Prediction of the Estrogen Receptor Binding Affinity for both hER&lt;sub&gt;&amp;#945;&lt;/sub&gt; and hER&lt;sub&gt;&amp;#946;&lt;/sub&gt; by QSAR Approaches

Deng, Changliang; Chen, Xuanxuan; Lu, Hongying; Yang, Xin; Luan, Feng; Cordeiro, M. Natália D. S.

doi:10.2174/15701808113109990067

Cited by 4 publications

(2 citation statements)

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“…However, several of these compounds (Heptachlor [P89], Lindane [P108], Endrin [P110], and Thiodan [P113]) have structures that deviate significantly from established structure–activity relationship (SAR) characteristics reported for estrogenic chemicals . Although quantitative SAR (QSAR) models have been previously developed for both ERα and ERβ binding, SAR approaches alone are unlikely to anticipate binding of a novel compound to ERα/β, especially if the compound structure does not fall under the established classes of known estrogenic chemicals or tested compounds . Whether these novel ER activators directly bind to the ER ligand‐binding pocket and activate the ER and ER‐dependent gene expression by the classical mechanism or whether they use a more novel mechanism (i.e., allosteric modulation or stimulation of an ER‐activating kinase activity) remains to be determined.…”

Section: Resultsmentioning

confidence: 99%

Development of a recombinant human ovarian (BG1) cell line containing estrogen receptor α and β for improved detection of estrogenic/antiestrogenic chemicals

Brennan

Bassal

et al. 2015

Enviro Toxic and Chemistry

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Estrogenic endocrine disrupting chemicals are found in environmental and biological samples, commercial and consumer products, food, and numerous other sources. Given their ubiquitous nature and potential for adverse effects, there is a critical need for rapidly detecting these chemicals. We developed an estrogen-responsive recombinant human ovarian (BG1Luc4E2) cell line recently accepted by the USEPA and OECD as a bioanalytical method to detect estrogen receptor (ER) agonists/antagonists. Unfortunately, these cells appear to contain only one of the two known ER isoforms, ERα but not ERβ, and the differential ligand selectivity of these ERs indicates that the currently accepted screening method only detects a subset of total estrogenic chemicals. To improve the estrogen screening bioassay, BG1Luc4E2 cells were stably transfected with an ERβ expression plasmid and positive clones identified using ERβ-selective ligands (genistein and Br-ERβ-041). A highly responsive clone (BG1LucERβc9) was identified that exhibited greater sensitivity and responsiveness to ERβ-selective ligands than BG1Luc4E2 cells and qRT-PCR confirmed the presence of ERβ expression in these cells. Screening of pesticides and industrial chemicals identified chemicals that preferentially stimulated ERβ-dependent reporter gene expression. Together, these results not only demonstrate the utility of this dual ER recombinant cell line for detecting a broader range of estrogenic chemicals than the current BG1Luc4E2 cell line, but screening with both cell lines allows identification of ERα and ERβ-selective chemicals.

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Section: Resultsmentioning

confidence: 99%

Development of a recombinant human ovarian (BG1) cell line containing estrogen receptor α and β for improved detection of estrogenic/antiestrogenic chemicals

Brennan

Bassal

et al. 2015

Enviro Toxic and Chemistry

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“…Quantitative structure-activity relationship (QSAR) modeling has been applied to develop estrogen receptor binding models in the past decade, as shown in Table 1 (Hong et al, 2002; Serafimova et al, 2007; Liu et al, 2008; Li and Gramatica, 2010; Taha et al, 2010; Vedani et al, 2012; Zang et al, 2013; Zhang et al, 2013, 2014; Deng et al, 2014; Ng et al, 2015). These studies have covered a wide range of modeling approaches and data set sizes, from a descriptor-based decision tree (Hong et al, 2002) to 3-D docking and multi-dimensional QSAR (Vedani et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

Ribay

Kim

Wang

et al. 2016

Front. Environ. Sci.

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Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

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A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells

Luan

Tang

Zhang

et al. 2018

Food and Chemical Toxicology

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Prediction of the Estrogen Receptor Binding Affinity for both hER<sub>α</sub> and hER<sub>β</sub> by QSAR Approaches

Cited by 4 publications

References 0 publications

Development of a recombinant human ovarian (BG1) cell line containing estrogen receptor α and β for improved detection of estrogenic/antiestrogenic chemicals

Development of a recombinant human ovarian (BG1) cell line containing estrogen receptor α and β for improved detection of estrogenic/antiestrogenic chemicals

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells

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