162 - ALZPED: A NEW DATA RESOURCE FOR IMPROVING THE RIGOR, REPRODUCIBILITY, TRANSPARENCY AND TRANSLATION OF ALZHEIMER'S DISEASE PRECLINICAL RESEARCH.
Abstract
Aims
A major challenge to successful development of therapies for Alzheimer’s disease (AD) is poor translation of preclinical efficacy from animal models to the clinic. Key contributing factors to the poor translation include poor reproducibility of published data, lack of rigor in study design and methodology. To address these issues the National Institute on Aging has created AlzPED, a publicly available database designed as a searchable knowledge platform for sharing and mining of experimental details, designs, data and methods relating to preclinical testing of candidate therapeutics in AD animal models.
Methods
Using key word-driven literature searches published studies are acquired and curated for data on authors, funding source, AD animal models, therapeutic targets and agents, study design, and outcome measures.
Results
AlzPED currently houses curated summaries from over 900 published studies. Summaries are searchable by author, funding source, animal model, therapeutic target and therapeutic agent and elements of experimental rigor and design. At present, the database contains data on 180 animal models, 168 therapeutic targets, 803 therapeutic agents and, more than 1500 AD-related outcome measures.
Conclusions
Analysis of curated studies demonstrates serious deficiencies in reporting critical elements of methodology such as power calculation, blinding for treatment/outcomes, randomization, sex of animal used and balancing for sex, animal genetic background and others. These deficiencies diminish the scientific rigor, reproducibility and translational value of the preclinical studies. Thus, it is evident that a standardized set of best practices is required for successful translation of therapeutic efficacy in AD research.
