPA found BMD and NOAEL values all greater than the 5 mg/kg-d NOAELs from mouse and rat multigeneration reproduction toxicity studies. While allometric and physiologically based pharmacokinetic (PBPK) models were constructed for interspecies scaling of BPA and its interaction with ER, multigeneration feeding studies with BPA at doses spanning 5 orders of magnitude failed to identify signs of developmental toxicity or adverse changes in reproductive tract tissues; the 5-mg/kg-d NOAELs identified for systemic toxicity in rats and mice were less than the oral NOAELs for reproductive toxicity. Thus, it is the generalized systemic toxicity of ingested BPA rather than reproductive, immunologic, neurobehavioral, or genotoxic hazard that represents the point of departure. Using U.S. Environmental Protection Agency (EPA) uncertainty factor guidance and application of a threefold database uncertainty factor (to account for the fact that the carcinogenic potential of transplacental BPA exposure has yet to be fully defined and comprehensive neurobehavioral and immunotoxicologic evaluations of BPA by relevant routes and at relevant doses have yet to be completed) to the administered dose NOAEL results in an oral RfD of 0.016 mg/kg-d. Assuming the 70-kg adult consumes 2 L of water each day and adopting the default 20% U.S. EPA drinking water relative source contribution yields a 100 microg/L BPA total allowable concentration (TAC).

Screening of synthetic and plant-derived compounds for (anti)estrogenic and (anti)androgenic activities

Recently we constructed yeast cells that either express the human estrogen receptor alpha or the human androgen receptor in combination with a consensus ERE or ARE repeat in the promoter region of a green fluorescent protein (yEGFP) read-out system. These bioassays were proven to be highly specific for their cognate agonistic compounds. In this study the value of these yeast bioassays was assessed for analysis of compounds with antagonistic properties. Several pure antagonists, selective estrogen receptor modulators (SERMs) and plant-derived compounds were tested. The pure antiestrogens ICI 182,780 and RU 58668 were also classified as pure ER antagonists in the yeast estrogen bioassay and the pure antiandrogen flutamide was also a pure AR antagonist in the yeast androgen bioassay. The plant-derived compounds flavone and guggulsterone displayed both antiestrogenic and antiandrogenic activities, while 3,3'-diindolylmethane (DIM) and equol combined an estrogenic mode of action with an antiandrogenic activity. Indol-3-carbinol (I3C) only showed an antiandrogenic activity. Coumestrol, genistein, naringenin and 8-prenylnaringenin were estrogenic and acted additively, while the plant sterols failed to show any effect. Although hormonally inactive, in vitro and in vivo metabolism of the aforementioned plant sterols may still lead to the formation of active metabolites in other test systems.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Exemestane's 17-hydroxylated metabolite exerts biological effects as an androgen

Aromatase inhibitors (AI) are being evaluated as long-term adjuvant therapies and chemopreventives in breast cancer. However, there are concerns about bone mineral density loss in an estrogen-free environment. Unlike nonsteroidal AIs, the steroidal AI exemestane may exert beneficial effects on bone through its primary metabolite 17-hydroexemestane. We investigated 17-hydroexemestane and observed it bound estrogen receptor alpha (ERalpha) very weakly and androgen receptor (AR) strongly. Next, we evaluated 17-hydroexemestane in MCF-7 and T47D breast cancer cells and attributed dependency of its effects on ER or AR using the antiestrogen fulvestrant or the antiandrogen bicalutamide. 17-Hydroexemestane induced proliferation, stimulated cell cycle progression and regulated transcription at high sub-micromolar and micromolar concentrations through ER in both cell lines, but through AR at low nanomolar concentrations selectively in T47D cells. Responses of each cell type to high and low concentrations of the non-aromatizable synthetic androgen R1881 paralleled those of 17-hydroexemestane. 17-Hydroexemestane down-regulated ERalpha protein levels at high concentrations in a cell type-specific manner similarly as 17beta-estradiol, and increased AR protein accumulation at low concentrations in both cell types similarly as R1881. Computer docking indicated that the 17beta-OH group of 17-hydroexemestane relative to the 17-keto group of exemestane contributed significantly more intermolecular interaction energy toward binding AR than ERalpha. Molecular modeling also indicated that 17-hydroexemestane interacted with ERalpha and AR through selective recognition motifs employed by 17beta-estradiol and R1881, respectively. We conclude that 17-hydroexemestane exerts biological effects as an androgen. These results may have important implications for long-term maintenance of patients with AIs.

Structural properties of estrogen receptor ligand obtained by study of hydroxylated phthalate ester derivatives

Phthalate esters (PEs), a group of plasticizers, are suspected to be endocrine-disrupting chemicals. Here, PE derivatives were used as probes for elucidating the structural properties of estrogen receptor (ER) ligands. A comprehensive study was performed using more than 40 PE derivatives including ring-/alkyl-hydroxylated and nonsymmetrical diesters possessing independently altered alkyls of C1-C8. Estrogenic activity of these derivatives is determined with three assays for ER-binding, coactivator-recruiting and transactivation. Phenolic hydroxylation increased activity, while hydroxylation of the ester alkyl group had no distinct effect on ER binding or transcription coactivator recruitment. Ring-hydroxylated PE derivatives harboring different ester alkyls revealed that the length of both alkyls independently affects transactivation of ER. These comprehensive data would be useful for the better understanding of structure-activity relationship of endocrine-disrupting chemicals.

