A 3D biomimetic liver sinusoid construct for predicting physiology and toxicity

描述(申请人提供)：一种用于预测生理和毒性的3D仿生肝窦结构大约90%进入第一阶段临床试验的候选药物失败，药物失败的主要原因之一是意想不到的毒性。肝脏在人体中起着中心作用，有助于体内平衡和药物的生物转化和代谢等重要功能。肝脏也是药物毒性最常见的目标。现有的体外模型和体内动物模型对人类肝脏毒性的预测能力有限。该项目的目标是构建一种模拟人类肝脏功能和反应的微流控肝脏模块，其读数旨在指示正常肝功能和毒性反应。这种人体肝脏模型有望成为模拟人体暴露的基本消除器官，提供更好的药物诱导肝脏毒性预测，并作为药物发现的疾病模型。我们的方法将是开发一个由人类肝细胞、Kupffer细胞、星状细胞和内皮细胞组成的3D微流体系统，以模拟肝脏最小的功能单位--肝腺泡。该模型的一个独特特征是介质的氧合，以及氧梯度的建立，这被认为是正常人类肝脏正弦空间中肝细胞的重要代谢、基因表达和功能异质性的原因。富氧区的肝细胞具有高效的氧化代谢、脂肪酸氧化、糖异生、胆汁酸提取、氨解尿素和谷胱甘肽结合等功能，而缺氧区的肝细胞具有高效的糖酵解、脂肪生成和细胞色素P-450生物转化功能。该模型的另一个独特之处将是加入“前哨”生物传感器细胞，这是一小部分细胞，带有指示细胞功能变化的工程生物传感器。当与其他荧光探针、标准生化和质谱学读数相结合时，该模型将提供实时高含量分析(HCA)配置文件，以监测器官功能和反应。读数的选择和验证以及模型的性能将基于一组具有可用临床数据的参考药物进行评估。为了便于比较，将建立一个包含临床数据的药物数据库，以及来自其他体外和体内研究的数据。该项目的最终目标是开发一个人体肝脏功能的微流体模型，该模型将与一系列其他人体器官模块集成，创建一个复制人类临床试验结果的微生理学平台，并为药物开发提供更好的暴露、安全性和有效性的可预测性。由于药物代谢的结果，肝脏在人类药物相互作用中发挥着核心作用，无论是在肝脏内还是在其他器官中。肝脏模块的性能是微生理平台性能的核心。我们相信，这里提出的设计将在该平台上以最佳方式概括人体肝脏功能。

DESCRIPTION (provided by applicant): A 3D biomimetic liver sinusoid construct for predicting physiology and toxicity Approximately 90% of drug candidates entering Phase 1 clinical trials fail, and one of the main reasons for drug failure is unexpected toxicity. The liver plays a centra role in the human body, contributing to homeostasis and important functions such as biotransformation and metabolism of drugs. The liver is also the most common target for drug-induced toxicity. Existing in vitro models and in vivo animal models have limited predictive power for human liver toxicity. The goal of this project is to construct a microfluidic liver modul which mimics the functions and responses of the human liver, with readouts designed to indicate both normal liver function and toxic responses. This human liver model is expected to be the essential elimination organ for modeling human exposure, provide improved predictions of drug induced liver toxicity, and also serve as a disease model for drug discovery. Our approach will be to develop a 3D microfluidic system with human hepatocyte, kupffer, stellate and endothelial cells, to mimic the liver acinus - the smallest functional unit of the liver. A uniue feature of the model will be the oxygenation of the media, and the establishment of an oxygen gradient, which is believed to account for important metabolic, gene expression and functional heterogeneity of the hepatocytes in the sinusoidal space of normal human liver. Hepatocytes in the oxygen rich zone are efficient in oxidative metabolism, fatty acid oxidation, gluconeogenesis, bile acid extraction, ammonia detoxification to urea and glutathione-conjugation while hepatocytes in the oxygen depleted zone are efficient in glycolysis, liponeogenesis and Cytochrome P-450 biotransformation. Another unique feature of the model will be the incorporation of 'sentinel' biosensor cells, a small fraction of cells with engineered biosensors that indicate changes in cellular functions. When combined with other fluorescent probes, standard biochemical and mass spectroscopy readouts, the model will provide a real-time High Content Analysis (HCA) profile to monitor organ function and response. The selection and validation of readouts and performance of the model will be evaluated based on a panel of reference drugs with available clinical data. To facilitate that comparison, a database of drugs with clinical data, and data from other in vitro and in vivo studies will be constructed. The ultimate goal of this project is to develop a microfluidic model of human liver function that will integrate with a series of other human organ modules, to create a microphysiology platform that reproduces human clinical trial results and provides improved predictivity of exposure, safety and efficacy for drug development. The liver plays a central role in human drug interactions, both within the liver and in other organs, as a result of drug metabolism. The performance of the liver module is central to the performance of the microphysiology platform. We believe the design proposed here will optimally recapitulate human liver function on that platform.