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

项目摘要 在了解工程纳米材料（ENM）对环境的影响方面存在根本性差距 单核吞噬细胞系统（MPS），包括肝脏中的枯否细胞（KC）和抗原呈递细胞 树突状细胞（DC）在免疫系统中的作用。我们的长期目标是开发一个可预测的世纪 用于ENM安全性评估的毒理学平台，该平台基于细胞和器官型培养物， 内容筛选，其中我们将使用不良结果途径（AOP）来推导结构-活性 对联合体提供的ENM进行毒理学分析和决策分析的SAR。的 总体目标是使用我们的机械和高内容筛选方法来执行危害排序， 分层风险评估和SAR分析，将ENM理化性质与KC和DC中的AOP联系起来， 然后将其用作对肝脏和免疫系统的不利影响的体内预测的基础。 我们的中心假设是ENM特性与细胞内分子和病理生理学改变的联系。 MPS将允许一种机械的、高通量的方法来预测电磁导弹的危险影响 关于MPS拟议研究的基本原理是，预测和替代的发展 测试平台，包括器官型和细胞共培养模型，将允许加速风险评估， 广泛的ENM类别的分类。我们在预测毒理学方面的丰富经验 通过建模，我们建议在三个具体的方面探索财团提供的ENM对MPS的影响 目的：目的1：使用机械的、高含量的筛选方法对一种有毒物质进行危害排序和毒理学特征分析。 KC和DC中的各种ENM可用于SAR分析和预测毒理学分析，可用于 计划在肝脏微组织和动物中进行研究。目的2：建立器官型三维肝脏模型，并在活体内进行限制性研究 毒性评估、成像和生物分布研究，用于各种相关的 在KC/肝细胞界面和表达的转基因动物的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（OVA肽）树突状细胞和T细胞共培养物 系统和过继性转移，用于多种 ENM，目标1中预先筛选。我们的做法是创新的，因为我们从实质上背离了 目前的现状是，描述性单一药剂毒性测试将被快速通量取代， 内容，和AOP为基础的预测毒理学方法ENM对MPS的影响。拟议 研究是重要的，因为我们将介绍基于机制的高温超导方法，可以用来连接 ENM的理化特性对细胞和分子响应谱的影响， 静脉注射ENMs。这不仅将为加速ENM的安全性评估提供平台， 还将形成与该联盟广泛合作的基础，在该联盟中可以使用预测建模 研究暴露系统，如肺，GIT和皮肤。

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.