2024 NIH Alzheimer's Research Summit: Building a Precision Medicine Research Enterprise - Gaps and Opportunities

Deconstructing Disease Heterogeneity: From Complex Genetics to Molecular Subtypes and Shared Causal Mechanisms

1A. Implement a health-span approach (from pregnancy to death) to AD/ADRD through the collection of deep phenotypic data, from molecular studies to studies on environmental and lifestyle factors.

1B. Continue support for existing and new cohorts with deep phenotypic data collection (e.g., longitudinal data, wearables, multiomics, and neuroimaging)

• Focus on representative populations including individuals resilient/resistant to AD/ADRD, younger persons, individuals with neuropsychiatric symptoms (NPS) and individuals with Down syndrome.

1C. Require best practices for human and model system biospecimen collection, banking, processing, and phenotyping approaches to improve research rigor and reproducibility of human and preclinical studies on AD/ADRD and brain aging.

1D. Require best practices for measuring clinical outcomes including NPS and quantitative neuropathology as part of deep phenotyping.

1E. Scale up multi-modal data integration, with additional inputs from longitudinal phenotyping including real world data and electronic health records combined with molecular profiling.

1F. Enhance computational approaches for multimodal data integration to understand the molecular mechanisms of resilience and the mechanisms of NPS in AD/ADRD.

1G. Conduct comparative, multiscale analyses to identify causal genes, pathways, molecular perturbations, neuropathology that ar4e shared or distinct across different neurodegenerative diseases as well as: AD/ADRD with NPS vs. AD/ADRD without NPS, healthy aging vs. AD/ADRD, resilient vs. non-resilient individuals, to identify and validate molecular subtypes of disease and prioritize precision biomarkers and therapeutics.

1H. Leverage complex human biology-based model systems to better translate biological insights through:

• Collection of primary human tissues (i.e., fibroblasts, blood samples, and other tissues) to create 2D and 3D ex vivo models from well-characterized, representative participants
• Deep phenotyping of experimental models and centralization of these resources
• Modeling of novel biology, in particular molecular subtypes of AD/ADRD and mechanisms of immune response to pathogens/microbe exposures.

1I. Develop communication strategies /platforms to increase bi-directional interaction with research participants, care partners, and healthcare teams.

• Use these platforms to educate the public about research on precision therapies and biomarkers, receive feedback on this research from study participants, caregivers, people with learning difficulties (PWLD), on the utility of findings to improve care and management of patients.

Enabling a Precision Environmental Health Approach to Risk Reduction and Disease Prevention

2A. Expand analyses of representative AD/ADRD cohorts in concert with a wide array of exposome measures (physical, chemical, social factors, and their interactions) using a biological and mechanistic lens.

2B. Enhance resources that provide widely representative linkages of exposome factors to biological markers of AD/ADRD (including brain tissue markers), to examine the dynamic relationship between exposome factors and biological markers of AD/ADRD.

2C. Evaluate the contribution of adverse exposome factors to AD/ADRD incidence and disease progression, within widely representative populations, including those disproportionately impacted by clinical dementia diagnoses.

2D. Leverage prospective and retrospective life-course and children's studies to determine windows of susceptibility, prior to the development of disease pathology and clinical onset, to inform risk reduction and disease prevention.

2E. Continue to develop and characterize fundamental mechanistic models to forward understanding of the biological underpinnings of how exposome promotes or reduces AD/ADRD risk.

2F. Continue to enhance a precision exposome approach for AD/ADRD prevention, slowing of progression, or mitigation of adverse exposures (i.e., and individualized risk assessment and interventions to prevent disease).

2G. Invest in the development of an iterative training framework for exposome-focused AD/ADRD research, embracing the full multi-disciplinary research spectrum.

2H. Enhance and expand open data resources (for researchers) and democratized data resources (for non-researchers) to increase the reach of AD/ADRD-relevant research across a wide-array of public, research, industry, government and other stakeholder groups.

2I. Continue development and expansion of harmonized exposome measure methodologies, including infrastructure to support exposome study and linkage to the full translational array of AD/ADRD study.

Bridging the Preclinical to Clinical Translational Gap and Accelerating Clinical Development

3A. Sustain and expand support for cell-based and animal model systems that capture shared and distinct mechanisms across AD/ADRD including recapitulation of mixed pathologies, sex-specific mechanisms, molecular subtypes of disease and incorporation of the exposome.

