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: 10020409
• 负责人: Thomas F Webster
• 金额: $ 45.84万
• 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 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10020409
• 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; Diet; Disease; Distal; Dose; Environmental Health; Estrogen Receptor 2; Estrogen Receptor alpha; Estrogen Receptor beta; Estrogen receptor positive; 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; dimer; environmental chemical; improved; in vitro testing; in vivo; interest; mathematical model; novel; predictive modeling; 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.

项目总结/摘要 评估接触复杂混合物对健康的影响是NIEHS的优先事项：“必须 制定评估与复合照射有关的健康影响的方法，以尽量减少 对疾病发展的影响。”大量的潜在混合物包括环境 化学品、药物、饮食和内源性化合物。浓度增加/剂量增加（CA） 是一种广泛用于通过类似机制起作用的化合物的预测方法， 进行风险评估。然而，CA不能对含有全部和部分受体的混合物进行预测， 激动剂的作用水平高于最低有效组分的作用水平。由于部分激动剂是常见的，我们 开发了广义浓度加成（GCA）来满足这一需求。GCA已应用于系统 其中配体竞争单个受体结合位点，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，因为它们在生物学上 重要的是，启动许多毒性途径，并适合建模和快速测试。我们的整体 假设GCA适用于所有受体系统，其中配体可逆地竞争相同的 受体结合位点。基于机制信息，我们使用基于药理学的数学模型， 建模来估计混合物的生物效应;我们用经验数据来测试预测。这里我们 建议测试GCA预测更复杂受体和混合物的生物效应的能力 场景特异性目的1测试GCA预测配体混合物对受体活化的能力， 同二聚化的受体。将使用AR和ERα的报告细胞系以及 配体谱（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 使用Tox 21数据对单一化学品和混合物进行了审查。具体目标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预测下游效应，如果 该效应是受体激活的函数。这将测试ER混合物的近端和远端效应。 配体（体外）和PPARγ配体（体外和体内）。具体目标3研究了类似机制如何 必须是GCA才能申请。几个“类似”机制的模型将与经验数据进行比较：1） 含有ERα和PPARγ的选择性受体调节剂的混合物; 2）异二聚体伴侣， 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ERα配体。该项目建立在Tox 21建议的基础上， 干扰毒性途径，增加使用体外测试和计算模型，并将产生 改进混合物风险评估的一种强有力的方法。