Alzheimer's disease research and development: a call for a new research roadmap
Alzheimer's disease research and development: a call for a new research roadmap
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DOI：
10.1111/nyas.12424
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发表时间：
2014-01-01
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影响因子：
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通讯作者：
Nye, Jeffrey S.
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作者：
Feldman, Howard H.;Haas, Magali;Nye, Jeffrey S.
阿尔茨海默病（AD）和相关痴呆的流行病学预测、65岁以上人口的迅速增长以及对健康、经济和社区的巨大社会后果预示着一场迫在眉睫的全球公共卫生危机。目前可用的AD治疗是对症的，具有适度的效应量，对长期疾病结局的影响有限。在过去的十年中，尽管做出了巨大的努力来开发针对阿尔茨海默氏症相关病理的疾病修饰治疗，但没有新批准的药物治疗。政府、监管机构、工业界、学术界和整个社区需要进行前所未有的合作努力，以解决这一危机，并制定一项可行的计划，以迅速取得进展，成功开发有效的治疗方法。在此，我们制定了四个关键优先领域的行动方案，包括（1）解决疾病的基本机制，目标是开发一套核心研究工具，数据共享框架，并创建可访问的验证和复制疾病模型;（2）发展转化研究，强调疾病模型开发的快速进展和从临床前阶段到临床阶段的更好转化，部署领先技术，更准确地开发预测模型;（3）通过开发强大的方法和资源来推进试验，并创建基础资源，如连续适应性试验，注册，数据存储库和工具开发来预防AD;（4）创新公私伙伴关系和全球合作，建立激励合作和投资的机制，开发更大的竞争前空间和更快的数据共享。
Epidemiological projections of the prevalence ofAlzheimer's disease (AD) and related dementias, the rapidly expanding population over the age of 65, and the enormous societal consequence on health, economics, and community foretell of a looming global public health crisis. Currently available treatments for AD are symptomatic, with modest effect sizes and limited impact on longer term disease outcomes. There have been no newly approved pharmaceutical treatments in the last decade, despite enormous efforts to develop disease-modifying treatments directed at Alzheimer's-associated pathology. An unprecedented collaborative effort of government, regulators, industry, academia, and the community at-large is needed to address this crisis and to develop an actionable plan for rapid progress toward successfully developing effective treatments. Here, we map out a course of action in four key priority areas, including (1) addressing the fundamental mechanisms of disease, with the goal of developing a core set of research tools, a framework for data sharing, and creation of accessible validated and replicated disease models; (2) developing translational research that emphasizes rapid progress in disease model development and better translation from preclinical to clinical stages, deploying leading technologies to more accurately develop predictive models; (3) preventing AD through the development of robust methods and resources to advance trials and creating fundamental resources such as continuous adaptive trials, registries, data repositories, and instrument development; and (4) innovating public/private partnerships and global collaborations, with mechanisms to incentivize collaborations and investments, develop larger precompetitive spaces, and more rapid data sharing.