3B. Expand the development of non-human primate (NHP) models to bridge the rodent to human translational gap for studying primate specific mechanisms (i.e., immune), cognitive decline, and NPS in AD/ADRD.

3C. Develop new resources/processes to improve data-driven prioritization and validation of novel targets by integrating functional genomics, model systems, network analyses, and drug-phenotype connectivity maps using iPSCs perturbed genetically or with pharmacological/chemical probes.

3D. Create opportunities for AI-enabled drug discovery, chemical biology, and novel hit finding methods to improve efficiency of discovery.

3E. Align efforts across funding agencies to adopt and propagate open science practices as terms and conditions for funding to improve rigor, reproducibility and translatability of preclinical studies in cell-based and animal models and to implement fully transparent reporting including studies with negative/null findings.

3F. Ensure that preclinical studies are balanced for sex and that clinical trials are powered to detect sex-specific differences in responsiveness to treatment.

3G. Develop funding mechanisms that leverage the high-quality resources delivered by the NIA-funded open science consortia and translational centers (AMP® AD, MODEL-AD, TREAT-AD, MARMO-AD) for studying the complex biology of AD/ADRD and preclinical therapy development.

3H. Establish resources that can provide on demand access to aged and conditioned model systems (mice, cell model systems, NHP resources) developed by NIA's translational centers (MODEL-AD, MARMO-AD. TREAT-AD, etc.).

3I. Develop accelerating funding mechanisms to shorten the NIH timeline from grant submission to review and funding decisions.

3J. Evaluate the need for inclusion of amyloid lowering therapies as a study arm in clinical trials testing novel therapeutics.

3K. Adoption of the CAP principles and NIA's expectations for data and samples sharing from AD/ADRD clinical trials by non-federal funding agencies and industry sponsors to enable identification of responder/non-responder molecular endotypes, back-translation and aid prospective participant recruitment efforts and design of precision medicine trials.

3L. Enable precision medicine approaches with smaller, orphan-like drug trial designs for AD/ADRD that leverage molecular subtypes of disease.

Expanding the Therapeutic Landscape: From Small Molecules to Cell-Based Therapies

4A. Advance the development of cell-based and animal models for AD/ADRD rooted in multi-modal human data from observational studies and clinical trials, and establish and expand resources for data-driven matching of experimental models to molecular targets/pathways.

4B. Develop new computational and experimental tools such as robust human iPSC/3D microphysiological model systems, quantitative systems pharmacology, and physiologically-based pharmacokinetic models to enable predictive drug development for AD/ADRD.

4C. Develop novel, preclinical and clinical biomarkers for multiple purposes (diagnosis, target engagement, efficacy and toxicity), with a special focus on those that translate across species.

4D. Establish a coordinated network infrastructure that facilitates cross evaluation and testing of identified lead compounds from labs/organizations working on AD/ADRD drug discovery.

4E. Expand investment in systems biology approaches to understand the molecular and cellular mechanisms of NPS in AD/ADRD and identify novel targets and biomarkers for NPS in AD/ADRD.

4F. Support robust preclinical validation of novel targets for NPS in AD/ADRD and initiate drug discovery efforts to develop new drug candidates for the treatment and prevention of NPS in AD/ADRD.

4G. Align effort across funding agencies supporting AD/ADRD research to establish expectations for full transparency of reporting from raw data to results including data from studies with negative/null findings.

4H. Shorten the NIH review cycle and increase the diversity of funding mechanisms and funding structures to include innovative partnerships that will accelerate the path to human proof of concept for new medicines.

4I. Develop efficient strategies and infrastructure to evaluate potential combination therapies.

4J. Leverage lessons learned from oncology in development of precision medicines.

4K. Continue to expand the clinical trials workforce and accelerate outreach and inclusion of study participants representative of populations affected with the disease or at risk for disease.

4L. Create mechanisms and opportunities for caregivers and people with lived experience to contribute to decision making related to clinical trials study design, data and sample sharing.

Developing Precision Combination Therapies

5A. Continue support for systems biology approaches to identify shared and distinct causal biological mechanisms of AD/ADRD (including single cell levels analyses) that can be leveraged for development of precision therapies.

