Recommendations from the NIH AD Research Summit 2015

The Alzheimer’s Disease Research Summit 2015: Path to Treatment and Prevention held Feb. 9-10 brought together leading experts on Alzheimer’s disease and other complex diseases to identify research priorities and strategies needed to accelerate the development of successful therapies.

Over 60 leading experts (PDF, 4.2M) from academia, industry, non-profit organizations and advocacy groups joined to develop the research recommendations. Convened by the National Institute on Aging (NIA) at NIH and the U.S. Department of Health and Human Services, with support from the Foundation for NIH, the Feb. 9–10, 2015, meeting drew 500 participants onsite, with another 500 participating via videocast.

The recommendations call for a change in how the academic, biopharmaceutical and government sectors participating in Alzheimer’s research and therapy generate, share and use knowledge to propel the development of critically needed therapies. They outline new scientific approaches to address critical knowledge gaps and propose ways to harness emerging technologies to accelerate treatments for people at all stages of the disease. They also identified infrastructure and partnerships necessary to successfully implement the new research agenda and strategies to empower patients and engage citizens.

View a video about the Alzheimer's Disease Research Summit 2015 featuring NIH officials and grantees.

The agenda will help guide both the public and private sectors toward meeting research goals set forth in the National Plan to Address Alzheimer’s Disease, a national strategy aimed at identifying effective interventions to treat and prevent Alzheimer’s by 2025.

Overarching Themes

Several overarching and transformative concepts were identified by Summit participants as critical to achieving success in Alzheimer’s disease therapy development, and these emerged repeatedly among the themes brought forward by the different workgroups:

Understand all aspects of healthy brain aging and cognitive resilience to inform strategies for Alzheimer’s disease (AD) prevention.

Expand integrative, data-driven research approaches such as systems biology and systems pharmacology.

Develop computational tools and infrastructure in order to enable storage, integration, and analysis of large-scale biological and other patient-relevant data.

Leverage the use of wearable sensors and other mobile health technologies to inform discovery science as well as research on Alzheimer’s disease care.

Support and enable Open Science in basic, translational, and clinical research.

Change the academic, publishing, and funding incentives to promote collaborative, transparent, and reproducible research.

Invest in the development of a new translational and data science workforce.

Engage citizens, caregivers, and patients as equal partners in Alzheimer’s disease research.

SESSION ONE: Interdisciplinary Research to Understand the Heterogeneity and Multifactorial Etiology of Disease

Maximize endophenotyping of established cohorts that are genetically, epigenetically, or otherwise at risk (e.g. due to cerebrovascular, metabolic, or neuroinflammatory compromise) to fill in the gaps of large-scale human data needed to formulate testable hypotheses around AD heterogeneity including sex differences and ethnicity phenotypes of risk.
Establish new cohorts for intense endophenotyping (e.g., exposome, multidimensional “omics”, imaging, cognitive) that represent gender and diverse populations.
Maximize, optimize, and evolve the existing NIA/NIH translational infrastructure and eliminate barriers to sharing, integrating, and reuse of data needed to build predictive models of disease by:

removing barriers to combining data from multiple sources and sharing processed data with other investigators
generating combined and harmonized data sets that can be shared between investigators
providing genetic and other patient-level data on a common-access cloud site where researchers can perform large-scale computational tasks without the need to download and store large data sets.
providing access to sponsor-level data from clinical trials to revisit those that failed to demonstrate efficacy
supporting electronic consenting and other consenting models that give ownership of health care data to patients and study participants

