of Program Coordination, Planning, and Strategic Initiatives
Title of proposed idea: Venture Fund for Research and Development of New Medications to Treat Chronic Pain (see “NIH Award Strategies” in Innovation Brainstorm ideas)
Nominator: NIH Institutes/Centers
Major obstacle/challenge to overcome: Chronic pain, which affects 116 million Americans and is a significant public health burden, is not adequately managed by current therapies. Although opiates are the most commonly prescribed medications to treat chronic pain conditions (e.g. cancer pain), their use pose important clinical risks such as abuse liability, diversion, and overdose. Other types of chronic pain (e.g., neuropathic pain caused by diabetes) are not well managed by either opiates or other approved agents (e.g., antidepressants). Currently, a significant amount of dollars is being invested in testing medications to treat chronic pain but the results of the studies have not yielded any significant progress in the treatment of this condition. There is an urgent need to conduct research that helps to understand the neurobiological mechanisms of chronic pain, which in turn will help to identify new targets and thus new compounds to treat this condition.
Unfortunately, it has been challenging to develop collaborations and much more to share resources among industry, academia, and government investigators to advance the study of chronic pain. A concerted and synergistic approach among those three groups will greatly advance the understanding and management of chronic pain. It is expected that the development of a venture fund for research and development will facilitate the collaboration among industry, academia, and government which will result in the discovery of new targets and the development of new medications to treat this condition.
The purpose of this program is to support eligible institutions that enter into a joint venture or collaboration with other entities which concomitantly provides support in the form of funds or resources to conduct research to advance the development of medications to treat chronic pain.
Research may focus on the discovery of new potential therapeutic targets, new molecules with action on those targets, as well as Phase I safety/tolerability studies, single or multisite Phase II or III studies, or translational projects.
Emerging scientific opportunity ripe for Common Fund investment: Currently, there are multiple individual efforts from industry, academia and government to advance the knowledge of the mechanisms of pain as well as the discovery and development of new pharmacotherapies; however, most of those efforts are not coming to fruition because of the lack of a coordinated and synergistic approach. This initiative is very timely because it aims at channeling all those efforts and making them more synergistic in achieving an ultimate goal of having safer and more effective medications to treat chronic pain
Common Fund investment that could accelerate scientific progress in this field:
It is expected that the identification and validation of novel targets associated with chronic pain can lead to novel and effective therapies. A pilot phase is proposed that if successful would go on to a therapeutics development phase to be done in collaboration with private sector partners.
Identification and validation of new therapeutic targets
Therapeutics Development Phase:
Identification of bioactive compounds for the new targets identified during the pilot phase
Early stage clinical trials
Potential impact of Common Fund investment:
The ability to effectively treat chronic pain conditions will impact more than 116 million Americans. In addition, identifying and validating chronic pain targets – may also lead to diagnostic tests that may prevent or delay the onset of chronic pain conditions.
Tags: therapeutics, pain, neurobiology, mechanisms, collaboration, industry, preclinical, clinical
Title of proposed idea: Translating Findings on Human Disease Risk Variants into New Interventions: Coordinated Studies for Therapeutic Target Identification (see “Beyond Genome-Wide Association Studies (GWAS)” in Innovation Brainstorm ideas)
Major obstacle/challenge to overcome: The potential to develop new therapies based on the expanding number of findings on human genetic variants’ relationships to disease risk is a well-recognized aspect of the potential for clinical progress stemming from advances in genomics (e.g., Green ED et al. , Charting a course for genomic medicine from base pairs to bedside; Nature 470; 204). A key early rate-limiting step in this pathway is identification of promising therapeutic targets based on epidemiologic findings on risk variants.
The Challenge: In a few cases (e.g., Crohn’s Disease), substantial progress has occurred from identification of risk variants to identification of therapeutic targets, providing proof-of-principle for this approach. However, to date, the number of new targets identified by this approach is limited. A major challenge to expanding and accelerating such efforts is the fact that, after an association between a variant and disease risk is established, a critical mass of additional information is needed to determine whether there is a sufficiently promising therapeutic target to justify proceeding with subsequent, generally costly, steps in therapeutics development, e.g., screening small molecules, identifying lead compounds, and pre-clinical studies. The studies needed to identify and evaluate potential targets span a wide range of research areas, including:
The breadth of types of studies required to obtain the needed critical mass of information and the need to integrate information from them poses a substantial challenge. The range of expertise required includes genetics, cell biology, physiology, epidemiology, and clinical expertise in specific diseases. Although it is likely that there will be many individual studies that explore one or a few such effects of various genetic variants, it is presently very uncertain that individual investigator-initiated NIH grant applications alone will frequently provide and integrate the critical mass and range of data regarding a specific genetic variant to justify a subsequent drug development effort. Assembling coalitions of investigators spanning the above disciplines and providing the needed infrastructure for data sharing and collaborative analyses is very challenging. Without an NIH initiative, these challenges, coupled with high uncertainty of funding, are likely to deter even experienced investigators from the considerable effort needed to develop applications for such projects.
