Novel Analytical and Experimental Approaches for Predicting the Biological Effects of Mixtures

预测混合物生物效应的新分析和实验方法

• 批准号: 10200039
• 负责人: Thomas F Webster
• 金额: $ 45.91万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10200039
• 关键词: Address; Agonist; Androgen Receptor; Aromatase Inhibitors; Binding Sites; Biological; Cell Culture System; Cell Culture Techniques; Cell Line; Cell Proliferation; Chemicals; Complex; Complex Mixtures; Computer Models; Data; Development; Disease; Distal; Dose; Environmental Health; Estrogen Receptor 2; Estrogen Receptor alpha; Estrogen Receptor beta; Estrogens; Exposure to; Foundations; GREB1 gene; Genes; Health; Homodimerization; In Vitro; Individual; Ligand Binding; Ligands; MCF7 cell; Methods; Modeling; Molecular Mechanisms of Action; Mus; National Institute of Environmental Health Sciences; Outcome; PPAR gamma; Pathway interactions; Pharmacologic Substance; Pharmacology; Physiological; Process Assessment; RXR; Receptor Activation; Recommendation; Reporter; Response Elements; Risk; Risk Assessment; System; Testing; Toxic effect; Toxicity Tests; Uncertainty; United States National Institutes of Health; Work; adipocyte differentiation; aryl hydrocarbon receptor ligand; base; biological systems; density; dietary; dimer; environmental chemical; improved; in vitro testing; in vivo; interest; mathematical model; novel; predictive modeling; rapid testing; receptor; receptor binding; receptor function; response

Project Summary / Abstract Assessing the health effects of exposure to complex mixtures is a priority for NIEHS: “It is imperative to develop methods to assess the health effects associated with complex exposures in order to minimize their impact on the development of disease.” The vast number of potential mixtures includes environmental chemicals, pharmaceuticals, dietary and endogenous compounds. Concentration addition/dose addition (CA) is a predictive method widely used for compounds that act by similar mechanisms and provides a foundation for risk assessment. However, CA cannot make predictions for mixtures that contain full and partial receptor agonists at effect levels above that of the least efficacious component. Since partial agonists are common, we developed Generalized Concentration Addition (GCA) to address this need. GCA has been applied to systems where ligands compete for a single receptor binding site, successfully predicting experimental data for mixtures of AhR ligands and of PPARγ ligands. This project focuses on ligand-receptor systems as they are biologically important, initiate many toxicity pathways, and are amenable to modeling and rapid testing. Our overall hypothesis is that GCA applies to all receptor systems in which ligands reversibly compete for the same receptor binding sites. Based on mechanistic information, we use pharmacologically-based mathematical modeling to estimate the biological effect of mixtures; we test the predictions with empirical data. Here, we propose to test the ability of GCA to predict the biological effects of more complex receptors and mixture scenarios. Specific Aim 1 tests the ability of GCA to predict receptor activation by mixtures of ligands for receptors that homodimerize. The predictions will be tested using reporter cell lines for AR and ERα and a spectrum of ligands (full agonists, partial agonists, competitive antagonists). Applicability of GCA will be further examined using Tox21 data for single chemicals and mixtures. Specific Aim 2 tests the ability of GCA to predict mixture effects for downstream biological endpoints. We hypothesize that GCA predicts a downstream effect if the effect is a function of receptor activation. This will be tested for proximal and distal effects of mixtures of ER ligands (in vitro) and PPARγ ligands (in vitro and in vivo). Specific Aim 3 examines how similar mechanisms must be for GCA to apply. Models for several “similar” mechanisms will be compared with empirical data: 1) mixtures that contain selective receptor modulators for ERα and PPARγ; 2) heterodimer partners that each bind ligands (ERα:ERβ, PPARγ:RXR) and 3) mixtures containing an aromatase inhibitor (altering the amount of natural ligand) plus ERα ligands. This project builds upon the Tox21 recommendations of examining perturbations of toxicity pathways, increased use of in vitro testing and computational models and will generate a powerful approach for improving risk assessment of mixtures.