Molecular modelling and QSAR analysis of the estrogenic activity of terpenoids isolated from Ferula plants

The relationship between chemical structure and estrogenic activity in a series of terpenoid esters with aromatic and aliphatic acid substituents isolated from Ferula plants, was studied. The fragments of the terpenoid structure that are potentially responsible for estrogenic activity were revealed. A quantitative structure-estrogenic activity study has been carried out using the QSAR approach with use of data derived from quantum-chemical calculations as well as data generated from three-dimensional structures of terpenoids. A number of molecular descriptors was obtained from the density functional theory (DFT) at the B3LYP/6-31G(d, p) level of calculation. Comparative analysis of the quantum-chemical computational data was also performed to confirm hypothesis concerning importance of the distance between the oxygen of alcohol hydroxyl group and the functional group in the para-position of the benzene ring (the hydroxyl or methoxy group). Use of the Genetic Algorithm in the QSAR analysis allowed the structural and physicochemical parameters of the terpenoids responsible for estrogenic activity to be determined. A significant QSAR model was obtained with an r(2) value of 0.892. The resulting model showed a reliable dependence of estrogenic activity of the terpenoids on such parameters as molecular shape, number of phenolic groups, surface polarity and the energy of the highest occupied molecular orbital.

Improving the Performance of Self-Organizing Maps via Growing Representations

Self-organizing maps (SOMs) are a type of artificial neural network that through training can produce simplified representations of large, high dimensional data sets. These representations are typically used for visualization, classification, and clustering and have been successfully applied to a variety of problems in the pharmaceutical and bioinformatics domains. SOMs in these domains have generally been restricted to static sets of nodes connected in either a grid or hexagonal connectivity and planar or toroidal topologies. We investigate the impact of connectivity and topology on SOM performance, and experiments were performed on fixed and growing SOMs. Three synthetic and two relevant data sets from the chemistry domain were used for evaluation, and performance was assessed on the basis of topological and quantization errors after equivalent training periods. Although we found that all SOMs were roughly comparable at quantizing a data space, there was wide variation in the ability to capture its underlying structure, and growing SOMs consistently outperformed their static counterparts in regards to topological errors. Additionally, one growing SOM, the Neural Gas, was found to be far more capable of capturing details of a target data space, finding lower dimensional relationships hidden within higher dimensional representations.

In silico screening of estrogen-like chemicals based on different nonlinear classification models

Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens.

Engineered Chimeric Enzymes as Tools for Drug Discovery: Generating Reliable Bacterial Screens for the Detection, Discovery, and Assessment of Estrogen Receptor Modulators

Engineered protein-based sensors of ligand binding have emerged as attractive tools for the discovery of therapeutic compounds through simple screening systems. We have previously shown that engineered chimeric enzymes, which combine the ligand-binding domains of nuclear hormone receptors with a highly sensitive thymidylate synthase reporter, yield simple sensors that report the presence of hormone-like compounds through changes in bacterial growth. This work describes an optimized estrogen sensor in Escherichia coli with extraordinary reliability in identifying diverse estrogenic compounds and in differentiating between their agonistic/antagonistic pharmacological effects. The ability of this system to assist the discovery of new estrogen-mimicking compounds was validated by screening a small compound library, which led to the identification of two structurally novel estrogen receptor modulators and the accurate prediction of their agonistic/antagonistic biocharacter in human cells. Strong evidence is presented here that the ability of our sensor to detect ligand binding and recognize pharmacologically critical properties arises from allosteric communication between the artificially combined protein domains, where different ligand-induced conformational changes in the receptor are transmitted to the catalytic domain and translated to distinct levels of enzymic efficiency. To the best of our knowledge, this is one of the first examples of an engineered enzyme with the ability to sense multiple receptor conformations and to be either activated or inactivated depending on the nature of the bound effector molecule. Because the proposed mechanism of ligand dependence is not specific to nuclear hormone receptors, we anticipate that our protein engineering strategy will be applicable to the construction of simple sensors for different classes of (therapeutic) binding proteins.

Imaging of Selective Nuclear Receptor Modulator-Induced Conformational Changes in the Nuclear Receptor to Allow Interaction with Coactivator and Corepressor Proteins in Living Cells

Selective nuclear receptor modulators (SNRMs), which are used clinically for the treatment of NR-related diseases, display mixed agonistic/antagonistic activity in a tissue-selective manner depending on the cellular concentrations of coregulator proteins, that is, coactivators and corepressors. The molecular details of the SNRM function provided us with an idea for a rational method for the high-throughput screening of SNRMs in real time in intact living cells. We have developed genetically encoded fluorescent indicators based on the principle of ligand-induced coactivator and/or corepressor recruitment to NR ligand binding domain in single living cells. We demonstrated that an SNRM induces a distinct conformational change in the NR LBD, which is different from that induced by a full agonist or antagonist, but favorable for the recruitment of a coactivator or corepressor protein to the NR. The molecular details of an SNRM binding to a NR, and the subsequently induced conformational changes and recruitment of coregulator protein(s) are important features for the understanding of SNRM action in the living body. Our fluorescent indicators are capable of distinguishing among agonists, antagonists, and SNRMs, and can therefore serve as versatile molecular sensors that predict the pharmacological character of ligands, which is important for an accurate cure of a disease.