This challenge also affects steps in therapeutics development downstream from target identification. There has been increased NIH support for structured programs to provide the infrastructure and coordination needed for small molecule screening and other steps focused on targets that have previously been identified. However, the steps from finding genetic risk variants through target identification have not been supported nearly as much by structured NIH programs, but rather have been left to investigator-initiated projects that generally address only isolated steps in the process. While investigator-initiated research has reflected enormous creativity and will continue to make important contributions, the efficiency of identifying targets for intervention might well be enhanced by support for a more integrated path of discovery. This proposal therefore calls for the testing of a complementary paradigm that supports continuity of research from genetic variant through target identification.
How to overcome this challenge: This challenge could be addressed by an initiative to support multidisciplinary projects, which would each obtain comprehensive information spanning the types of studies noted above, in regard to one or more variants associated with altered disease risk, and analyze this information to identify potential therapeutic targets and evaluate their potential for further development. This initiative would markedly enhance therapeutics development capabilities by these unique contributions in a crucial and currently unfilled niche.
Such studies could be supported through one or more Common Fund RFAs, with individual awards supported by the most appropriate IC, or multiple ICs if appropriate. Peer review considerations regarding selection of variants for these studies could include the strength of their relationship to health risk, the public health importance of the condition(s) they affect, and the potential for finding new therapeutic targets, based on current knowledge about functions of the gene in which the variant is found. If more active NIH planning is desired regarding the range of conditions and/or selection of genes on which such projects could be focused, one or more pre-RFA advisory workshops could be convened by a trans-NIH committee to identify particularly important foci. Such a workshop could also recommend criteria by which to evaluate the results of individual projects in regard to decisions about proceeding with subsequent therapeutics development steps after target identification.
Coordination among projects could be facilitated by annual meetings and a coordinating center. An independent panel could review progress of the projects with regard to established benchmarks and advise on the rationale for their continuation. Based on these reviews, the efficiency of the set of projects could be enhanced by withdrawing resources from studies showing less promise for finding good targets and increasing resources for those with greater promise.
How the proposed initiative would address this challenge and fill a gap in current efforts: The focused coordinated target identification activities described above would help to increase the rate of discovery of promising therapeutic targets above the current less-than-ideal level, by providing the incentives and organization for their efficient identification and evaluation and a mechanism for focusing on the most promising ones. The initiative would also address the challenge of fulfilling the therapeutic potential of findings on genetic risk variants by promoting substantial progress on the crucial early therapeutic development stage of target identification.
Further, this structured approach would enhance the potential of current structured programs focused on steps after target identification by increasing the number of targets for consideration at the beginning of their therapeutic developmental “pipelines.” By increasing the number of promising targets, it could also provide synergy with NCATS, by enhancing opportunities for NCATS activities focused on subsequent stages of therapeutics development.
Emerging scientific opportunity ripe for Common Fund investment: The increasing number of genetic risk variants identified by epidemiologic studies provides a well-recognized opportunity for the proposed activities to contribute to therapeutics development. Further, the many large population studies with extensive phenotype data, whose participants have already been genotyped, provide a cost-efficient platform for more detailed studies of specific genetic variants’ relationships to phenotypic differences. The expertise to conduct the proposed types of studies for target identification is available and improving rapidly. The potential contribution of such genetic findings and research expertise to target identification could be greatly enhanced by the proposed coordinated efforts to obtain a critical mass of information about selected variants and their effects.
Common Fund investment that could accelerate scientific progress in this field: Therapeutic target identification could be accelerated by a Common Fund investment in the types of projects described above. Ideally, these might be supported by a seven-year investment (one year for detailed planning and protocol refinement, five years of data collection, and one year for analyses). An interim evaluation of ongoing projects would be used to inform decisions of subsequent resource allocation, selecting those projects that would continue and those that would be revised or discontinued. Based on costs of the types of studies that would be included in the projects, it is estimated that studies on the effects of 20 variants could be supported by an investment of approximately $30M (direct costs) over seven years. The average annual direct cost would be approximately $4.3M, though first- and last-year costs would likely be lower, with higher costs in the intervening years. It is possible that the studies could be organized as a private-public partnership, which could expand resources and the number of targets identified.
