Alzheimer's disease research and development: a call for a new research roadmap. (April 2014)

• Record Type: Journal Article
• Authors: Feldman, Howard H.; Haas, Magali; Gandy, Sam; Schoepp, Darryle D.; Cross, Alan J.; Mayeux, Richard; Sperling, Reisa A.; Fillit, Howard; van de Hoef, Diana L.; Dougal, Sonya; Nye, Jeffrey S.
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1313(2014)
• Page Start: 1
• Page End: 16
• Publication Date: 2014-04
• Subjects: Alzheimer's disease -- dementia -- research and development -- therapy -- treatment -- economics -- epidemiology; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.12424
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract : Epidemiological projections of the prevalence of Alzheimer'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.