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的出现，高通量筛查和“ OMIC”技术提供了前所未有的机会，可以发现新的治疗剂。但是，由于人类的疗效，不利的药效特性，不可接受的不良反应以及主要的未预测毒性，许多候选人因效力不佳而失败。当前的非临床安全评估是昂贵的，耗时的，利用大量药物并涉及大量动物，因此在药物开发管道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的潜在安全问题，这是造成昂贵的临床试验损耗的主要原因。因此，需要有效地确定可以在药物开发管道中较早整合的新经济方法来有效地确定吸收，分布，新陈代谢，排泄和毒性（ADMET），以确定在临床试验中取得成功的可能性更大的NCE。这个高风险和高影响力的建议将确定成年人类肝脏和肾脏干细胞的实用性，作为用于受体介导的毒性的体外高通量模型。将研究雌激素，过氧化物酶体增殖物激活，芳基碳氢化合物和妊娠X受体介导的活性，以评估对体内引起的啮齿动物效应的成年人肝和肾脏干细胞反应。使用直系同源的人，小鼠和大鼠cDNA微阵列进行剂量和时间依赖性的基因表达谱分析，将与传统（例如组织病理学，细胞增殖，临床化学），分子（例如染色质免疫原理）和计算方法（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素）（例如，响应元素）（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素搜索）（例如，响应元素搜索）。所有数据将在DBZACH（http://dbzach.fst.msu.edu）中进行管理。通过使用高级统计方法（例如遗传算法/部分最小二乘正方形框架），将通过计算整合这些不同的数据来重建监管途径和网络，并对其进行了相对检查，以识别基于机械机械的跨机械性跨物种保存的生物标志物，这些生物标志物可预测高度通过透过透过的透过透过的透过透过的人，并预测了Invivo handy nym's nyvivo humans sytivo invivo humans sytitive。一旦得到验证，这些成年干细胞模型和基于机械的生物标志物可以用于优化，排名和优先排序新的铅化合物。此外，该建议还将开发计算工具，以改善药物发现和开发期间的知识管理和决策。