Alzheimer's Drug Discovery Foundation

Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. The objective of this work is to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL (Vocal Biomarker Guidelines for Ontology, Classification, Application, and Logistics) initiative, a structured, international consensus-based framework that aims to provide standards, and guidelines. VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. Multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), culminating in an in person workshop at the 2025 Bridge2AI Voice Symposium. A group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. VOCAL's iterative process involved five rounds of review, feedback, and an in-person workshop at the international 2025 Bridge2AI Voice Symposium, ensuring the incorporation of diverse perspectives and achieving a robust agreement on the proposed definitions. Consensus-based definitions for vocal biomarkers, spanning from broad concepts (biomarker, digital biomarker, vocal biomarker) to domain-specific measures (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (Level 0: Biomarker, Digital Biomarker, Vocal Biomarker) to more specific physiological and cognitive domains (Level 1: Cardio-Respiratory Acoustic; Level 2: Voice; Level 3: Speech/Articulatory; Level 4: Cognitive/Language, including linguistic and paralinguistic subtypes). This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility.

BackgroundProgressive multifocal leukoencephalopathy (PML) is an opportunistic infection caused by the JC virus and often fatal. Natalizumab is a highly effective therapy for multiple sclerosis (MS) but is linked to a high incidence of PML. The current metrics used to stratify MS patients at risk for PML are incomplete, leading some patients to prematurely discontinue an effective treatment and others to develop PML despite perceived low risk.ObjectiveWe sought to develop a combination of cell-based assays using peripheral blood which can be used to provide a more comprehensive assessment of immune system function and complement existing PML risk metrics.MethodsOur assays measure general and JCV specific responses in CD4+ and CD8+ T cells following antigen stimulation. We examined responses in systemic lupus erythematosus (SLE) and MS patients with and without PML. Our cytotoxicity index (CTI) measures expression of IFNγ and the degranulation marker CD107a on CD8+ T cells, while our OX40 immunity index (OII) measures CD4+ T cell activation, as determined by OX40 and CD25 co-expression.Results and conclusionWe find that the combined metrics can be used to assess JCV immunocompetence, which can distinguish patients with and without PML, and could be used to predict and monitor PML patients from diagnosis onward to facilitate timely intervention.

Abstract BackgroundWidely used neuropsychological test instruments are notoriously biased across the demographics of age, sex/gender, education, language and culture. This includes verbal memory tests that elicit speech such as the paragraph recall or list‐learning memory tests. Language tests are similarly biased, including the Boston Diagnostic Aphasia Examination Cookie Theft Test (CTT) that has been used to elicit both written and spoken responses for decades. Biased tests generate biased results, and this problem is exacerbated by the widespread practice of repurposing tests generated for those in largely high income, Westernized countries for use in other countries that are demographically and culturally distinct. We tested the use of large language model (LLM) tools to automatically generate verbal test stimuli that were more locally relevant.MethodsStarting with the CTT, that involves describing a picture that contains 14 core content units, we used ChatGPT to aid in generating customized pictures with the same number of core content units that were more universally relevant, (e.g., open park area, beach, etc.) by suggesting parameters and integrating resulting features into new pictures. We also tested capabilities to generate pictorial scenes that are more representative of regional settings, such as the Asia Pacific or Middle East. We are testing LLM capacity to generate similarly regionally customized list learning stimuli that maintain the original list learning test word frequency and difficulty across languages.ResultsUsing an iterative approach, we were able to use automated artificial intelligence (AI)‐driven tools to rapidly generate regionally relevant test stimuli that retained the original test characteristics to preserve generation of comparable results. Descriptive comparisons found that aided by automated AI tool, we could generate new neuropsychological test stimuli in a matter of minutes compared to months long manual methods.ConclusionThe rapid emergence of AI has generated significant concerns about its appropriate use for health and health care applications. Given that AI is here to stay, we are testing the capacity of this technological advance to address long‐standing, persistent problems in AD research and usher in a new era of precision brain health that has global relevance in a rapidly aging world population.