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.

QSAR and mechanistic interpretation of estrogen receptor binding

A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A-B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A-C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A-B-C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA > or = 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism.

Pharmacophore Mapping of Selective Binding Affinity of Estrogen Modulators through Classical and Space Modeling Approaches: Exploration of Bridged-Cyclic Compounds with Diarylethylene Linkage

Research on Selective Estrogen Receptor Modulators (SERMs) has been driven by interest in discovering target selective molecules. In view of such significance, the present work explored the pharmacophores of estrogen receptor (ER) subtypes specific binding affinities of diverse compounds belonging to the category of bridged bicyclic-1,1-diarylethylene derivatives. Implementing classical QSAR and CATALYST based space-modeling approaches, it has been explored that attachment of aryl ring systems to unsaturated linkages, availability of phenolic hydroxyl group, global hydrophobicity, and stereochemistry of certain functional groups might be important for governing the subtype specific estrogenic behavior of this group of compounds. Supplementing this deduction, critical interfeature distances between hydrogen bond acceptor, hydrophobic, and ring aromatic features along with steric influence are found to primarily influence the ER-subtypes specific binding of this series of compounds.

Ligand-based approach to in silico pharmacology: nuclear receptor profiling

Bioactive ligands are a valuable and increasingly accessible source of information about protein targets. On the basis of this statement, a list of 25 nuclear receptors was described by a series of bioactive ligands extracted directly from bibliographical sources, stored properly in an annotated chemical library, and mathematically represented using the recently reported SHED molecular descriptors. Analysis of this ligand information allowed for derivation of a threshold of nuclear receptor concern. If the similarity of one molecule to any of the molecules annotated to one particular nuclear receptor is below that threshold, the molecule receives an alert on the probability of having affinity below 10 microM for that nuclear receptor. On this basis, a linkage map was constructed that reveals the interaction network of nuclear receptors from the perspective of their active ligands. This ligand-based approach to nuclear receptor profiling was subsequently applied to four external chemical libraries of 10,000 molecules targeted to proteases, kinases, ion channels, and G protein-coupled receptors. The percentage of each library that returned an alert on at least one nuclear receptor was reasonably low and varied between 4.4 and 9.7%. In addition, ligand-based nuclear receptor profiling of a set of 2944 drugs provided an alert for 153 drugs. For some of them, namely, acitretin, telmisartan, phenyltoloxamine, tazarotene, and flumazenil, bibliographical evidence could be found indicating that those drugs may indeed have some potential off-target residual affinity for the nuclear receptors annotated. Overall, the present findings suggest that ligand-based approaches to protein family profiling appear as a promising means toward the establishment of novel tools for in silico pharmacology.

Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

The estrogen receptor (ER) is an important drug target for the development of novel therapeutic agents for the treatment of breast cancer. Progress towards the design of more potent and selective ER modulators requires the optimization of multiple ligand-receptor interactions. Comparative molecular field analyses (CoMFA) and hologram quantitative structure-activity relationships (HQSAR) were conducted on a large set of ERalpha modulators. Two training sets containing either 127 or 69 compounds were used to generate QSAR models for in vitro binding affinity and potency, respectively. Significant correlation coefficients (affinity models, CoMFA, r(2)=0.93 and q(2)=0.79; HQSAR, r(2)=0.92 and q(2)=0.71; potency models, CoMFA, r(2)=0.94 and q(2)=0.72; HQSAR, r(2)=0.92 and q(2)=0.74) were obtained, indicating the potential of the models for untested compounds. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel ERalpha modulators having improved affinity and potency.

Estrogenic activity associated with organochlorine compounds in fish extracts from European mountain lakes

Fish muscle extracts from ten European mountain lakes were analyzed for organochlorine compounds (OCs) and estrogenic activity, the latter by a recombinant yeast assay based on the human estrogen receptor. Seventy percent of the samples showed estrogenic activity above detection limits and a subset of five samples showed estrogenic activities, equivalent to more than 10,000 pg/g of estradiol. These highly estrogenic samples occurred in two lakes, Velké Hinçovo in the Tatra Mountains and Redon in the Pyrenees. Principal component analysis correlated estrogenic activity of muscle extracts to fish age and concentrations of the more chlorinated polychlorobiphenyls (PCBs). This is consistent with previously observed correlations of these PCBs with fish age. In addition, most fish with high estrogenic activity were found in lakes containing high OC levels in the sediments, which gives further ground to atmospheric deposition of anthropogenic pollutants as main process leading to the observed endocrine disruption effects.

Benchmarking sets for molecular docking

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.

In silico prediction of pregnane X receptor activators by machine learning approaches

Pregnane X receptor (PXR) regulates drug metabolism and is involved in drug-drug interactions. Prediction of PXR activators is important for evaluating drug metabolism and toxicity. Computational pharmacophore and quantitative structure-activity relationship models have been developed for predicting PXR activators. Because of the structural diversity of PXR activators, more efforts are needed for exploring methods applicable to a broader spectrum of compounds. We explored three machine learning methods (MLMs) for predicting PXR activators, which were trained and tested by using significantly higher number of compounds, 128 PXR activators (98 human) and 77 PXR non-activators, than those of previous studies. The recursive feature-selection method was used to select molecular descriptors relevant to PXR activator prediction, which are consistent with conclusions from other computational and structural studies. In a 10-fold cross-validation test, our MLM systems correctly predicted 81.2 to 84.0% of PXR activators, 80.8 to 85.0% of hPXR activators, 61.2 to 70.3% of PXR nonactivators, and 67.7 to 73.6% of hPXR nonactivators. Our systems also correctly predicted 73.3 to 86.7% of 15 newly published hPXR activators. MLMs seem to be useful for predicting PXR activators and for providing clues to physicochemical features of PXR activation.

