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Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank.


Over 5 million Americans and 50 million individuals worldwide are living with Alzheimer's disease (AD). The progressive dementia associated with AD currently has no cure. Although clinical trials in patients are ultimately required to find safe and effective drugs, animal models of AD permit the integration of brain pathologies with learning and memory deficits that are the first step in developing these new drugs. The purpose of the Alzheimer's Association Business Consortium Think Tank meeting was to address the unmet need to improve the discovery and successful development of Alzheimer's therapies. We hypothesize that positive responses to new therapies observed in validated models of AD will provide predictive evidence for positive responses to these same therapies in AD patients. To achieve this goal, we convened a meeting of experts to explore the current state of AD animal models, identify knowledge gaps, and recommend actions for development of next-generation models with better predictability. Among our findings, we all recognize that models reflecting only single aspects of AD pathogenesis do not mimic AD. Models or combinations of new models are needed that incorporate genetics with environmental interactions, timing of disease development, heterogeneous mechanisms and pathways, comorbidities, and other pathologies that lead to AD and related dementias. Selection of the best models requires us to address the following: (1) which animal species, strains, and genetic backgrounds are most appropriate; (2) which models permit efficient use throughout the drug development pipeline; (3) the translatability of behavioral-cognitive assays from animals to patients; and (4) how to match potential AD therapeutics with particular models. Best practice guidelines to improve reproducibility also need to be developed for consistent use of these models in different research settings. To enhance translational predictability, we discuss a multi-model evaluation strategy to de-risk the successful transition of pre-clinical drug assets to the clinic.

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