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新兴卤代污染物与人甲状腺素运载蛋白竞争相互作用的定量构效关系预测。

QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

机构信息

QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy.

出版信息

SAR QSAR Environ Res. 2013;24(4):333-49. doi: 10.1080/1062936X.2013.773374. Epub 2013 May 28.

DOI:10.1080/1062936X.2013.773374
PMID:23710908
Abstract

The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

摘要

化学物质(如多/全氟化合物 (PFCs) 和溴化阻燃剂 (BFRs))潜在内分泌干扰 (ED) 活性的测定仍然受到实验数据有限的限制。定量构效关系 (QSAR) 策略可用于填补这一数据空白,有助于表征 ED 潜力,并筛选具有危险毒理学特征的 PFCs 和 BFRs。本文提出了通过回归和分类 QSAR 模型来模拟 PFCs 和 BFRs 与 T4-TR(甲状腺素-转甲状腺素蛋白)的 T4-TTR(甲状腺素-转甲状腺素蛋白)竞争效力和相对结合效力(logT4-REP)。本研究是对以前工作的跟进,该工作分别分析了 BFRs 和 PFCs 与载体 TTR 的相互作用。新的结果表明,有可能开发出稳健且可预测的 QSAR,这些 QSAR 将 BFRs 和 PFCs 都包含在训练集中,获得的适用域比单独为 BFRs 和 PFCs 开发的现有模型更大。模型化分子描述符的选择证实了结构特征(如芳香性 OH 或分子长度)对增加研究化学品与 TTR 结合的重要性。此外,强调了一些化学物质,特别是一些 BFRs,需要进行实验测试。

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