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

SMIREP: Predicting Chemical Activity from SMILES

Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the first step, a typically large set of fingerprints, or fragments of interest, is constructed (either by hand or by some recent data mining techniques). In the second step, machine learning techniques are applied to obtain a predictive model. The result is often not only a highly accurate but also hard to interpret model. In this paper, we demonstrate the capabilities of a novel SAR algorithm, SMIREP, which tightly integrates the fragment and model generation steps and which yields simple models in the form of a small set of IF-THEN rules. These rules contain SMILES fragments, which are easy to understand to the computational chemist. SMIREP combines ideas from the well-known IREP rule learner with a novel fragmentation algorithm for SMILES strings. SMIREP has been evaluated on three problems: the prediction of binding activities for the estrogen receptor (Environmental Protection Agency's (EPA's) Distributed Structure-Searchable Toxicity (DSSTox) National Center for Toxicological Research estrogen receptor (NCTRER) Database), the prediction of mutagenicity using the carcinogenic potency database (CPDB), and the prediction of biodegradability on a subset of the Environmental Fate Database (EFDB). In these applications, SMIREP has the advantage of producing easily interpretable rules while having predictive accuracies that are comparable to those of alternative state-of-the-art techniques.

Reproductive effects of ethnomedicinal formulation of tape-vine leaves in female rats

Documented ethno-contraceptive use of Tape-vine or Stephania japonica (THUNB.) MIERS., Syn. Stephania hernandifolia (WILLD.) WALP. leaves is evaluated with regards to post-coital pregnancy interceptive activity of its aqueous extract (AE) and an ethnomedicinal formulation (EF) in Wistar rats. EF at 500 and 250 mg/kg doses induced 66.7% and 33.3% post-coital pregnancy interception respectively and the higher dose exhibited significant reduction in number of litters born and also anti-implantation property. In contrast, none of the dose levels of AE interfered in pregnancy but significant anti-implantation property was observed at doses of 2 and 1 g/kg, even as the higher dose produced significant reduction in number of litters born as well. EF at 500 mg/kg also exhibited significant uterotrophic activity and histological changes in uterus. Pair-wise comparison of sex hormone-levels exhibited significant increment in serum estradiol, LH and FSH but decrease in progesterone levels. Assessed blood lipid-carbohydrate profile exhibited substantial decrease in glucose, cholesterol, VLDL and triglyceride contents and significant increase in HDL. It is concluded that EF probably acts as better post-coital pregnancy interceptive agent through restriction of implantation by alteration of gonadal hormone levels and decline in blood-glucose levels that possibly disrupts oxidative energy metabolism in uterus during implantation. High surge in LH and FSH suggests negligible interference in ovulatory mechanism. This preparation also seems to be free of cardiovascular risk factors. HPTLC and HPLC analysis of both EF and AE exhibited marked chemical differences.

Endocrine disruptor bisphenol A strongly binds to human estrogen-related receptor gamma (ERRgamma) with high constitutive activity

Bisphenol A (BPA) has been acknowledged as an estrogenic chemical able to interact with human estrogen receptors (ER). Many lines of evidence reveal that BPA has an impact as an endocrine disruptor even at low doses. However, its binding to ER and hormonal activity is extremely weak, making the intrinsic significance of low dose effects obscure. We thus supposed that BPA might interact with nuclear receptor(s) other than ER. Here we show that BPA strongly binds to human estrogen-related receptor gamma (ERRgamma), an orphan receptor and one of 48 human nuclear receptors. In a binding assay using [3H]4-hydroxytamoxifen (4-OHT) as a tracer, BPA exhibited a definite dose-dependent receptor binding curve with the IC50 value of 13.1 nM. 4-Nonylphenol and diethylstilbestrol were considerably weaker (5-50-fold less than BPA). When examined in the reporter gene assay for ERRgamma using HeLa cells, BPA completely preserved ERRgamma's high constitutive activity. Notably, BPA exhibited a distinct antagonist action to reverse the inverse agonist activity of 4-OHT, retaining high basal activity. ERRgamma is expressed in a tissue-restricted manner, for example very strongly in the mammalian brain during development, and in the adult in the brain, lung and other tissues. It will now be important to evaluate whether BPA's hitherto reported low dose effects may be mediated through ERRgamma.

