Relative Binding Affinity For Estrogen Receptor Research Articles

Applications of in silico methods to analyze the toxicity and estrogen receptor-mediated properties of plant-derived phytochemicals

A myriad of phytochemicals may have potential to lead toxicity and endocrine disruption effects by interfering with nuclear hormone receptors. In this examination, the toxicity and estrogen receptor−binding abilities of a set of 2826 phytochemicals were evaluated. The endpoints mutagenicity, carcinogenicity (both CAESAR and ISS models), developmental toxicity, skin sensitization and estrogen receptor relative binding affinity (ER_RBA) were studied using the VEGA QSAR modeling package. Alongside the predictions, models were providing possible information for applicability domains and most similar compounds as similarity sets from their training sets. This information was subjected to perform the clustering and classification of chemicals using Self−Organizing Maps. The identified clusters and their respective indicators were considered as potential hotspot structures for the specified data set analysis. Molecular screening interpretations of models were exhibited accurate predictions. Moreover, the indication sets were defined significant clusters and cluster indicators with probable prediction labels (precision). Accordingly, developed QSAR models showed good predictive abilities and robustness, which observed from applicability domains, representation spaces, clustering and classification schemes. Furthermore, the designed new path could be useful as a valuable approach to determine toxicity levels of phytochemicals and other environmental pollutants and protect the human health.

SMILES-based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation balance

Quantitative structure—activity relationships for model of estrogen receptor relative binding affinity (pRBA) have been built. These models are one-variable correlations between pRBA and optimal descriptor calculated with simplified molecular input line entry system. These models were obtained by means of the correlation balance: one subset of the training set (sub-training set) plays role of the training; the second subset (calibration set) plays role of the preliminary check of the models. Three splits into the sub-training set, calibration set, and external test set were examined. It has been shown that the correlation balance is a more robust predictor for the endpoint than classic scheme (training set–test set: without of the calibration). The statistical characteristics of the model are n = 59, r2 = 0.8792, s = 0.643, F = 415 (sub-training set); n = 39, r2 = 0.8805, s = 0.637, F = 273 (calibration set); and n = 31, r2 = 0.8132, s = 0.781, F = 126 (test set).

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17 g -estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.

Structure–Activity Relationships for Triphenylethylene Antiestrogens on Hepatic Phase-I and Phase-II Enzyme Expression

To better understand the mechanism(s) by which tamoxifen induces rat hepatic CYPIIB2 and suppresses GSTA1, structure–activity studies were performed. Compounds employed in these studies included: tamoxifen, fixed-ring tamoxifen, ethylated fixed-ring tamoxifen, pyrrolidino-tamoxifen, 4-iodotamoxifen, idoxifene, and toremifene. With respect to GSTA1 suppression, tamoxifen, fixed-ring tamoxifen, 4-iodotamoxifen, idoxifene, and toremifene were all potent suppressors of GSTA1, while ethylated fixed-ring tamoxifen and pyrrolidino-tamoxifen were completely without activity. The results suggest that the aminoethoxy side chain plays a crucial role in GSTA1 suppression, and that 4-iodination may potentiate this activity. With respect to induction of CYPIIB2, tamoxifen, fixed-ring tamoxifen, and ethylated fixed-ring tamoxifen were inducers of this enzyme, while toremifene and 4-iodotamoxifen were inactive, suggesting that the aminoethoxy side chain is not a structural determinant of CYPIIB2 induction. Because ethylated fixed-ring tamoxifen, toremifene, and 4-iodotamoxifen had differential activities in the two assays, we conclude that CYPIIB2 induction and GSTA1 suppression by triphenylethylenes are the result of two separate and distinct mechanistic pathways. Structure–activity relationships for GSTA1 suppression and CYPIIB2 induction were compared with previously published relationships for triphenylethylene: 1) estrogen receptor relative binding affinity; 2) calmodulin antagonism; 3) antiuterotrophic activity; and 4) antagonism of MCF-7 cell growth. No clear correlation was observed between the effects on CYPIIB2 and these other four activities, suggesting no relationship between the mechanisms responsible for these effects. Similarly, no precise correlation was observed between GSTA1 suppression and these other activities, although rough similarities were observed for relative binding affinity and antiuterotrophic activity. This suggests that the mechanisms responsible for CYPIIB2 induction and GSTA1 suppression are not related to the mechanisms of action for these other documented activities, and may represent different mechanistic pathways.

Transcriptional regulation of estrogen-responsive genes by non-steroidal estrogens: Doisynolic and allenolic acids

Estrogen receptor (ER), a member of the nuclear receptor superfamily, exerts prominent physiological roles in both humans and other species by acting directly as a transcription factor, altering nuclear gene expression. One peculiarity of estrogenic regulation is that it is affected by a wide variety of non-steroidal compounds in addition to the natural hormone, estradiol. Doisynolic and allenolic acid compounds are non-steroidal compounds that act as potent estrogens in animal studies, yet bind to ER extremely poorly in competitive binding assays, raising the possibility of alternative molecular mechanisms for the observed estrogenic effects. In this work we demonstrate that (±)- Z-bisdehydrodoisynolic acid, (±)- Z-bisdehydrodoisynolic acid 3-methyl ether, and (−) allenolic acid can interact directly with ER. These compounds all serve as ligands for ER in mechanism-specific tissue culture-based reporter gene assays for both positive and negative gene regulation. We have also used a novel assay based on electromobility shift by ER for directly determining relative binding affinities for ER. In addition, we show cell-type-specific activity differences for (±)- Z-bisdehydrodoisynolic acid 3-methyl ether, supporting clinical observations indicating a higher potency of this compound in female animals than in humans.

Multivariate analysis by the minimum spanning tree method of the structural determinants of diphenylethylenes and triphenylacrylonitriles implicated in estrogen receptor binding, protein kinase C activity, and MCF7 cell proliferation

The response profiles of 36 para-substituted diphenylethylenes (DPEs) and triphenylacrylonitriles (TPEs) have been compared by multivariate analysis. The responses measured were (a) relative binding affinity (RBA) for the cytosol estrogen receptor (ER), (b) ability to promote the growth of the human MCF7 breast cancer cell-line, (c) cytotoxicity in MCF7 cells, and (d) ability to stimulate or inhibit protein kinase C (PKC) III activity under three different conditions of enzyme activation. The prime object of the analysis was to observe the simultaneous influence of diverse combinations of substituents on all these in vitro responses. To do this, the minimum spanning tree (MST) method was used to organize the molecules into a network in which proximate molecules are closely related with regard to their responses whereas remote molecules are distinct. The MST of this population of molecules had four main branches. E2 and its TPE mime were located in a central position within the trunk whereas the tips of the branches tended toward molecules of different specificity, i.e., cytotoxic molecules that bind to ER and interfere with PKC, noncytotoxic molecules that also bind to ER and interfere with PKC but promote cell growth, molecules only active on PKC, and molecules active on all parameters except PKC stimulation. A parallel MST analysis of the relationships among the response parameters themselves confirmed previous conclusions: For this population of molecules, RBAs for ER are fairly closely related to ability to promote MCF7 cell growth and only little to cytotoxicity (Bignon et al. J. Med. Chem. 1989, 32, 2092). Cytotoxicity is much more clearly correlated with inhibition of diacylglycerol-stimulated PKC activity than with RBAs for ER. PKC inhibition differs substantially depending upon whether the substrate is H1 histone or protamine sulfate.