Integrate AD research with neurobiology of aging and biology of aging research by developing new programs on systems biology and integrative physiology to gain a deeper understanding of the complex biology of disease.
Apply systems pharmacology approaches that leverage existing systems biology efforts built on human biology to construct advanced, multiscale models of diseases. Systems pharmacology efforts can be informed by the disease models to identify sub-networks relevant to aspects of disease and disease subtypes that can serve as targets for drug discovery and drug repurposing.
Develop the next generation of in vivo models based on human data to explore experimentally the biology and physiology of genetic, epigenetic, vascular, environmental, and other risk factors for Alzheimer’s and related dementias.
Integrate new technologies such as iPSC, genome editing, optogenetics/deep brain stimulation/trans-magnetic stimulation, and next generation in vivo imaging to facilitate assessment and validation of findings from human studies.
Accept the limitations of rodent animal models and divest from using behavioral endpoints as measures of therapeutic efficacy in favor of biochemical and physiological endpoints.
Build translatable biomarkers that inform the level of target engagement early in the life cycle of a drug discovery project and seek to develop the widest possible therapeutic window that will support testing a full dose range in humans.
Develop a new translational workforce by bringing new expertise and developing new, integrative training programs (all aspects of data science, translational bioinformatics, software engineering, traditional, and emerging drug discovery disciplines, variety of geriatric specialties).
Develop and implement strategies/policies to improve the poor reproducibility and translatability of basic research findings. These should include changes in the reward systems in academia, funding agencies, and journals.

SESSION TWO: Transforming AD Therapy Development: From Targets to Trials

Support research on the interaction among amyloid, tau, inflammation, glucose and lipid metabolism, oxidative stress, and other aspects of AD; this should include quantification of the trajectories of biochemical changes within and across these pathways and their integrative response to therapeutic perturbations.
Invest in comprehensive and systematic studies of wellness that go beyond genetics by using extensive molecular profiling of individuals that age successfully and the rare individuals who resist/escape AD despite having high genetic risk (E4 homozygous) or highly penetrant, deleterious mutations that cause AD (FAD mutation carriers).
Continue to support and expand existing efforts focused on the generation and integration of large-scale molecular, cellular, and physiologic data to construct predictive models of AD and initiate efforts to incorporate non-traditional data modalities as dimensions of health and disease such as data collected by wearable sensors and mobile health technologies. The development of the most predictive model of AD will require open sharing of all data, results, and network models.
Support in silico and experimental target discovery and validation efforts that are fully informed by systems biology/pharmacology models and fully embrace the complexity of disease and drug action, such that networks are considered as targets and read-outs of therapeutic activity.
Support the development of computational tools and infrastructure that enable basic and clinical researchers to query systems biology/pharmacology models from integrated perspectives supporting translational research (e.g., target-oriented and patient/subgroup oriented).
Support research aimed at identifying quantitative methods to assess synergy /additivity of potential therapeutics including synergy between drugs and non-pharmacological (e.g., foods, exercise) perturbations.
Leverage the network concept of drug targets and the power of phenotypic screening to advance rational drug repurposing and data-driven development of drug combinations based on the ability of single or multiple therapeutic agents to perturb entire molecular networks away from disease states in cell-based and/or animal models.
Develop cost-effective, high throughput methods to isolate different neural and glial cells for “omics” profiling, drug-screening, and other studies and improved iPSC protocols for relevant cell types and invest in more sophisticated and relevant human-based models such as organoid systems.
Develop robust quantitative biomarkers that can be used to provide information on specific aspects of the disease process and to measure the extent of perturbation of the disease process introduced by therapeutic interventions during preclinical testing and early-phase clinical trials. These efforts should include:

development of translatable biomarkers indicative of incipient disease (i.e. ocular, olfactory)
development of biomarkers for detection and tracking of synaptic dysfunction and synaptic response to treatment in freely behaving animals and in humans
discovery and validation of translatable pharmacodynamics biomarkers for a variety of therapeutic targets

Invest in the development of clinical tools needed to characterize disease progression to make available sensitive quantitative outcome measures across all stages of AD to efficiently evaluate meaningful clinical impact of therapies.
Enable the adoption of formal failure analysis as a routine approach in preclinical and clinical drug development to accelerate translational learning. This requires access to study data and biosamples and resources for data hosting and curation.
Expand existing and create new educational and training resources in traditional and emerging drug discovery disciplines for academic researchers and develop research programs that bring academic and industry experts together.

