QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, via J.H. Dunant 3, 21100 Varese, Italy.
J Hazard Mater. 2024 Dec 5;480:136188. doi: 10.1016/j.jhazmat.2024.136188. Epub 2024 Oct 16.
The use of New Approach Methodologies (NAMs), such as Quantitative Structure-Activity Relationship (QSAR) models, is highly recommended by international regulations to speed up hazard and risk assessment of Endocrine Disruptors, which are known to be linked to a wide spectrum of severe diseases on humans and wildlife. A very sensitive target for these chemicals is the thyroid hormone system, which plays a key role in regulating metabolic and cognitive functions. Several chemicals have been demonstrated to compete with the thyroid hormone thyroxine (T4) for binding to human thyroid hormone distributor protein transthyretin (hTTR). In this work, we generated three new datasets composed by T4-hTTR competing potencies of more than 200 heterogeneous chemicals measured by three different in vitro assays. These datasets were used for the development of new regression QSAR models. The best models were thoroughly validated by internal and external validation procedures. The mechanistic interpretation of the selected molecular descriptors provided information on structural features which are relevant to characterise hTTR binders, such as the presence of hydroxylated and halogenated aromatic rings. PCA analysis was used to rank the studied chemicals according to their increasing T4-hTTR competing potency. Hydroxylated and halogenated bicyclic aromatic compounds are ranked as the strongest hTTR binders. The new QSARs are useful to screen potential Thyroid Hormone System-Disrupting Chemicals (THSDCs), and to support the identification of sustainable alternatives to hazardous chemicals.
新方法（NAMs）的使用，如定量构效关系（QSAR）模型，被国际法规强烈推荐用于加速内分泌干扰物的危害和风险评估，这些物质已知与人类和野生动物的广泛严重疾病有关。这些化学物质的一个非常敏感的靶标是甲状腺激素系统，它在调节代谢和认知功能方面起着关键作用。已经证明，有几种化学物质能够与甲状腺激素甲状腺素（T4）竞争结合人甲状腺激素分布蛋白转甲状腺素（hTTR）。在这项工作中，我们生成了三个新的数据集，这些数据集由三种不同的体外测定方法测量的 200 多种异构化学物质的 T4-hTTR 竞争能力组成。这些数据集用于开发新的回归 QSAR 模型。通过内部和外部验证程序对最佳模型进行了彻底验证。所选分子描述符的机制解释提供了与表征 hTTR 结合物相关的结构特征的信息，例如存在羟基化和卤化芳环。PCA 分析用于根据 T4-hTTR 竞争能力对研究中的化学物质进行排序。羟基化和卤化双环芳烃化合物被评为最强的 hTTR 结合物。新的 QSAR 可用于筛选潜在的甲状腺激素系统破坏化学物质（THSDCs），并支持识别危险化学品的可持续替代品。
