Translational Center for Alzheimer's Disease Target and Drug Discovery

 描述(申请人提供)：阿尔茨海默病(AD)是痴呆症最常见的原因，这是一种毁灭性的疾病，影响着全球超过3500万人。我们的最终目标是将定量和系统药理学(QSP)方法应用于AD药物的发现和开发，以便提供能够显著减缓疾病进展和/或预防疾病的治疗方法。我们将利用这笔规划赠款整合必要的人员、专业知识和资源，以在圣地亚哥的四个医疗研究机构(加州大学圣地亚哥分校、索尔克研究所、斯克里普斯研究所和桑福德·伯纳姆医学研究所)和加州大学洛杉矶分校建立一个核心基础设施，这是规划AD预测药物开发翻译中心所必需的。这将在一定程度上使用目前正在开发的基于网络的平台(系统医学阿尔茨海默氏症研究和治疗或SMART)来实现：//Smart.ucsd.edu。编目和整合资源，包括细胞和动物模型、试剂、化合物化学库、不同发育阶段的现有化合物、生物样本、死后脑组织、高通量筛选和新产生的多组学数据集(包括细胞和动物模型的现有数据)以及人类纵向研究数据的存档和注释等方法。一个主要的焦点将是利用来自表型非常好的患者(包括极端表型)的人IPSC(HiPSC)来尝试在DISH模型中建立疾病。此外，我们会利用本地在系统生物学、生物统计学和生物信息学生物工程、药理学、神经病理学和临床研究方面的丰富专业知识，为研究生、医学实习生和博士后学者设立合适的QSP培训计划。最后，我们将确定实现高效数据共享的流程，并与制药公司建立强有力的合作。

 DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is the most common cause of dementia, a devastating disorder that affects over 35 million people worldwide. Our ultimate objective is to apply quantitative and systems pharmacology (QSP) approaches to AD drug discovery and development in order to deliver treatments capable of significantly attenuating the progression and/or preventing the disease. We will utilize this planning grant to integrate the necessary personnel, expertise and resources to assemble an infrastructure of cores across four San Diego medical research institutions (UCSD, the Salk Institute, the Scripps Research Institute and the Sanford Burnham Medical research Institute) and UCLA that are necessary for planning an AD Translational Center for Predictive Drug Development. This will be accomplished in part using a web-based platform that is currently under development (Systems Medicine Alzheimer's Research and Treatment or SMART) ://smart.ucsd.edu. Cataloging and integration of resources including cellular and animal models, reagents, chemical libraries of compounds, existing compounds in various stages of development, biosamples, postmortem brain tissue, methods such as high throughput screens and archiving and annotating of newly-generated multi-omics datasets (including existing data from cellular and animal models) as well as data from human longitudinal studies. A major focus will be the utilization of human iPSC (hiPSC) from extremely well phenotyped patients (including extreme phenotypes) to attempt to establish disease in a dish models. In addition we will capitalize on the local abundance of expertise in systems biology, biostatistics and bioinformatics bioengineering, pharmacology, neuropathology and clinical research to establish appropriate training programs in QSP for graduate students, medical trainees and postdoctoral scholars. Finally, we will identify processes to enable highly effective data sharing and establish strong collaborations with pharmaceutical companies.