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Alzheimer's disease research and development: a call for a new research roadmap
Author(s) -
Feldman Howard H.,
Haas Magali,
Gandy Sam,
Schoepp Darryle D.,
Cross Alan J.,
Mayeux Richard,
Sperling Reisa A.,
Fillit Howard,
Hoef Diana L.,
Dougal Sonya,
Nye Jeffrey S.
Publication year - 2014
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.12424
Subject(s) - government (linguistics) , disease , data sharing , translational research , clinical trial , business , public health , population , medicine , political science , alternative medicine , environmental health , pathology , philosophy , linguistics
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.