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

Ng, Hui Wen; Perkins, Roger; Tong, Weida; Hong, Huasheng

doi:10.3390/ijerph110908709

Cited by 64 publications

(46 citation statements)

References 126 publications

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“…Consequently, this specific oxidized metabolite should be considered of higher priority for further investigation, also in respect to the possible effects mediated by ERβ. Indeed, the ERβ binding site differs from ERα in only two residues (Ng et al, 2014a) -thus a similar calculated pattern of interaction is expected -but 15-OH-ZEN might exert tissue-specific actions through the activation of both ERs isoforms in living organisms. Overall, the estrogenic potency ranking obtained by docking appears of biological significance and therefore may be applied to select the toxicological information to be used in order to characterize the hazard of oxidized ZEN metabolites through read-across.…”

Section: Discussionmentioning

confidence: 99%

Hazard assessment through hybrid in vitro / in silico approach: The case of zearalenone

Ehrlich

2015

ALTEX

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SummaryWithin the framework of reduction, refinement and replacement of animal experiments, new approaches for identification and characterization of chemical hazards have been developed. Grouping and read-across has been promoted as a most promising alternative approach. It uses existing toxicological information on a group of chemicals to make predictions on the toxicity of uncharacterized ones. In the present work, the feasibility of applying in vitro and in silico techniques to group chemicals for read-across was studied using the food mycotoxin zearalenone (ZEN) and metabolites as a case study. ZEN and its reduced metabolites are known to act through activation of the estrogen receptor α (ERα). The ranking of their estrogenic potencies appeared highly conserved across test systems including binding, in vitro and in vivo assays. This data suggests that activation of ERα may play a role in the molecular initiating event (MIE) and be predictive of adverse effects, and it provides the rationale to model receptor-binding for hazard identification. The investigation of receptor-ligand interactions through docking simulation proved to accurately rank estrogenic potencies of ZEN and reduced metabolites, showing the suitability of the model to address estrogenic potency for this group of compounds. Therefore, the model was further applied to biologically uncharacterized, commercially unavailable, oxidized ZEN metabolites (6α-, 6β-, 8α-, 8β-, 13-and 15-OH-ZEN). Except for 15-OH-ZEN, the data indicate that in general, the oxidized metabolites would be considered of lower estrogenic concern than ZEN and reduced metabolites.

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Section: Discussionmentioning

confidence: 99%

Hazard assessment through hybrid in vitro / in silico approach: The case of zearalenone

Ehrlich

2015

ALTEX

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“…Chemicals with suitable molecular weight are expected to fit the binding pocket of ER and an electronegative atom or group increases binding interaction with ER (i.e., compounds 6 , 30 and 31 ) [29]. …”

Section: Resultsmentioning

confidence: 99%

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Hong

Rua

Sakkiah

et al. 2016

IJERPH

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Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.

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“…Correlations based on a single lowestenergy pose were considerably worse for ERa (R 2 = 0.44), likely due to promiscuousness of the enzyme's binding pocket. 39 Disparity among the top docked poses is demonstrated on Formononentin ( Figure S4). For AChE, multiple-pose MC/FEP calculations yielded only marginally better correlations over single pose simulations (R 2 = 0.95 vs. 0.94); this result can be attributed to a tight fit of the relatively large inhibitors in the active site ( Figure S2).…”

Section: Methodsmentioning

confidence: 99%

Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

Clymer¹,

Vargas²,

Corcoran³

et al. 2018

Preprint

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SignificanceOver the past two decades, green chemistry has empowered sustainability science by advocating for development of chemicals, materials and processes that minimize hazard throughout their lifecycle. There is an urgent need to alleviate the economic and ethical burden of traditional toxicity testing and develop robust and systematic approaches for designing functional yet benign chemicals. While synthetic chemists regularly design for specific industrial or pharmaceutical functions, design for reduced hazard is an emerging field with few practical solutions offered to date. To this end, a new in silico framework is presented that affords accurate and economical redesign of exiting chemicals in commerce with target-specific toxicities. Bridging the fields of computer-aided drug discovery and molecular toxicology, this approach promises to dramatically lower costs associated with new chemical development; reduce time-to-market; and avoid regrettable substitutions of chemicals in products and supply chains. AbstractScreening all existing and new chemicals introduced to commerce annually using computational models is viewed as a promising alternative to the both economically and ethically unviable animal tests. The high throughput screening (HTS) assays and the many quantitative structure-activity relationships (QSARs) developed over the past decade to assess chemical exposure, hazard and risk are a testament to the shift toward cost-effective strategies to comprehensively protect human and environmental health. While existing computational tools can be used to screen chemicals for a host of toxic endpoints, their utility in informing design of safer chemicals is very limited. Being able to rationally design chemicals with minimal biological activity and optimal functional efficacy is the foundational principle of green chemistry; yet, it is also the least-developed field of this discipline. This paper outlines development of a novel computational framework for designing new or redesigning existing chemicals in commerce, while considering both their biological activity and intended function. The presented approach is based on transformed methodology from computational drug discovery, which can be applied to systematically design out target-specific toxicities based on molecular initiating events (MIE) irrespective of chemical class or toxic endpoint. Classical free energy perturbation calculations used in conjunction with Monte Carlo simulations are employed to demonstration this approach for organophosphates, a class of chemicals associated with neurotoxicity, carcinogenicity and endocrine-system disruption. The presented framework promises a strategy for safer chemical design that allows the practitioner to systematically weigh chemical drivers of toxicity, metabolism and functionality in order to determine respective trade-offs and optimal solutions to existing toxic chemicals in commerce. Complementing current predictive toxicology approaches and design guidelines, the presented framework is poised to ad...

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

Cited by 64 publications

References 126 publications

Hazard assessment through hybrid in vitro / in silico approach: The case of zearalenone

Hazard assessment through hybrid in vitro / in silico approach: The case of zearalenone

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

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