项目摘要 /摘要 评估暴露于复杂混合物的健康影响是NIEHS的优先事项：“必须 开发方法来评估与复杂暴露相关的健康影响，以最大程度地减少其 对疾病发展的影响。”大量潜在混合物包括环境 化学物质，药物，饮食和内源性化合物。浓度添加/剂量添加（CA） 是一种广泛用于通过相似机制起作用的化合物并提供基础的预测方法 进行风险评估。但是，CA无法对包含完整和部分接收器的混合物做出预测 效应水平高于效率最低的成分的激动剂。由于部分激动剂很普遍，我们 开发了普遍的浓度添加（GCA）来满足这一需求。 GCA已应用于系统 配体竞争单个接收器结合位点，成功预测了混合物的实验数据 AHR配体和PPARγ配体的。该项目侧重于配体受体系统，因为它们是生物学上的 重要的是，启动许多毒性途径，并且可以进行建模和快速测试。我们的整体 假设是GCA适用于所有受体系统，其中配体可逆地竞争相同 接收器绑定位点。基于机械信息，我们使用基于药品的数学 建模以估计混合物的生物学效应；我们使用经验数据测试预测。在这里，我们 提议测试GCA预测更复杂受体和混合物的生物学作用的能力 方案。特定的目标1测试GCA预测配体混合的受体激活的能力 接收器同二聚体。这些预测将使用AR和ERα的报告细胞系和A进行测试 配体频谱（完整的激动剂，部分激动剂，竞争性拮抗剂）。 GCA的适用性将进一步 使用TOX21数据进行了单个化学物质和混合物的检查。特定的目标2测试GCA预测的能力 下游生物学终点的混合效应。我们假设GCA可以预测，如果 效果是受体激活的函数。这将测试ER混合物的近端和异常作用 配体（体外）和PPARγ配体（体外和体内）。特定的AIM 3考试如何使用类似的机制 必须申请GCA。将几种“类似”机制的模型与经验数据进行比较：1） 包含用于ERα和PPARγ的选择性受体调节剂的混合物； 2）异二聚体合作伙伴每个 结合配体（ERα：ERβ，PPARγ：RXR）和3）包含芳香酶抑制剂的混合物（改变量 天然配体）加ERα配体。该项目基于TOX21检查的建议 毒性途径的扰动，增加体外测试和计算模型的使用，并将产生 改善混合物风险评估的有力方法。

项目成果

Generalized concentration addition for ligands that bind to homodimers.

与同型二聚体结合的配体的广义浓度添加。

• DOI: 10.1016/j.mbs.2019.108214
• 发表时间: 2019
• 期刊: Mathematical biosciences
• 影响因子: 4.3
• 作者: Webster,ThomasF;Schlezinger,JenniferJ
• 通讯作者: Schlezinger,JenniferJ

Characterization of adipogenic, PPARγ, and TRβ activities in house dust extracts and their associations with organic contaminants.

• DOI: 10.1016/j.scitotenv.2020.143707
• 发表时间: 2021-03-01
• 期刊: The Science of the total environment
• 作者: Kassotis CD;Hoffman K;Phillips AL;Zhang S;Cooper EM;Webster TF;Stapleton HM
• 通讯作者: Stapleton HM

Unknown Organofluorine Mixtures in U.S. Adult Serum:Contribution from Pharmaceuticals?

• DOI: 10.3390/toxics11050416
• 发表时间: 2023-04-27
• 期刊: Toxics
• 影响因子: 4.6
• 作者: Pennoyer EH;Heiger-Bernays W;Aro R;Yeung LWY;Schlezinger JJ;Webster TF
• 通讯作者: Webster TF

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro.

• DOI: 10.1016/j.tox.2021.153024
• 发表时间: 2022-01-15
• 期刊: Toxicology
• 影响因子: 4.5
• 作者: Nielsen G;Heiger-Bernays WJ;Schlezinger JJ;Webster TF
• 通讯作者: Webster TF

Young children's exposure to phenols in the home: Associations between house dust, hand wipes, silicone wristbands, and urinary biomarkers.

• DOI: 10.1016/j.envint.2020.106317
• 发表时间: 2021-03
• 期刊: Environment international
• 影响因子: 11.8
• 作者: Levasseur JL;Hammel SC;Hoffman K;Phillips AL;Zhang S;Ye X;Calafat AM;Webster TF;Stapleton HM
• 通讯作者: Stapleton HM