Quantitative structure-activity relationship of various endogenous estrogen metabolites for human estrogen receptor alpha and beta subtypes: Insights into the structural determinants favoring a differential subtype binding

To search for endogenous estrogens that may have preferential binding affinity for human estrogen receptor (ER) alpha or beta subtype and also to gain insights into the structural determinants favoring differential subtype binding, we studied the binding affinities of 74 natural or synthetic estrogens, including more than 50 steroidal analogs of estradiol-17beta (E2) and estrone (E1) for human ER alpha and ER beta. Many of the endogenous estrogen metabolites retained varying degrees of similar binding affinity for ER alpha and ER beta, but some of them retained differential binding affinity for the two subtypes. For instance, several of the D-ring metabolites, such as 16 alpha-hydroxyestradiol (estriol), 16 beta-hydroxyestradiol-17 alpha, and 16-ketoestrone, had distinct preferential binding affinity for human ER beta over ER alpha (difference up to 18-fold). Notably, although E2 has nearly the highest and equal binding affinity for ER alpha and ER beta, E1 and 2-hydroxyestrone (two quantitatively predominant endogenous estrogens in nonpregnant woman) have preferential binding affinity for ER alpha over ER beta, whereas 16 alpha-hydroxyestradiol (estriol) and other D-ring metabolites (quantitatively predominant endogenous estrogens formed during pregnancy) have preferential binding affinity for ER beta over ER alpha. Hence, facile metabolic conversion of parent hormone E2 to various metabolites under different physiological conditions may serve unique functions by providing differential activation of the ER alpha or ER beta signaling system. Lastly, our computational three-dimensional quantitative structure-activity relationship/comparative molecular field analysis of 47 steroidal estrogen analogs for human ER alpha and ER beta yielded useful information on the structural features that determine the preferential activation of the ER alpha and ER beta subtypes, which may aid in the rational design of selective ligands for each human ER subtype.

Formation of estrogenic products from environmental phthalate esters under light exposure

Phthalate esters (PEs) have been suspected to be environmental endocrine disruptors and the detailed mechanism remains unclear. The activities of these chemicals can be enhanced through chemical modification under the environmental conditions. We demonstrate that PEs acquire unequivocal estrogenic activity by light exposure. Through UV exposure of an aqueous PE solution, one active photoproduct, identified as 4-hydroxyPE (PE-4OH) based on its characteristic UV and mass spectra, was detected in an estrogen receptor alpha-dependent transactivation assay. PE-4OH was effectively generated by UV 290 nm. The PE-4OH production accompanied H2O2 generation in a UV dose-dependent manner. Both PE and UV irradiation were indispensable in the generation of H2O2. Addition of H2O2 to the PE solution increased PE-4OH production under UV irradiation. The PE-4OH production was also observed in the PE reaction with the Fenton reagent generating hydroxyl radical without UV irradiation. The proposed mechanism for PE-4OH production based on these results is such that by PE-mediated photosensitization H2O2 is generated from O2 and H+ and decomposed to hydroxyl radical, thus oxidizing the PE benzene ring. The PEs-4OH are remarkably active estrogenic products of PEs and would be involved in ER-mediated endocrine disruption.

Formation of estrogenic products from benzophenone after exposure to sunlight

Benzophenone (BP) is a suspected endocrine disrupter that is found in our environment. BP undergoes metabolic and photochemical activation. In this study, photoproducts of BP were identified using high-performance liquid chromatography and mass spectrometry and their estrogenic activity was determined using both in vitro and in vivo assays. Although BP showed no estrogenic activity, two estrogenic photoproducts were detected after irradiating an aqueous solution of BP with UV or sunlight. These active products were identified as 3-hydroxy BP (BP-3OH) and 4-hydroxyBP (BP-4OH). The formation of hydrogen peroxide H2O2) was detected with increasing levels of UV, and the addition of H2O2 to the BP solution increased BP-3OH and BP-4OH production under UV irradiation. BP hydroxylation was also observed in the reaction with the Fenton reagent generating hydroxyl radical without UV irradiation. These results suggest the involvement of photochemically generated H2O2 and hydroxyl radical in the BP hydroxylation. BP-4OH was more potent than BP-3OH for promoting estrogen receptor (ER)-mediated transcription and uterotrophic activity, although both of them showed same affinity in ER binding. In conclusion, BP can be converted into ring-hydroxylated derivatives that have estrogenic activity after exposure to light.

Derivatives of Z-bisdehydrodoisynolic acid provide a new description of the binding-activity paradox and selective estrogen receptor modulator activity

Z-Bisdehydrodoisynolic acid [(+/-)-Z-BDDA], an estrogenic carboxylic acid, is highly active in vivo yet binds poorly to estrogen receptors (ERs). Studies of Z-BDDA and its enantiomers demonstrate therapeutic potential as selective ER modulators; however, the activity vs. binding paradox has remained. One possible explanation is that the carboxylic acid group of Z-BDDA may be modified in vivo to an ester or amide. Synthesis of these derivatives showed the relative binding affinity (RBA) of the methyl ester for ERalpha and ERbeta was increased approximately 14- and 20-fold, respectively, relative to the parent compound. Yet, this increased affinity did not result in increased reporter gene expression. In contrast, the amide showed an unexpected approximately 4-fold decrease in RBA to both ERs compared with the parent. The relationship among the RBAs of ester, acid, and amide is consistent with their predicted polarity, suggesting the carboxylic acid, and not the carboxylate of BDDA, binds to ERs. Studies at pH 6.5, 7.4, and 8.0 were consistent with a simple acid-base equilibrium model, with BDDA binding as the undissociated acid and with affinity equal to or exceeding that of estradiol, consistent with high in vivo potency. Furthermore, the alcohol BDD-OH also demonstrated high affinity and increased activity in gene expression assays. In addition to suggesting a resolution to the decades-old binding/activity paradox, these studies may provide a direction for definitive in vivo metabolic and pharmacokinetic studies and provide additional insight into the chemical and metabolic determinants of BBDA's unique tissue selectivity and selective ER modulator activities.