SESSION THREE: New Strategies for Prevention

Provide resources to liberate publicly funded data into the public domain and ensure their adequate curation to maximize usability.
Enhance the potential of community-based cohort studies to generate multiple types of molecular and physiological measurements that can be used for systems biology and gene-environment studies by:

incorporating technologies such as actigraphy and other passive devices to collect precise quantitative data to serve as endophenotypes
including assessments of how other aging physiologic systems interact with the brain (e.g., cardiac, pulmonary, renal, vascular, metabolic, circadian rhythm)
expanding the types of cross-sectional and longitudinal ante- and post-mortem biospecimen data collection needed to generate multiple layers of “omics” data

To accelerate the identification of genomic variants and other risk and protective factors that contribute to the heterogeneity and multifactorial etiology of dementia, develop cohorts with participants of African, Native American, Asian, and mixed ancestry, e.g., Latinos as well as younger cohorts (midlife and younger).
Apply an ecological perspective to better understand how lifestyle factors can impact risk of cognitive impairment and AD. Such a perspective should span physical, behavioral, social, and environmental levels and elucidate interactions among behavioral and contextual variables that can influence risk.
Employ a lifespan approach to study the epigenomic changes during vulnerable periods/physiological transition states‎ to understand the mechanisms of protective and risk factors. This will require development of methods to circumvent issues of cellular heterogeneity.
Invest in research aimed at:

testing the therapeutic potential of epigenetic regulators as therapeutic targets for treatment and prevention
characterizing the extent to which molecular (epi)genomic and transcriptomic variation identified in peripheral tissues (blood, saliva, etc.) can be used as a proxy for inter-individual variation manifest in the brain.

Create research programs aimed at understanding the (epi)genetics and the complex biology of cognitive resilience, especially in high-risk individuals such as E4/E4 and FAD carriers and in individuals with exceptional longevity.
Intensify efforts to understand the pathological and protective roles of APOE and its pharmacogenetic effects on various pharmacological and non-pharmacological interventions.
Develop integrative research programs to understand how peripheral systems (in particular: immune, metabolic, microbiome) interact with the brain to impact a variety of CNS measures related to brain aging and the initiation and progression of neurodegenerative changes.
Invest in understanding the integrative physiology of sleep and elucidating the short- and long-term consequences of disrupted and optimized sleep on brain aging and AD.
Provide high-quality evidence to inform the selection and implementation of prevention strategies by:

using a range of clinical research designs (n-of-1 studies, functional challenge studies, pragmatic clinical trials, population-based cohort designs, and clinical trial/population-based cohort hybrid designs)
stratifying participant risk groups using dense "omics" and gene-environment interaction profiles
developing guidelines for defining “clinically significant” results of prevention trials
developing sustainable, ”real-life,” multi-systems interventions and methods to analyze and interpret the observed changes

Compare the effectiveness of dissemination science methodologies to ensure that prevention strategies with high-level evidence are utilized by patients and other stakeholders to support healthy brain aging.