Potential impact of Common Fund investment:
Identification of several new, well-validated, therapeutic targets by this program would have transformative, durable impacts that would persist after the Common Fund support ended. The program would have impacts in at least two domains:
Tags: therapeutics, genetics/genomics, epigenomics, model organism, epidemiology, data integration, phenotype
Title of proposed idea: A synthetic cohort for the analysis of longitudinal effects of gene-environment interactions
Major obstacle/challenge to overcome: Francis Collins has emphasized that there is a pressing need for a large-scale US prospective cohort study of genes and the environment, with a minimal sample size estimated at 500,000 (Collins, 2004; Manolio et al., 2006). However, even with a very large cohort, at least 7 to 15 years follow-up is needed to accrue enough incident cases to adequately power studies of common diseases (Burton et al, 2009).
Several NIH institutes support multiple high-quality cohort studies with rich biomedical, environmental, behavioral, and social data that, if strategically coordinated, could help elucidate how genes and environments interact to affect trajectories of health and disease (Willett et al., 2007). Recently, many of these cohorts have been extensively genotyped and some have undergone whole exome sequencing. Open sharing policies through dbGaP have created databases of extensive genotype data linked to phenotypes which are incompletely harmonized, and whose data standards vary significantly. More complex analyses could be served by a synthetic cohort with harmonized data. The longitudinal data included in these cohorts increase the reliability of measures and afford the examination of change phenotypes, which are crucial for understanding the causal pathways leading to changes in health. An integrated, harmonized, synthetic cohort will also facilitate the selection of genetically distinctive subpopulations for specific studies of genetic and environmental influences and their interaction. It would accelerate and support new approaches to discovery such as PheWAS (Denny et al., 2010; see also phenotype mining), fine-grained admixture mapping (Shriner et al., 2011), or conditioning on known risk variants to find potential gene-gene interactions via GWAS (Wijsman et al., 2011). Harnessing the potential inherent in these existing studies will allow us to analyze the roles played by life-style factors, social circumstances, and environmental exposures in modulating disease risk and progression.
We face a similar set of opportunities and challenges from the ever-growing number of patient registries, which increasingly include rich data, but where there is little consistency to date as to what data are reported or required. Investigators and patient advocacy groups alike express the need for creation of patient registries to facilitate research, but we are not yet getting the maximal benefit from the available data (Drolet & Johnson, 2008). In rare diseases especially, the ability to rapidly and accurately identify affected persons and to know enough about genotype and phenotype to be able to determine who might be informative for basic biology studies and who might be available for clinical trials is critical to the efficient conduct of research studies across the biomedical spectrum. The feasibility of greatly expanding current patient registries is reinforced by the fact that patients are increasingly willing to share their data by their participation in internet sites such as Patients Like Me (http://www.patientslikeme.com/), but these data collected through the sites have limited utility to researchers. Patients are also signing up to participate in research studies through sites such as Research Match at Vanderbilt (https://www.researchmatch.org/). The value of existing and proposed registries is severely limited by inconsistencies in data standards, ontologies, and policies for access and sharing and by the phenomenal inefficiencies of duplication of effort and investments. The need for such registries crosses multiple ICs.
Emerging scientific opportunity ripe for Common Fund investment: The initiative would systematically evaluate design issues for a synthetic cohort study of genes, environment, health, and behavior drawing from existing cohort studies which have rich longitudinal or early life data. The large collection of longitudinal NIH-funded cohorts that have valuable data on exposures throughout the life course have already had profound impact on medical, behavioral, and social science even before the genomic era. Many of these cohorts have added genetic and biomarker collections and now provide unparalleled opportunities for exploiting these epidemiological findings at a genomic level. To date, however, attempts to harmonize or synthesize data collection efforts among studies have been modest, in part due to resource constraints and IC boundaries.
Strikingly similar issues have arisen for patient registries, which have been proliferating rapidly both for rare and common diseases, with their goals now not limited to enhancing participation in clinical trials, but to enhancing our understanding of patient outcomes (e.g., AHRQ’s 2010 Registries for Evaluating Patient Outcomes: a User’s Guide). The common obstacle faced by both cohort studies and patient registries is a lack of harmonization among existing efforts. Essential analyses requiring large samples are frustrated by the lack of documented history of the creation of the registry, of common or documented consent procedures, of data standards, of common measures, and of time points for observation across the sample. Patient registries thus present an additional opportunity for trans-NIH harmonization. It should be possible to leverage the investment in the design of a virtual cohort to create a format for all NIH IC supported registries. With a common set of data standards, sharing of and access to data, all ICs could support registries within the virtual cohort system which would create efficiencies of scale and consistency of operations to greatly enhance the impact of individual IC investments.
