Human Stem Cells for Toxicity Screening

用于毒性筛选的人类干细胞

• 批准号: 7140203
• 负责人: Timothy R. Zacharewski
• 金额: $ 36.86万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-23 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7140203
• 关键词: NIH Roadmap Initiative tag; cell line; cell proliferation; chromatin immunoprecipitation; drug adverse effect; drug discovery /isolation; drug screening /evaluation; gene expression profiling; human genetic material tag; kidney cell; laboratory mouse; laboratory rat; liver cells; microarray technology; stem cells; technology /technique development; toxicant screening

DESCRIPTION (provided by applicant): Completion of the human genome sequence, recent advances in elucidating the molecular mechanisms of disease, and the emergence of combinatorial chemistry, high-throughput screening and "omic" technologies have provided unprecedented opportunities to discover new therapeutic agents. However, many candidate agents are failing due to poor efficacy in humans, unfavorable pharmacodynamic properties, unacceptable adverse effects, and major unpredicted toxicities. Current nonclinical safety assessments are costly, time consuming, utilize large amounts of agent and involve significant numbers of animals, making it impractical to evaluate the toxicity of large numbers of new chemical entities (NCEs) early in the drug development pipeline. Moreover, typical toxicity studies fail to reliably predict potential safety problems in humans, a leading cause of costly clinical trial attrition. Consequently, new economical approaches to efficiently determine the absorption, distribution, metabolism, excretion and toxicity (ADMET) are required that can be integrated earlier in the drug development pipeline in order to identify NCEs that have a greater likelihood of success in clinical trials. This high-risk and high-impact proposal will establish the utility of adult human liver and kidney stem cells as in vitro high-throughput models for receptor-mediated toxicity. Estrogen, peroxisome proliferator-activated, aryl hydrocarbon and pregnane X receptor mediated activities will be investigated to comparatively assess adult human liver and kidney stem cell responses to in vivo elicited rodent effects. Dose- and time-dependent gene expression profiling using orthologous human, mouse and rat cDNA microarrays will be used in conjunction with traditional (e.g. histopathology, cell proliferation, clinical chemistry), molecular (e.g. chromatin immunoprecipitation) and computational approaches (e.g. response element searches) to elucidate conserved and divergent responses. All data will be managed within dbZach (http://dbzach.fst.msu.edu) a MIAME-compliant modular toxicogenomic supportive relational database. Regulatory pathways and networks will be reconstructed by computationally integrating this disparate data using advanced statistical approaches (e.g. genetic algorithm/partial least squares framework) and comparatively examined in order to identify putative mechanistically-based cross-species conserved biomarkers that are amendable to high throughput screening, and predictive of in vivo human toxicity. Once validated, these adult stem cell models and mechanistically-based biomarkers could be employed in optimizing, ranking and prioritizing new lead compounds. Moreover, this proposal will also develop computational tools that will improve knowledge management and decision-making during drug discovery and development.

描述（由申请人提供）： 人类基因组序列的完成、阐明疾病分子机制的最新进展以及组合化学、高通量筛选和“组学”技术的出现，为发现新的治疗药物提供了前所未有的机会。然而，许多候选药物由于对人体疗效差、药效学特性不利、不可接受的副作用以及不可预测的主要毒性而失败。目前的非临床安全性评估成本高昂、耗时、使用大量药物并涉及大量动物，因此在药物开发流程的早期评估大量新化学实体 (NCE) 的毒性是不切实际的。此外，典型的毒性研究无法可靠地预测人类潜在的安全问题，这是造成昂贵的临床试验流失的主要原因。因此，需要新的经济方法来有效确定吸收、分布、代谢、排泄和毒性 (ADMET)，并将其早期整合到药物开发流程中，以便识别在临床试验中更有可能成功的 NCE。这项高风险和高影响的提案将建立成人肝脏和肾脏干细胞作为受体介导毒性的体外高通量模型的效用。将研究雌激素、过氧化物酶体增殖物激活、芳基烃和孕烷 X 受体介导的活性，以比较评估成人肝脏和肾脏干细胞对体内引起的啮齿动物效应的反应。使用直系同源人类、小鼠和大鼠 cDNA 微阵列进行剂量和时间依赖性基因表达谱分析将与传统方法（例如组织病理学、细胞增殖、临床化学）、分子方法（例如染色质免疫沉淀）和计算方法（例如反应元件搜索）结合使用，以阐明保守和不同的反应。所有数据都将在 dbZach (http://dbzach.fst.msu.edu) 中进行管理，这是一个符合 MIAME 的模块化毒物基因组支持关系数据库。将使用先进的统计方法（例如遗传算法/偏最小二乘框架）通过计算整合这些不同的数据来重建监管途径和网络，并进行比较检查，以识别假定的基于机制的跨物种保守生物标志物，这些生物标志物可修正为高通量筛选，并预测人体体内毒性。一旦经过验证，这些成体干细胞模型和基于机械的生物标志物可用于优化、排序和优先考虑新的先导化合物。此外，该提案还将开发计算工具，以改善药物发现和开发过程中的知识管理和决策。