SESSION FOUR: Innovating Disease Monitoring, Assessment and Care

Support the development of a wide range of technologies that enable in-place monitoring of individuals at all stages of the disease and integration of this information with other patient-relevant data in order to refine our understanding of disease progression and our ability to build predictive models of disease.
Develop a standard set of outcome measures to enable data comparisons across studies including, but not limited to, daily physical function, home safety, quality of life, physical and cognitive function, behavioral symptoms, and caregiver-related outcomes. Ensure that all outcome measures are validated across diverse educational, linguistic, and cultural groups.
Improve methodologies for data capture, storage, and analysis and ensure collection of raw sensor data to enable pooling of data across studies. Sensor data collection apps and data collection server infrastructure should be built and released as open-source tools. These efforts should include the development of standards to enhance interoperability between devices and networks.
Initiate programs that bring together cross-disciplinary expertise (including mathematical, statistical and software engineering experts) needed to develop innovative monitoring technologies and incentivize researchers with expertise in technology design, health literacy, and form-factor expertise, to develop more personalized technologies to serve the diverse populations of aging adults with and without AD and their caregivers.
Capitalize on the NIH Big Data to Knowledge Initiative and invest in training of the next generation of AD data scientists.
Leverage large community-based longitudinal cohort studies to efficiently, economically, and systematically explore the use of the technologies and involve the community in the research process to spur discovery science.
Incorporate pervasive computing approaches in AD clinical trials to enable objective and continuous data capture of individual participants’ everyday function. This will allow the assessment of intra-individual differences and significantly reduce trial sample sizes and the time required to identify efficacy signal(s).
Invest in research to develop new technologies that enhance the delivery of clinical care, caregiver support, and in-home monitoring.
Integrate mobile health (mHealth) technologies used for disease monitoring and assessment with the formal health care system and conduct clinical trials of mHealth technologies that rely on lay workers (rather than patients or families) to collect data in community settings and transmit these data to the formal health care system.
Conduct research that explores the barriers and facilitators of early diagnosis of dementia in primary care settings and explores disparities in diagnosis and treatment among vulnerable elders, including ethnic minorities.
Support research on clinical trials testing new models of care across the care continuum and across the full time-course of AD as well as research that tests effective approaches for facilitating productive care partnerships among the formal health care system, community service agencies, and family caregivers.
Conduct research designed to test the use of technology to overcome the workforce limitations in the care of older adults with dementia as well as in providing caregiver support and education.

SESSION FIVE: Empowering Patients, Engaging Citizens

Network with community leaders to build mutually beneficial relationships and create culturally tailored educational programs.
Increase the presence of individuals from ethnically and culturally diverse backgrounds as investigators, outreach staff, and personnel.
Engage primary physicians’ offices serving under-represented communities as partners in participant recruitment.
Partner with communities to learn about how to measure meaningful impact of treatments and to determine the appropriate return of value from research participation (i.e., results, general information, failures).
Create synergies between federally funded programs such as PCORI and CTSAs to make community involvement less expensive, and to learn from the various experiments of community engagement.
Bring the trials to the people – (i.e., their homes, assisted living facilities, day programs) and leverage emerging technologies for data collection (mobile health applications, electronic medical records).
Use existing and invest in new crowd-powered medical research platforms that educate the public while accelerating data collection and analysis.
Develop methods and policies for data collection and sharing that empower participant control such as:

electronic consent forms which provide an option for broad sharing of de-identified data
improved access to data for participants and recast of complex data into forms consumable by non-specialists

Align enabling technologies with policy to streamline and innovate data sharing and patient consent.

SESSION SIX: Enabling Partnerships for Open Innovation

Create partnerships among funding agencies to enforce and incentivize rapid and broad sharing of data to enable open, reproducible, and translatable research. Open sharing of data must go beyond the distribution of raw data to support the sharing of efforts in data integration, repository development and maintenance, and consortia-led science.
Academic institutions, funding agencies, and journals should develop incentives for researchers (particularly early-career investigators) to participate in large-scale collaborative science by adopting alternative recognition and attribution methods such as:

microattribution (similar to the use of GitHub in software engineering)
use of alphabetical author lists associated with detailed acknowledgement of individual contributions within the text of the manuscript
developing new metrics for recruitment and career advancement purposes that recognize the importance of scientific contribution to shared-science programs

Develop innovative partnerships for next-generation research to incentivize students and early-career investigators to adopt a collaborative approach to research through the use of targeted small funding schemes.
Develop precompetitive partnerships to support novel/disruptive science focusing on ideas or approaches that are outside of the mainstream for the field and tend not to fare well in traditional peer-review systems.
Develop partnerships where intellectual property is not an issue, or is a minimal issue and is agreed upon from the start. Ensure that IP is used to support innovation and bring additional investment in the field and not to block others from working in the same research space.
Expand the precompetitive space from target selection through clinical proof of mechanism/proof of concept to overcome the most significant hurdle in developing innovative treatments. Specifically:

Create precompetitive partnership(s) to validate the therapeutic targets that will be delivered by the Accelerating Medicines Partnership for AD (AMP AD), as well as other pioneer targets, through clinical proof of mechanism/proof of concept.

NOTE: These recommendations are to be considered by the National Advisory Council on Aging at its meeting on May 12 and 13, 2015.