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

项目摘要/摘要 评估暴露在复杂混合物中对健康的影响是NIEHS的优先事项： 制定评估与复杂暴露相关的健康影响的方法，以最大限度地减少其 对疾病发展的影响。大量潜在的混合物包括环境 化学品、药品、饮食和内源性化合物。浓度添加/剂量添加(CA) 是一种广泛用于作用机理相似的化合物的预测方法，并提供了一个基础 用于风险评估。然而，CA不能对含有全部和部分受体的混合物进行预测 有效水平高于最低有效成分的激动剂。由于部分激动剂很常见，我们 开发了通用浓度添加(GCA)来满足这一需求。已将GCA应用于系统 在配体竞争单一受体结合位置的情况下，成功预测混合物的实验数据 AHR配体和PPARγ配体。这个项目的重点是配体-受体系统，因为它们是生物学上的 重要的是，启动了许多毒性途径，并易于建模和快速测试。我们的整体 假设GCA适用于配体可逆竞争相同的所有受体系统 受体结合部位。基于机械性信息，我们使用基于药理学的数学 为估计混合物的生物效应而建立的模型；我们用经验数据检验预测。在这里，我们 建议测试GCA预测更复杂的受体和混合物的生物效应的能力 场景。特异性目标1测试GCA预测配体混合物激活受体的能力 同源二聚体的受体。这些预测将使用AR和ERα的报告细胞系以及 配体的光谱(完全激动剂、部分激动剂、竞争性拮抗剂)。GCA的适用性将进一步提高 使用Tox21数据对单一化学品和混合物进行了检查。《特定目标2》测试GCA的预测能力 下游生物终点的混合效应。我们假设GCA预测了下游效应，如果 这种效应是受体激活的一种功能。这将测试ER混合物的近端和远端效应 (体外)和PPARγ配体(体外和体内)。《特定目标3》考察了类似的机制如何 必须是GCA才能申请。几种“相似”机制的模型将与经验数据进行比较：1) 含有ERα和PPARγ选择性受体调节剂的混合物；2)每个 结合配体(ERα：ERβ，PPARγ：Rxr)和3)含有芳香酶抑制剂的混合物(改变量 天然配体)加上ERα配体。该项目建立在Tox21建议的基础上 毒性途径的扰动，更多地使用体外测试和计算模型，并将产生 一种改进混合物风险评估的有效方法。

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.