Blood-based markers (BBMs) have recently shown promise to revolutionize the diagnostic and prognostic work-up of Alzheimer's disease (AD), as well as to improve the design of interventional trials. Here we discuss in detail further research needed to be performed before widespread use of BBMs. We already now recommend use of BBMs as (pre-)screeners to identify individuals likely to have AD pathological changes for inclusion in trials evaluating disease-modifying therapies, provided the AD status is confirmed with positron emission tomography (PET) or cerebrospinal fluid (CSF) testing. We also encourage studying longitudinal BBM changes in ongoing as well as future interventional trials. However, BBMs should not yet be used as primary endpoints in pivotal trials. Further, we recommend to cautiously start using BBMs in specialized memory clinics as part of the diagnostic work-up of patients with cognitive symptoms and the results should be confirmed whenever possible with CSF or PET. Additional data are needed before use of BBMs as stand-alone diagnostic AD markers, or before considering use in primary care.

Between 2018 and 2019, multiple clinical trials ended earlier than planned, resulting in calls to improve communication with and support for participants and their study partners (dyads). The multidisciplinary Participant Follow-Up Improvement in Research Studies and Trials (Participant FIRST) Work Group met throughout 2021. Its goals were to identify best practices for communicating with and supporting dyads affected by early trial stoppage. The Participant FIRST Work Group identified 17 key recommendations spanning the pre-trial, mid-trial, and post-trial periods. These focus on prospectively allocating sufficient resources for orderly closeout; developing dyad-centered communication plans; helping dyads build and maintain support networks; and, if a trial stops, informing dyads rapidly. Participants and study partners invest time, effort, and hope in their research participation. The research community should take intentional steps toward better communicating with and supporting participants when clinical trials end early. The Participant FIRST recommendations are a practical guide for embarking on that journey.

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.