With this initiative, we could also explore the possibility of collaborating with privately funded efforts (e.g., 23andMe) and cohorts funded by other government agencies. Although implementing the synthetic cohort design would require costs for harmonization and data collection, there are existing platforms for the former (e.g., P3G, www.p3g.org, resources developed by caBIG) and a growing number of freely available, high quality measures of phenotypes and exposures developed by NIH (e.g., instruments from the HRS, PROMIS, PhenX, and the NIH Toolbox). Furthermore, the HITECH and Affordable Care Acts have enhanced the prospects for the widespread incorporation of these measures in EHRs (e.g., the eMERGE network). This initiative is therefore well poised to create a time- and cost-effective harmonization plan.
The recent special issue of Science (Feb 11 2011) on large datasets emphasized that ”large integrated data sets can potentially provide a much deeper understanding of both nature and society and open up many new avenues of research”. Better organization and access to data is imperative to realizing emerging scientific opportunities, including the development of common metadata. We propose that the Common Fund be used to invest in the three most important planning activities for a future synthetic cohort project and explore the potential for extending this effort to cover patient registries.
Although we believe that in the long run the synthetic cohort would be cost effective given that ICs currently maintain the full costs of participating studies, there are uncertainties as to the actual per-participant costs of any follow-up CF activity, and it will be crucial during the pilot to determine the post-CF longer term maintenance costs of the synthetic cohort and identify a credible source of funds (e.g., costs could be distributed across ICs/agencies supporting the synthesized cohorts, buying the substantial benefits of harmonization). The target design would include a synthetic cohort of 500,000 well-phenotyped participants. We estimate the cost of this initial developmental and feasibility initiative at $2.0 million per year for two years, with funds to be divided between RMS needs and supplements to enable cohort study investigators to participate.
Should the initial process of creating a virtual cohort prove successful, efforts could be extended to include issues related to patient registries. The initial investment by the Common Fund could create a program for investment of IC-specific resources for global benefit at marginal increases in cost. The Office of Rare Diseases has recently a web-based Global Rare Diseases Patient Registry-Data Repository (GRDR) which could form the nucleus of the registry (see Forrest et al. 2010, and Rubenstein et al., 2010, for more information). To ensure maximal benefit, the NIH would require the following of all data deposited in a new, central Registry:
Potential impact of Common Fund investment: The plan for a synthetic cohort for the analysis of longitudinal effects of gene-environment interactions would establish the feasibility and cost of an intended scalable synthetic national cohort of people for discovery research in health and disease; in essence it would design an affordable but well-powered phenome-genome project as recommended at the recent NIH Innovation Brainstorm meeting. Because the founder cohorts are longitudinal, the synthetic cohort would provide rapid access to trajectory information as well as a richer characterization of the social, environmental, and genetic factors influencing health and health disparities. Leveraging such a system to harmonize new and existing registries would ensure ongoing value, and provide multiple benefits: increased sample size for study of the pathogenesis and treatment of rare diseases, the ability to study the overlap in pathogenesis of apparently unrelated diseases sharing an etiology (e.g., coronary artery disease and invasive melanoma; see Manolio, 2010), and the chance to observe g-e interactions for phenotypically specific or genetically overlapping disease states, identifying genetic and environmental “pathogens” that can be studied in a synthetic cohort. This planning activity could also inform the design of a de novo national cohort study, or determine to what degree a synthetic cohort could provide similar information while allowing a greater degree of innovation within individual studies to address questions within their specific areas of science.
If determined to be feasible, the development of a centralized Registry, within which IC and outside organizations could deposit secure and sharable data, would allow more rapid translation of basic science information into clinically useful knowledge, greatly improved data quality, and enhanced engagement of patient advocacy groups in support of research at minimal cost. The centralized process would facilitate communication efforts to ensure broad awareness of the resource.
Burton, P. R., Hanell, A. L., Fortier, I., Manolio, T. A., Khoury, M. J., Little, J. & Elliott, P. (2009). Size matters: just how big is BIG? Quantifying realistic sample size requirements for human genome epidemiology. International Journal of Epidemiology, 38(1) 263-273.