Abstract BackgroundResearch on racial and ethnic disparities in lifetime dementia costs is sparse. We analyzed lifetime Medicare expenditures for dementia patients to identify when during the course of care costs are the highest, and whether these costs differ by race and ethnicity.MethodWe analyzed data from the 2000‐2016 Health and Retirement Survey (HRS) linked with corresponding Medicare claims to estimate total Medicare expenditures for four “phases”: 1) the year before dementia diagnosis; 2) the first year following a dementia diagnosis; 3) ongoing care for dementia after the first year; and 4) the last year of life. We then estimated each patient’s phase‐specific and lifetime Medicare expenditures by using a race‐specific survival model and monthly expenditures adjusted for demographics, socio‐economic status, year of dementia diagnosis, nursing home status, functional limitations, cognitive impairment, comorbidities, and Medicaid eligibility status.ResultAdjusted mean lifetime Medicare expenditures for non‐Hispanic Blacks ($166,548) and Hispanics with dementia ($163,425) exceeded corresponding expenditures for non‐Hispanic whites ($135,648) (Figure). In the year preceding and immediately following an initial dementia diagnosis, mean Medicare expenditures for non‐Hispanic Blacks ($26,384 and $20,824) exceeded expenditures for Hispanics and non‐Hispanic whites (ranging $21,340‐23,379 and 17,382‐18,085). Ongoing care costs for non‐Hispanic Blacks with dementia (mean $1,734/month) exceeded costs for Hispanics ($1,538/month) and non‐Hispanic whites ($1,424/month). Of the four phases, the last year of life was responsible for the greatest cost contribution: $52,003 for non‐Hispanic Blacks, $48,377 for Hispanics, and $39,648 for non‐Hispanic whites.ConclusionsTotal mean Medicare expenditures for non‐Hispanic Black and Hispanic beneficiaries with dementia exceeded corresponding expenditures for non‐Hispanic whites. Expenditures were highest for non‐Hispanic Blacks with dementia in every phase of care. Compared to non‐Hispanic whites, Hispanics had lower expenditures in the first year following dementia diagnosis and similar monthly ongoing care expenditures but much higher expenditures during their last year of life. Research should identify factors contributing to the disparities identified here so that policy makers can address problems adversely affecting disadvantaged populations. Figure: Estimated mean lifetime Medicare expenditures for beneficiaries newly diagnosed with dementia, by phase of care and by race and ethnicity (adjusted)

Evolving immunogenicity assay performance expectations and a lack of harmonized anti-drug antibody validation testing and reporting tools have resulted in significant time spent by health authorities and sponsors on resolving filing queries. Following debate at the American Association of Pharmaceutical Sciences National Biotechnology Conference, a group was formed to address these gaps. Over the last 3 years, 44 members from 29 organizations (including 5 members from Europe and 10 members from FDA) discussed gaps in understanding immunogenicity assay requirements and have developed harmonization tools for use by industry scientists to facilitate filings to health authorities. Herein, this team provides testing and reporting strategies and tools for the following assessments: (1) pre-study validation cut point; (2) in-study cut points, including procedures for applying cut points to mixed populations; (3) system suitability control criteria for in-study plate acceptance; (4) assay sensitivity, including the selection of an appropriate low positive control; (5) specificity, including drug and target tolerance; (6) sample stability that reflects sample storage and handling conditions; (7) assay selectivity to matrix components, including hemolytic, lipemic, and disease state matrices; (8) domain specificity for multi-domain therapeutics; (9) and minimum required dilution and extraction-based sample processing for titer reporting.Graphical

The 2020 COVID-19 pandemic has disrupted Alzheimer’s disease (AD) clinical studies worldwide. Digital technologies may help minimize disruptions by enabling remote assessment of subtle cognitive and functional changes over the course of the disease. The EU/US Clinical Trials in Alzheimer’s Disease (CTAD) Task Force met virtually in November 2020 to explore the opportunities and challenges associated with the use of digital technologies in AD clinical research. While recognizing the potential of digital tools to accelerate clinical trials, improve the engagement of diverse populations, capture clinically meaningful data, and lower costs, questions remain regarding the stability, validity, generalizability, and reproducibility of digital data. Substantial concerns also exist regarding regulatory acceptance and privacy. Nonetheless, the Task Force supported further exploration of digital technologies through collaboration and data sharing, noting the need for standardization of digital readouts. They also concluded that while it may be premature to employ remote assessments for trials of novel experimental medications, remote studies of non-invasive, multi-domain approaches may be feasible at this time.

BACKGROUND Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. METHODS Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. RESULTS The residents of the +CP and –CP communities exhibit no statistical difference in age (<i>P</i>=.64), sex (<i>P</i>=.63), and staff service hours per resident (<i>P</i>=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (<i>P</i>=.02), a 69% lower fall rate (<i>P</i>=.01), and a 67% greater length of stay (<i>P</i>=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (<i>P</i>=.02) and 40% (<i>P</i>=.02), respectively. CONCLUSIONS The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

Viral vector-based gene therapies (GTx) have received significant attention in the recent years and the number of ongoing GTx clinical trials is increasing. A platform of choice for many of these studies is adeno-associated virus (AAV). All humans may be exposed to natural AAV infections and could mount an immune response against the virus. Consequently, there can be a high prevalence of pre-existing anti-AAV immunity. This presents a potential limitation for AAV-based GTx due to the potential for AAV-specific antibodies to reduce the efficacy of the GTx. Therefore, appropriate assessment of potential subjects enrolled in these studies should include evaluation for the presence and degree of anti-AAV immunity, including anti-AAV neutralizing antibodies (NAb). Recommendations for the development and validation of cell-based anti-AAV NAb detection methods, including considerations related to selection of appropriate cell line, surrogate vector/reporter gene, assay matrix and controls, and methodologies for calculating assay cut-point are discussed herein. General recommendations for the key assay validation parameters are provided as well as considerations for the development of NAb diagnostic tests. This manuscript is produced by a group of scientists involved in GTx therapeutic development representing various companies. It is our intent to provide recommendations and guidance to industrial and academic laboratories working on viral vector based GTx modalities with the goal of achieving a more consistent approach to anti-AAV NAb assessment.