SAR and QSAR modeling of endocrine disruptors

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Metalloestrogens: an emerging class of inorganic xenoestrogens with potential to add to the oestrogenic burden of the human breast

Many compounds in the environment have been shown capable of binding to cellular oestrogen receptors and then mimicking the actions of physiological oestrogens. The widespread origin and diversity in chemical structure of these environmental oestrogens is extensive but to date such compounds have been organic and in particular phenolic or carbon ring structures of varying structural complexity. Recent reports of the ability of certain metal ions to also bind to oestrogen receptors and to give rise to oestrogen agonist responses in vitro and in vivo has resulted in the realisation that environmental oestrogens can also be inorganic and such xenoestrogens have been termed metalloestrogens. This report highlights studies which show metalloestrogens to include aluminium, antimony, arsenite, barium, cadmium, chromium (Cr(II)), cobalt, copper, lead, mercury, nickel, selenite, tin and vanadate. The potential for these metal ions to add to the burden of aberrant oestrogen signalling within the human breast is discussed.

Proteasome-independent down-regulation of estrogen receptor-alpha (ERalpha) in breast cancer cells treated with 4,4'-dihydroxy-trans-stilbene

Treatment of cells with estrogens and several pure ERalpha antagonists rapidly induces down-regulation of the alpha-type estrogen receptor (ERalpha) in the nucleus by mechanisms that are sensitive to the proteasome inhibitors, MG132 and clasto-lactacystin-beta-lactone. Hence, it is believed that these ER ligands induce down-regulation of ERalpha by proteasome-dependent mechanisms, which serve to control both the amount of transcriptional activity and the level of ligand-bound ERalpha in cells. In this study, we observed that treatment of cultured MCF-7 and T47D human breast cancer cells with the low affinity ER ligand, 4,4'-dihydroxy-trans-stilbene (4,4'-DHS), inhibited the transcriptional activity of ERalpha and induced slow and gradual decrease in the amount of ERalpha protein (henceforth referred to as down-regulation of ERalpha). The 4,4'-DHS-induced down-regulation of ERalpha in MCF-7 cells involved a mechanism that was insensitive to the two most specific proteasome inhibitors, clasto-lactacystin-beta-lactone and epoxomycin, but sensitive to MG132 at concentrations exceeding that required for maximal inhibition of the proteasome in MCF-7 cells. Therefore, 4,4'-DHS appears to induce down-regulation of ERalpha by a proteasome-independent mechanism. Here, we present data to show that both 4-OH and 4'-OH are critical for the ability of 4,4'-DHS to induce down-regulation of ERalpha and suggest that 4,4'-DHS provides a useful scaffold for development of novel ERalpha antagonists.

Quantitative structure-activity relationship based quantification of the impacts of enzyme-substrate binding on rates of peroxidase-mediated reactions of estrogenic phenolic chemicals

The initial rates of horseradish peroxidase (HRP)-mediated enzymatic reactions of 15 assorted aqueous phase phenolic chemicals were studied. The associated reaction rate constants were found to correlate quantitatively with two independent variables: the highest-occupied molecular orbital energy (E(HOMO)) defining the intrinsic redox reactivities of the phenolic substrates and the distance between a substrate and the deltaN of HIS42's imidazole ring in an HRP/substrate binding complex, obtained through molecular simulations. Highly correlated quantitative structure-activity relationship (QSAR) equations were thus developed. This work provides insights into the impacts that HRP/substrate binding may have on HRP-mediated reactions. Additionally, the QSAR equations developed in the work may serve as a basis to further explore the potential use of HRP-mediated reactions in the treatment of estrogenic contaminants, and they constitute an important tool for redesign and screening of potential proteomic modifications to the wild-type HRP structure intended to enhance reactivity toward selected substrates.

Urolithins, ellagic acid-derived metabolites produced by human colonic microflora, exhibit estrogenic and antiestrogenic activities

Urolithins A and B (hydroxy-6H-dibenzo[b,d]pyran-6-one derivatives) are colonic microflora metabolites recently proposed as biomarkers of human exposure to dietary ellagic acid derivatives. Molecular models suggest that urolithins could display estrogenic and/or antiestrogenic activity. To this purpose, both urolithins and other known phytoestrogens (genistein, daidzein, resveratrol, and enterolactone) were assayed to evaluate the capacity to induce cell proliferation on the estrogen-sensitive human breast cancer MCF-7 cells as well as the ability to bind to alpha- and beta-estrogen receptors. Both urolithins A and B showed estrogenic activity in a dose-dependent manner even at high concentrations (40 microM), without antiproliferative or toxic effects, whereas the other phytoestrogens inhibited cell proliferation at high concentrations. Overall, urolithins showed weaker estrogenic activity than the other phytoestrogens. However, both urolithins displayed slightly higher antiestrogenic activity (antagonized the growth promotion effect of 17-beta-estradiol in a dose-dependent manner) than the other phytoestrogens. The IC(50) values for the ERalpha and ERbeta binding assays were 0.4 and 0.75 microM for urolithin A; 20 and 11 microM for urolithin B; 3 and 0.02 for genistein; and 2.3 and 1 for daidzein, respectively; no binding was detected for resveratrol and enterolactone. Urolithins A and B entered into MCF-7 cells and were metabolized to yield mainly urolithin-sulfate derivatives. These results, together with previous studies regarding absorption and metabolism of dietary ellagitannins and ellagic acid in humans, suggest that the gut microflora metabolites urolithins are potential endocrine-disrupting molecules, which could resemble other described "enterophytoestrogens" (microflora-derived metabolites with estrogenic/antiestrogenic activity). Further research is warranted to evaluate the possible role of ellagitannins and ellagic acid as dietary "pro-phytoestrogens".

