Sulfonamides as selective oestrogen receptor β agonists

Roberts, Lee R.; Armor, Duncan; Barker, Chris; Bent, Andrew F.; Bess, Kirstin L.; Brown, Alan D.; Favor, David A.; Ellis, David; Irving, S.L.; MacKenny, Malcolm; Phillips, Chris; Pullen, Nick; Stennett, Adam; Strand, Linda P.; Styles, Michelle

doi:10.1016/j.bmcl.2011.08.041

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…The effects of all the compounds on the proliferation response of the MCF-7 estrogen-dependent cells was evaluated at different concentrations (10 À6 to 10 À9 m), and the IC 50 values are given in Table 3. The superior anti-proliferative activity of coumestrol over other members of the coumestan family [14] is in consistent with our findings that synthetic coumestans (20)(21)(22)(23)(24) having oxygenation patterns that differ from the pattern of coumestrol 1 Figure 2. Schematic representation showing the interactions between coumestrol (1) and ERb-ligand binding domain (LBD) (1U9E).…”

Section: Resultssupporting

confidence: 90%

“…Indeed, much effort is currently being invested in developing such selective estrogen-receptor modulators (SERMs). [23][24][25] Despite significant potential of coumestrol as a drug, the absence of an efficient synthetic strategy that can provide the natural product and its unnatural analogues in sufficient amounts for biology studies has frustrated any further developments. Several total syntheses of coumestrol have been reported (see Scheme 2), [26] but these usually involve multistep syntheses, which afford only small quantities of the natural product, leaving the production problem unsolved.…”

Section: Introductionmentioning

confidence: 99%

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Aerobic Iron‐Based Cross‐Dehydrogenative Coupling Enables Efficient Diversity‐Oriented Synthesis of Coumestrol‐Based Selective Estrogen Receptor Modulators

Kshirsagar

Parnes

Goldshtein

et al. 2013

Chemistry A European J

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An iron-based cross-dehydrogenative coupling (CDC) approach was applied for the diversity-oriented synthesis of coumestrol-based selective estrogen receptor modulators (SERMs), representing the first application of CDC chemistry in natural product synthesis. The first stage of the two-step synthesis of coumestrol involved a modified aerobic oxidative cross-coupling between ethyl 2-(2,4-dimethoxybenzoyl)acetate and 3-methoxyphenol, with FeCl3 (10 mol%) as the catalyst. The benzofuran coupling product was then subjected to sequential deprotection and lactonization steps, affording the natural product in 59% overall yield. Based on this new methodology other coumestrol analogues were prepared, and their effects on the proliferation of the estrogen receptor (ER)-dependent MCF-7 and of the ER-independent MDA-MB-231 breast cancer cells were tested. As a result, new types of estrogen receptor ligands having an acetamide group instead of the 9-hydroxyl group of coumestrol were discovered. Both 9-acetamido-coumestrol and 8-acetamidocoumestrol were found more active than the natural product against estrogen-dependent MCF-7 breast cancer cells, with IC50 values of 30 and 9 nM, respectively.

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Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Aerobic Iron‐Based Cross‐Dehydrogenative Coupling Enables Efficient Diversity‐Oriented Synthesis of Coumestrol‐Based Selective Estrogen Receptor Modulators

Kshirsagar

Parnes

Goldshtein

et al. 2013

Chemistry A European J

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“…Roberts et al [ 80 ] explored a series of p -hydroxybenzenesulphonamides as ERβ selective agonists. Compound 10 was identified as a compound of potential interest due to its satisfactory potency and efficacy in recombinant ligand binding domain and functional ERα and ERβ assays respectively.…”

Section: Updated Overview Of Subtype Selective Compoundsmentioning

confidence: 99%

Versatility or Promiscuity: The Estrogen Receptors, Control of Ligand Selectivity and an Update on Subtype Selective Ligands

Perkins

Tong

et al. 2014

IJERPH

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The estrogen receptors (ERs) are a group of versatile receptors. They regulate an enormity of processes starting in early life and continuing through sexual reproduction, development, and end of life. This review provides a background and structural perspective for the ERs as part of the nuclear receptor superfamily and discusses the ER versatility and promiscuity. The wide repertoire of ER actions is mediated mostly through ligand-activated transcription factors and many DNA response elements in most tissues and organs. Their versatility, however, comes with the drawback of promiscuous interactions with structurally diverse exogenous chemicals with potential for a wide range of adverse health outcomes. Even when interacting with endogenous hormones, ER actions can have adverse effects in disease progression. Finally, how nature controls ER specificity and how the subtle differences in receptor subtypes are exploited in pharmaceutical design to achieve binding specificity and subtype selectivity for desired biological response are discussed. The intent of this review is to complement the large body of literature with emphasis on most recent developments in selective ER ligands.

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“…Roberts et al found sulfonamides as selective ER-β agonists. 16 Paterni et al identified a series of new salicylketoxime derivatives that display unprecedentedly high levels of ER-β selectivity, and one compound was further proved to be active in an in vivo xenograft model of human glioma. 17 …”

Section: Introductionmentioning

confidence: 99%

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

Niu¹,

Xie²,

Wang³

et al. 2016

DDDT

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BackgroundEstrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects.MethodsHerein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.ResultsThe chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists.ConclusionThese results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.

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Sulfonamides as selective oestrogen receptor β agonists

Cited by 11 publications

References 7 publications

Aerobic Iron‐Based Cross‐Dehydrogenative Coupling Enables Efficient Diversity‐Oriented Synthesis of Coumestrol‐Based Selective Estrogen Receptor Modulators

Aerobic Iron‐Based Cross‐Dehydrogenative Coupling Enables Efficient Diversity‐Oriented Synthesis of Coumestrol‐Based Selective Estrogen Receptor Modulators

Versatility or Promiscuity: The Estrogen Receptors, Control of Ligand Selectivity and an Update on Subtype Selective Ligands

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

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