5B. Expand the use of cutting-edge computational approaches in combination with experimental approaches to identify combination therapies (pharm/pharm, non-pharm/pharm) with potential to be effective for the treatment and prevention of AD/ADRD and NPS in AD/ADRD.

5C. Invest in resources/infrastructure that enable cross-collaboration between researchers with expertise in non-pharmacologic interventions and drug trials to facilitate the development and implementation of combination therapy trials.

5D. Create opportunities and mechanisms for engagement of communities in planning for combination therapies to include community stakeholder input on adaptation of study protocol to increase recruitment success and long-term retention and selection of placebo group before the study design is finalized.

5E. Expand support for existing trial repositories to test biomarkers for identification of subpopulations that potentially benefited from trials or responded better than "slower decline" and inform the design of precision medicine trials.

5F. Develop biomarkers reflecting core mechanisms (i.e., synapse integrity, immune function) to detect additive and/or synergistic effects of combination therapies and biomarkers that are mechanistically related to novel targets, to enable rational selection of combination therapies and to match the right drug(s) to the right stage of disease.

5G. Invest in bio-engineering technologies to develop non-blood based biomarkers for monitoring of therapeutic efficacy at home by trial participants and by family members.

5H. Leverage individual level biomarker data combined with omics measures generated in biosamples from AD/ADRD clinical trials (drug trials and non-pharmacologic trials) to back translate and elucidate: i) disease mechanisms, ii)mechanisms of action for the drug or non-pharm intervention, iii) identify new molecular targets for treatment and prevention, and, iv) reveal potential positive or negative synergy of combination therapies.

5I. Leverage individual level biomarker data with omics measures generated in biosamples from non-AD/ADRD trials to characterize and derive 'signatures' for drugs that can then be repositioned/repurposed individually or in combination for the treatment of AD/ADRD.

5J. Support the development of combination therapies beyond adding new therapies to anti-amyloid treatments and invest in research aimed at identifying combination therapies that reduce and/or prevent adverse events such as ARIA.

5K. Invest in short-term, biomarker-rich clinical trials for existing and novel drug candidates to obtain deeper insights related to target engagement and potential therapeutic effect more quickly to allow for identification of candidate combinations.

5L. Support short-term studies for lifestyle and nutritional interventions (individually or in combination) that evaluate kinetic responses to these interventions using multi-omic panels to provide a stronger basis for representing these multimodal interventions within causal frameworks for precision medicine.

5M. Support innovative trials designs such as aggregated N-of-1 or single case experimental designs (SCEDs) to address the heterogeneity of treatment response to interventions for AD/ADRD and interventions for NPS in AD/ADRD. This would provide precision medicine insights such as:

• Variation in treatment responses as opposed to average response
• Validation of biomarker and surrogate endpoints
• Collection of data that might benefit a participant's health during the trial
• Broad phenotypic effects of a drug (e.g., sleep, mood, general health measures, etc.).

Diversifying the Biomarker Toolkit

6A. Support collection of baseline and longitudinal plasma, digital and ocular phenotypic/biomarkers both individually and in combination across representative cohorts.

6B. Determine how plasma, digital, and ocular biomarkers contribute independently, jointly, and/or interactively with other exposome measures to impact AD/ADRD risk and progression across the life-course.

6C. Invest in discovery and validation of new AD/ADRD biomarkers that are scalable at the population level and across the life-course including:

• Plasma and post-mortem biomarkers beyond amyloid and tau (e.g., TDP-43, alpha-synuclein, etc.)
• Digital
• Ocular (lens and retina)
• Imaging

6D. Develop cross-cutting approaches to enable multi-measure/multi-study data harmonization across:

• Different AD/ADRD plasma biomarker assays
• Different digital data collection devices (wearables, tablets, smartphones, in-home sensors) and versions (upgrades in hardware and software)
• Different ocular scanning instruments, images and metrics.

6E. Facilitate research on combining scalable multi-modal diagnostic and prognostic biomarkers for across the AD/ADRD continuum (e.g., asymptomatic, preclinical, prodromal, diagnosed disease).

6F. Promote the development of open-source front end tools for low/no cost data collection approaches and data ingestion platforms to enable research equally in low-to-middle resourced settings as high resourced settings.