Lessons learnt from structural studies of the oestrogen receptor

Over the last 10 years, structural studies of the ligand-binding domains of nuclear hormone receptors have provided a wealth of information on the nature of ligand-binding and its role in receptor activation. This review examines the insights that studies on oestrogen receptor subtypes ERalpha and ERbeta have provided in terms of (1) basis of receptor activation; (2) recruitment of coregulators; (3) hormone recognition; and (4) subtype-selective ligands. It also highlights the implications for the binding of endocrine disruptors.

Environmental oestrogens, cosmetics and breast cancer

The established role of oestrogen in the development and progression of breast cancer raises questions concerning a potential contribution from the many chemicals in the environment which can enter the human breast and which have oestrogenic activity. A range of organochlorine pesticides and polychlorinated biphenyls possess oestrogen-mimicking properties and have been measured in human breast adipose tissue and in human milk. These enter the breast from varied environmental contamination of food, water and air, and due to their lipophilic properties can accumulate in breast fat. However, it is emerging that the breast is also exposed to a range of oestrogenic chemicals applied as cosmetics to the underarm and breast area. These cosmetics are left on the skin in the appropriate area, allowing a more direct dermal absorption route for breast exposure to oestrogenic chemicals and allowing absorbed chemicals to escape systemic metabolism. This review considers evidence in support of a functional role for the combined interactions of cosmetic chemicals with environmental oestrogens, pharmacological oestrogens, phyto-oestrogens and physiological oestrogens in the rising incidence of breast cancer.

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs)

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERa) was built by homology with the human estrogen receptor (hERa). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERa and the corresponding free binding energies Delta(DeltaG(bind)) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Delta(DeltaG(bind)) values was derived in order to predict which further organic pollutants are likely to bind to rtERa.

Effects of dietary daidzein and its metabolite, equol, at physiological concentrations on the growth of estrogen-dependent human breast cancer (MCF-7) tumors implanted in ovariectomized athymic mice

Genistein and daidzein are the main isoflavones in legumes. Equol is an intestinal bacterial metabolite of daidzein. In this study, we evaluated the estrogenic potential of daidzein and synthetic (+/-)-equol to stimulate growth of estrogen-dependent breast cancer (MCF-7) in vitro and in vivo. We hypothesize that estrogenic effects of daidzein and (+/-)-equol could modulate the growth of MCF-7 cells both in vitro and also once implanted into ovariectomized athymic mice. At concentrations between 0.001 and 50 microM, daidzein and (+/-)-equol stimulated the growth of MCF-7 cells with maximal stimulation at 1 muM in vitro. To evaluate their effects on the growth of MCF-7 cells implanted in ovariectomized athymic mice, two dietary dose-response studies [daidzein (125, 250, 500 and 1000 p.p.m.) and (+/-)-equol (250, 500 and 1000 p.p.m.)] were conducted. Tumor size and body weight were monitored weekly during the study. At completion of the study, we analyzed cellular proliferation of tumors using immunohistochemical staining (ki-67), pS2 expression in tumors using a real time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and total daidzein and (+/-)-equol levels in plasma using liquid chromatography-electrospray tandem mass spectrometry (LC-ES/MS/MS). Dietary daidzein had a slight but significant stimulatory effect on MCF-7 tumor growth in mice. No significant induction of pS2 mRNA (an estrogen-responsive marker) in tumors by dietary daidzein was observed. Total plasma daidzein concentrations in plasma were between 0.25 and 1.52 microM. Dietary equol treatment (for 37 weeks) did not stimulate MCF-7 tumor growth. There were no statistical differences in tumor size, proliferation and pS2 expression among any treatment groups. Total equol concentrations in plasma were 2.10-3.21 microM. In conclusion, daidzein and (+/-)-equol have proliferative effects on MCF-7 cell growth in vitro within the concentration range tested. Dietary daidzein had a slight but significant stimulatory effect on tumor growth, whereas (+/-)-equol did not stimulate the growth of estrogen-dependent breast tumor growth in athymic mice, increase the cell proliferation in tumors, or induce an estrogen-responsive pS2 expression. Total daidzein or (+/-)-equol plasma levels in mice fed the isoflavones were in the range that stimulated MCF-7 cell growth in vitro. These results suggest that pharmacokinetic and/or metabolic factors attenuate the estrogenic effects of daidzein and equol in vivo.

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.

