Toxicological Profiling of Engineered Nanomaterials (ENMs) in the MPS (RES)

项目摘要 在理解工程纳米材料(ENM)对 单核巨噬细胞系统(MPS)，包括肝脏中的枯否细胞(KC)和抗原提呈 免疫系统中的树突状细胞(DC)。我们的长期目标是发展一个可预见的21世纪 用于ENM安全评估的毒理学平台，前提是高密度的细胞和器官培养 内容筛选，其中我们将使用不良结果通路(AOPS)来推导结构-活性 对联盟提供的环境卫生物质进行毒理学分析和决策分析的关系(SARS)。这个 总体目标是使用我们的机械性和高含量筛选方法来进行危险排名， 分级风险评估，以及将ENM物理化学性质与KC和DC的AOPS联系起来的SAR分析， 然后将其用作体内预测对肝脏和免疫系统不利影响的基础。 我们的中心假设是ENM特性与分子和病理生理改变之间的联系 MPS将允许一种机械性和高通量的方法来预测ENMS的危险影响 在国会议员上。拟议研究的理由是，预测性和替代性的发展 测试平台，包括器官类型和细胞共培养模型，将允许加快风险评估和 对广义的ENM类别进行分类。在我们丰富的预测毒理学经验的指导下 在模型中，我们建议从三个具体的方面来探讨联合体提供的环境管理模式对主生产计划的影响 目标：目标1：使用机械的、高含量的筛选来进行危险分级和毒理学分析 KC和DC中各种类型的ENM，用于SAR分析和预测性毒理学分析，可用于 计划对肝脏微组织和动物进行研究。目的2：应用器官型3-D肝脏模型，并限制在体 一系列相关的毒理学图谱的毒性评估、成像和生物分布研究 对转基因动物KC/肝细胞界面和肝脏的毒理学损伤途径 报告基因(例如，NF-κB)。目的3：使用抗原特异性(卵清蛋白多肽)树突状细胞和T细胞共培养 小鼠体内毒理学和免疫毒理学研究的系统和过继转移 我们的方法是创新的，因为从本质上偏离了 目前的现状是，描述性的单剂毒性测试将被快速、高吞吐量、高 内容，以及基于AOP的ENM对MPS影响的预测毒理学方法。建议数 研究意义重大，因为我们将引入基于机制的HTS方法，这些方法可用于链接 ENMS的物理化学性质对细胞和分子响应曲线的影响 静脉注射环磷酰胺。这不仅将为加快环境管理系统的安全评估提供一个平台，而且 还将成为与该财团广泛合作的基础，在该财团中可以使用预测建模 研究肺、胃和皮肤等暴露系统。

Project Summary There is a fundamental gap in understanding the impact of engineered nanomaterials (ENM) on the mononuclear phagocyte system (MPS), including Kupffer cells (KC) in the liver and antigen-presenting dendritic cells (DC) in the immune system. Our long-term goal is to develop a predictive 21st century toxicological platform for ENM safety assessment that is premised on cellular and organotypic cultures for high content screening, in which we will use adverse outcome pathways (AOPs) to derive structure-activity relationships (SARs) for toxicological profiling and decision analysis on consortium-provided ENMs. The overall objective is to use our mechanistic and high content screening approaches to perform hazard ranking, tiered risk assessment, and SAR analysis that link ENM physicochemical properties to AOPs in KC and DC, which is then used as the basis of in vivo predictions of the adverse impact on the liver and immune system. Our central hypothesis is that linkage of the ENM properties to molecular and pathophysiological alterations in the MPS will allow a mechanistic and high throughput approach for predicting the hazardous impact of ENMs on the MPS. The rationale of the proposed research is that the development of predictive and alternative testing platforms, including organotypic and cell co-culture models, will allow expedited risk assessment and categorization of broad ENM categories. Guided by our extensive experience for predictive toxicological modeling, we propose to explore the impact of the consortium-provided ENMs on the MPS in three specific aims: Aim 1: To use mechanistic, high content screening for hazard ranking and toxicological profiling of a diverse range of ENMs in KC and DC for SAR analysis and predictive toxicological profiling that can be used to plan studies in liver micro-tissues and animals. Aim 2: To use organotypic 3-D liver models, and limited in vivo toxicity assessment, imaging and biodistribution studies for toxicological profiling of a diverse range of related to toxicological injury pathways at the KC/hepatocyte interface and the liver of transgenic animals that express reporter genes (e.g., NF-κB). Aim 3: To use an antigen-specific (OVA peptide) dendritic and T-cell co-culture system and adoptive transfer in mice for toxicological and immunotoxicological profiling of a diverse range of ENMs, prior screened in Aim 1. Our approach is innovative, because of the substantive departure from the current status quo, where descriptive single agent toxicity testing will be replaced by rapid throughput, high content, and AOP-based predictive toxicological approaches for ENM effects on the MPS. The proposed research is significant because we will introduce mechanisms-based HTS approaches that can be used to link ENMs physicochemical properties to cellular and molecular response profiles for hazard profiling of intravenous injected ENMs. Not only will this provide a platform for expedited safety assessment of ENMs, but will also form the basis of extensive collaboration with the consortium, where predictive modeling can be used to study exposure systems such as the lung, GIT and the skin.