6G. Accelerate the development of advanced analytics applied to multi-modal, multi-dimensional data and multi-factorial disease risk modeling.

6H. Support development of automated ('hands free') open-source tools and low/no cost secure private enclave platforms for:

• Storage of multi-modal personal identifying/sensitive raw digital data files and ocular scans
• Digital and ocular data de-identification methods
• Digital and ocular data processing
• Novel non-data reduction harmonization approaches.

6I. Engage regulatory agencies to:

• Establish and/or refine regulatory pathways for approval of plasma, ocular, and digital biomarkers
• Develop approaches for use of biomarkers (individually and/or in combination) in clinical care decision making
• Identify pathways for integrating plasma, digital, ocular and imaging biomarkers both individually and in combination into clinical trials and clinical care workflow for monitoring onset of symptoms and disease diagnosis, informing treatment plans and tracking responsiveness to treatment.

Building a Knowledge Network for Precision Medicine

7A. Ensure data findability by creating a national multi-modal data catalogue or 'front door'.

7B. Create data and metadata standards to ensure data are accessible and well-documented and encourage data harmonization approaches.

7C. Establish technical standards for data federati8on, federated learning, and digital twins and develop open-source tools for data exploration and analysis.

7D. Institute sustainable funding models for long-term data storage, security, and curation.

7E. Devine clear intellectual property rights that ensure clear ownership of data and derivative works.

7F. Align incentives to drive innovation between data platforms.

7G. Encourage interoperability between platforms to nurture a healthy data ecosystem.

7H. Support a hybrid data access models (e.g., enclave, download, aggregated, federated) to break down data siloes.

7I. Aggregate and integrate evidence on the clinical use of the first-generation disease-modifying therapies from national treatment registers to develop better therapies, treatment plans, and pricing models.

7J. Merge real-world data (RWD) with health records and the Centers for Medicare and Medicaid Services.

7K. Link environmental data with health records and molecular profiles from Alzheimer's patients.

7L. Use Privacy Preserving Record Linkage approaches to support linkage across RWD and research cohorts.

7M. Create guidelines for ethical, reproducible, explainable, equitable, and trustworthy artificial intelligence (AI).

7N. Prepare data for AI by using machine-readable data formats, improving validation, and ensuring easy access.

7O. Encourage the establishment of collaborative model development platforms that implement security and privacy best-practices.

7P. Measure and reduce the environmental impact of data sharing and AI model training and inference.

7Q. Make clinical trial data broadly available regardless of trial outcome.

7R. Leverage trial registries to improve recruitment and reduce attrition of study participants.

7S. Incentivize development of precision medicine approaches in trial design and implementation.

7T. Support the use of digital twin technologies to accelerate clinical development within a precision medicine framework.

7U. Engage all data stakeholders to ensure equity of benefits and risks.

7V. Establish data science training programs and tool foundries to improve accessibility and utilization of large complex data.

7W. Ensure reproducibility in experiments by requiring transparent sharing of raw and processed data, methods, and design details in standardized formats, using well-validated reagents with clear provenance.

7X. Encourage research on "team science" and create strategies for efficiently and cost-effectively using large technical and human resources within an open science framework.

7Y. Promote transparent and reciprocal open science data sharing and make it easier for US-based researchers to acces international data and samples.

Participants as Direct Partners in Research

8A. Integrate and engage lived experience in all aspects of research by:

• Implementing standardized processes, opportunities and expectations of both research teams and participants regarding return of individual results.
• Engaging lived experience through the lifecycle of research studies - from formulating the research question to reporting outcomes.
• Maximizing scientific gains from the study participants' data and participation in studies through robust and expedient data and sample sharing with minimal barriers, provided the participant privacy is protected.

8B. Enable dissemination of research outcomes and findings to the community where participants are from.

8C. Establish feedback mechanisms for people with lived experience to provide iterative input on research funding decisions.

8D. Study the impact of engaging people with lived experience in AD/ADRD research.

8E. Develop standardized processes for return of research results to participants.

8F. Develop evidence-based best practices for engaging people with cognitive impairment as research stakeholders.

8G. Develop evidence-informed training for researchers on how to engage individuals with lived experience in AD/ADRD research.

8H. Develop evidence-informed training on how to engage with researchers from the lived experience perspective regarding how to serve as an effective research advocate and voice with lived experience.

DN