Acteoside and martynoside exhibit estrogenic/antiestrogenic properties

Acteoside and martynoside are plant phenylpropanoid glycosides exhibiting anticancer, cytotoxic and antimetastatic activities. We investigated their potential to activate estrogen receptor isoforms ERalpha and ERbeta in HeLa cells transfected with an estrogen response element (ERE)-driven luciferase (Luc) reporter gene and an ERalpha or ERbeta expression vector. Their estrogenic/antiestrogenic effects were also assessed in breast cancer cells (MCF7), endometrial cancer cells (Ishikawa) and osteoblasts (KS483), by measuring IGFBP3 levels, cell viability and number of mineralized nodules, respectively, seeking for a natural selective estrogen receptor modulator (SERM). Acteoside and martynoside antagonized both ERalpha and ERbeta (p<0.001), whereas they reversed the effect of E(2) mainly via ERalpha (p<0.001). Martynoside was a potent antiestrogen in MCF-7 cells, increasing, like ICI182780, IGFBP3 levels via the ER-pathway. In osteoblasts, martynoside induced nodule mineralization, which was abolished by ICI182780, implicating an ER-mediated mechanism. Furthermore, its antiproliferative effect on endometrial cells suggests that martynoside may be an important natural SERM. Acteoside was an antiestrogen in breast cancer cells and osteoblasts, without any effect on endometrial cells. Our study suggests that the nature is rich in selective ERalpha and ERbeta ligands, the discovery of which may lead to the development of novel neutraceutical agents.

Estrogenicity of pyrethroid insecticidemetabolites

There is concern that insecticides are able to mimic the action of 17beta-estradiol by interaction with the human estrogen receptor. Pyrethroids are commonly used insecticides and several have been assessed for potential endocrine disrupting activity by various methods. It has been noted that some metabolites of pyrethroids, in particular, permethrin and cypermethrin, have chemical structures that are more likely to interact with the cellular estrogen receptor than the parent pyrethroid. For this study permethrin and cypermethrin metabolites 3-(4-hydroxy-3-phenoxy)benzyl alcohol, 3-(4-hydroxy-3-phenoxy)benzoic acid, and N-3-(phenoxybenzoyl)glycine were synthesised, and together with the commercially available 3-phenoxybenzyl alcohol, 3-phenoxybenzaldehyde, and 3-phenoxybenzoic acid, were studied in a recombinant yeast assay expressing human estrogen receptors (YES). Three metabolites, 3-phenoxybenzyl alcohol, 3-(4-hydroxy-3-phenoxy)benzyl alcohol, and 3-phenoxybenzaldehyde, showed estrogenic activity of approximately 10(5) less than that of 17beta-estradiol. No activity was observed in the yeast assay for 3-phenoxybenzoic acid, 3-(4-hydroxy-3-phenoxy)benzoic acid, and N-3-(phenoxybenzoyl)glycine. The results from this study show that pyrethroid metabolites are capable of interacting with the human estrogen receptor, and so might present a risk to human health and environmental well being. The impact would be expected to be small, but still add to the overall environmental xenoestrogen load.

Comparison of the global gene expression profiles produced by methylparaben,n-butylparaben and 17β-oestradiol in MCF7 human breast cancer cells

Since the alkyl esters of p-hydroxybenzoic acid (parabens) can be measured intact in the human breast and possess oestrogenic properties, it has been suggested that they could contribute to an aberrant burden of oestrogen signalling in the human breast and so play a role in the rising incidence of breast cancer. However, although parabens have been shown to regulate a few single genes (reporter genes, pS2, progesterone receptor) in a manner similar to that of 17beta-oestradiol, the question remains as to the full extent of the similarity in the overall gene profile induced in response to parabens compared with 17beta-oestradiol. The GE-Amersham CodeLink 20 K human expression microarray system was used to profile the expression of 19881 genes in MCF7 human breast cancer cells following a 7-day exposure to 5 x 10(-4) M methylparaben, 10(-5) M n-butylparaben and 10(-8) M 17beta-oestradiol. At these concentrations, the parabens gave growth responses in MCF7 cells of similar magnitude to 17beta-oestradiol. The study identified genes which are upregulated or downregulated to a similar extent by methylparaben, n-butylparaben and 17beta-oestradiol. However, the majority of genes were not regulated in the same way by all three treatments. Some genes responded differently to parabens from 17beta-oestradiol, and furthermore, differences in expression of some genes could be detected even between the two individual parabens. Therefore, although parabens possess oestrogenic properties, their mimicry in terms of global gene expression patterns is not perfect and differences in gene expression profiles could result in consequences to the cells that are not identical to those following exposure to 17beta-oestradiol.

Flavonoid content in leaf extracts of the fig (Ficus carica L.), carob (Ceratonia siliqua L.) and pistachio (Pistacia lentiscus L.)

The total flavonoid content of leaf extracts (70% ethanol) from fig (Ficus carica L.), carob (Ceratonia siliqua L.) and pistachio (Pistacia lentiscus L.) plants were determined by using reverse phase high-performance liquid chromatography (HPLC)-and analyzed by UV/VIS array and electrospray ionization (ESI)-mass spectrometry (MS) detectors. As a base for comparison, flavonoid type and level were also determined in extracts from soybeans and grape seeds. It was found that the major flavonoids in Ficus are quercetin and luteolin, with a total of 631 and 681 mg/kg extract, respectively. In Ceratonia leaves, nine different flavonoids were detected. The major one was myricetin (1486 mg/kg extract), with a similar level in Pistacia (1331 mg/kg extract, myricetin). The present study is the first to report the presence of the isoflavone genistein in the Pistacia leaf, which was discovered to consist of about a third of the genistein level detected in soybean.