In Vitro Gene Expression Model Predicting Drug Induced Liver Disease

描述（由申请人提供）：磷脂病（PLDosis）或脂质代谢紊乱是许多药物性肝脏疾病的一个因素。药物诱导的、剂量依赖性的、基因组表达的改变，无论是直接原因还是影响，都可以提供功能性表型效应（例如不良代谢）的分子特征。PLDosis的最终鉴定是基于电子显微镜，但基于基因表达和荧光脂质染色的高通量评估PLDosis方法已经发表。大多数系统使用细胞系，尽管有些使用次优化培养的原代肝细胞，但没有一个系统提供用于QSAR的精确剂量反应数据。众所周知，细胞系缺乏原代肝细胞和肝脏中发现的代谢途径。在这个I期SBIR项目中，我们将一种测量基因表达的新方法- qnpa -与最佳培养（三明治培养）的人原代肝细胞结合起来，将其与Hep-G2系统进行比较，并分析所有（~86）个与PLDosis相关的基因，这些基因来自先前报道的大量（~63）参考化合物。我们将评估之前用于基因表达（24小时）、尼罗河红（48小时）和电子显微镜（72小时）的时间点上的基因表达，以便对结果进行直接比较。我们将对所有化合物的所有基因进行剂量反应测量，验证该分析可以为QSAR提供有用的EC50数据。最后，将选择一组重点基因作为PLDosis标记，并评估原代肝细胞对HEp-G2和基于qnpa的基因表达对Nile Red的益处。如果成功，我们将提交II期申请，以扩展模型，包括miRNA和其他可能更具选择性的基因，并包括其他肝脏疾病的基因特征识别。公共卫生相关性：磷脂病（PLDosis）或脂质代谢紊乱是许多药物性肝病的一个因素。在这个I期SBIR项目中，我们建议将基因表达方法、定量核酸酶保护试验（qNPA）与三明治培养的原代肝细胞结合起来，为在药物发现过程的早期识别PLDosis提供一个敏感的体外模型。qNPA提供了比PCR更精确的水平。原代肝细胞具有肝细胞代谢和药物转运的所有机制，而Hep-G2等细胞系则不具备；因此，我们的模型应该是一个大大改进的、更相关的细胞系统。我们将跨时间点验证其他基因，这些时间点涵盖了其他评估PLDosis方法所使用的时间点，以证明能够提供精确的剂量反应数据和EC50值，药物化学家可以使用这些数据来“设计”化学系列的PLDosis责任。我们的目标是生成所需的数据，以确定使用qNPA基因表达/原代肝细胞模型与使用其他方法相比的优势。在第二阶段，我们将改进模型并将其扩展到其他肝脏疾病，大鼠原代肝细胞系统，以及使用循环血细胞的体内生物标志物系统。

DESCRIPTION (provided by applicant): Phospholipidosis (PLDosis) or lipid metabolism disorders are a factor in many drug induced liver diseases. Drug induced, dose-dependent, alterations in the expression of sets of genes, whether direct cause or effect, can provide a molecular signature of the functional phenotypic effect(s) (e.g. adverse metabolism). Definitive identification of PLDosis is based on electron microscopy, but high throughput methods to assess PLDosis have been published based on gene expression and fluorescent lipids staining. Most systems use cell lines, though some have used sub-optimally cultured primary hepatocytes, but none provides precise dose response data used for QSAR. It is known that cell lines lack metabolic pathways found in primary hepatocytes and the liver. In this Phase I SBIR program, we combine a novel method for measuring gene expression-qNPA--with optimally cultured (sandwich-cultured) human primary hepatocytes, compare this to a Hep-G2 system, and profile all the (~86) genes that have been linked to PLDosis across a large set (~63) of reference compounds taken from previous reports. We will evaluate gene expression across the time points used previously for gene expression (24hr) Nile Red (48 hr) and electron microscopy (72 hr) so that a direct comparison of results can be made. We will perform dose response measurements for all the genes for all the compounds, validating that the assay can provide EC50 data useful for QSAR. Finally, a focused set of genes will be selected as a PLDosis signature, and the benefit of primary hepatocytes vs HEp-G2 and of qNPA-based gene expression versus Nile Red will be evaluated. If successful, we will submit a Phase II application to expand the model to include miRNA and additional, potentially more selective genes, and to include identification of gene signatures for other liver diseases. PUBLIC HEALTH RELEVANCE: Phospholipidosis (PLDosis) or lipid metabolism disorders are a factor in many drug induced liver diseases. In this Phase I SBIR program, we propose to combine a gene expression method, the quantitative Nuclease Protection Assay (qNPA) with sandwich-cultured primary hepatocytes to provide a sensitive in vitro model for identifying PLDosis early in the drug discovery process. qNPA provides a level of precision superior to that of PCR. Primary hepatocytes have all the mechanisms of liver cell metabolism and drug transport intact, while cell lines such as Hep-G2 do not; therefore, our model should be a greatly improved and more relevant cell system. We will validate additional genes across time points that cover the time points used by other methods of assessing PLDosis to demonstrate the ability to provide precise dose response data and EC50 values that can be used by medicinal chemists to "design out" the PLDosis liability of chemical series. Our goal is to generate the data required to determine what the advantages of using the qNPA gene expression/primary hepatocyte model are compared to using other methods. In Phase II we will improve the model and extend it to other liver diseases, to a rat primary hepatocyte system, and to an in vivo biomarker system using circulating blood cells.