IGF::OT::IGF ALZHEIMER'S DISEASE PRECLINICAL EFFICACY DATABASE (ALZPED)

为了缓解这一挑战并增加阿尔茨海默病(AD)疗法的临床应用，美国国家老龄研究所(NIA)/神经科学分部(DN)建议创建一个公共可用的数据库(阿尔茨海默病临床前疗效数据库或阿尔茨海默氏症临床前疗效数据库或阿尔茨海默病)，其中包含与AD动物模型中候选治疗剂的临床前测试相关的信息。更具体地说，在该项目的范围内，NIA/DN建议开发和实施一个基于网络的门户，用于存储、共享和挖掘与AD动物模型中的治疗剂的临床前试验相关的数据。我们设想，门户网站将包含正反两方面数据的实验细节、设计和分析。阿尔茨海默病数据库将向来自学术中心、行业、专注于疾病的基金会和普通公众的科学家广泛提供。为了填充数据库，数据将至少从两个来源收集：1)科学文献；2)直接从研究人员那里。数据将通过馆长提交，将包括关于动物模型、住房和饮食条件、研究设计、主要和次要结果衡量、统计计划、治疗剂(例如，药物、生物制品)、配方、给药途径、治疗剂量和持续时间、数据分析和结果的信息。数据库的这些特点应有助于建立标准化方法和最佳做法，从而增加研究的可重复性，并限制发表对负面结果的偏见。

To mitigate this challenge and increase the translation of Alzheimer’s Disease (AD) therapies to the clinic the National Institute on Aging (NIA)/ Division of Neuroscience (DN) proposes to create a publically available data repository (Alzheimer’s Preclinical Efficacy Database or AlzPED) containing information relating to the preclinical testing of candidate therapeutic agents in AD animal models. More specifically, within the scope of this project the NIA/ DN proposes to develop and implement a web-based portal for housing, sharing and mining of data relating to the preclinical testing of therapeutic agents in AD animal models. We envision that the portal would house experimental details, designs and, analyses of both positive and negative data. The AlzPED database will be broadly available to scientists from academic centers, industry, disease-focused foundations and the lay public. To populate the database data will be gleaned from at least two sources; 1) the scientific literature; 2) directly from researchers. Data will be submitted through a curator and will include information on the animal models, conditions of housing and diet, study design, primary and secondary outcome measures, statistical plan, therapeutic agents (e.g., drugs, biologics), formulation, route of administration, dose and duration of treatment, data analyses and results. These features of the database should aid in establishing standardized methods and best practices, thus increasing reproducibility of studies and limiting publication bias against negative results.