Collins, F. S. (2004). The case for a US prospective cohort study of genes and environment. Nature, 429, 475-477.
Collins, F. S. & Manolio, T. A. (2007). Necessary but not sufficient. Nature, 445, 259.
Denny, J. C., Ritchie, M. D., Basford, M. A. Pulle, J. M. Bastarache, L., and others. (2010). PheWAS: demonstrating the feasibility of phenome-wide scan to discover gene-disease associations. Bioinformatics, 26(9), 1205-1210.
Doplet B. C., Johnson K. B. (2008). Categorizing the world of registries. Journal of Biomedical Informatics 411009–1020.
Forrest, CB, Bartek, RJ, Rubinstein Y, and Groft, SC; The Case for a Global Rare Diseases Registry The Lancet, 377, 1057-1059.
Manolio, T. A., Bailery-Wilson, J. E., & Collins, F. S. (2006). Genes, environment and the value of prospective cohort studies. Nature Reviews Genetics, 7, 812-820.
Manolio, T. A. (2010). Genomewide association studies and assessment of the risk of disease. NEJM, 363, 166-176.
Science Staff. (2011). Challenges and Opportunities. Science, 331, 692-693.
Shriner, D., Adeyemo, A., Ramos, E., Chen, G., & Rotimi, C. N. (2011). Mapping of disease-associated variants in admixed populations. Genome Biology, 12, 223-231.
Willett, W. C., Blot, W. J., Colditz, G., A., Folsom, A. R., Henderson, B. E. & Stampfer, M. J. (2007). Not worth the wait. Nature, 445, 257-258.
Wijsman, E.M. Pankratz, N.D., Choi, Y., Rothstein, J. H., Faber, K. M. and others (2011). Genome-wide association of familial late-onset Alzheimer’s disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genetics, 7(2), e1001308.
Rubenstein, Y. R., Groft, S, C., Bartek, R., Brown, K., Christensen, and others. Creating a global rare disease patient registry linked to a rare diseases biorepository database: Rare Disease-HUB (RD-HUB), Contemporary Clinical Trials, 31(5), 394-404
Tags: human, cohort, genetics/genomics, data integration, data sharing, environmental
Title of proposed idea: Synergizing Omic Science with Patient Reported Outcomes
Major obstacle/challenge to overcome: Current breakthroughs in omic research have increased awareness that diseases are complex disorders arising in response to the interaction among multiple genes, cellular metabolites, and environmental factors. In addition, there is growing speculation that how a person experiences illness may be genetically predisposed. Hence, a new era suggests scientific progress towards innovations in health will require the integration of diverse information from biologic processes, physiologic pathways, and behavioral models in order to predict and treat disease, improve survival, manage symptoms and enhance quality of life. This symbiosis of disparate knowledge is necessary to ensure biomedical science improves health through its translation into practical clinical applications. Therefore, fostering synergy between omic science (genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics) and patient reported outcomes such as symptoms (‘‘sympt-omics ‘’) and health-related quality life (HRQL) may promote new pathways for reducing burdens associated with chronic illness and enhance personalized health
Emerging scientific opportunity ripe for Common Fund investment: Progress in this area promises to fill gaps in broadening the links between genetic and molecular variants and patient reported outcomes in chronic illness. This opportunity will: 1) support cross-cutting research with substantial potential to create new perspectives for reducing the illness burden of chronic health conditions; and 2) encourage collaborative teams of diverse, interdisciplinary investigators to tackle the complex health and research challenges posed by chronic illness and to turn their discoveries into practical solutions for patients.
Potential impact of Common Fund investment: Synergy of omic profiling with practical application to patients will allow new pathways for improving treatment outcomes to emerge and ultimately reduce the chronic illness burden and enhance personalized health across the lifespan.
Tags: new methods, genetics/genomics, proteomics, metabolomics, epigenomics, symptoms, patients, biomarker, intervention
Title of proposed idea: Regulatory Science Initiative
Major obstacle/challenge to overcome: The rapid pace of scientific discovery coupled with development of new technologies presents a challenge to researchers, clinical investigators, and regulators as they work to translate basic scientific advances into approved medical products. Basic and preclinical research has been performed in large part independent of regulatory issues. In addition, it is clear that novel technologies and approaches to medical research are outpacing the ability of our regulatory system to incorporate them into current review practices and guidelines. To overcome these obstacles, NIH should support strategic initiatives that are essential to the translation of NIH funded discoveries into diagnostic and therapeutics.
Emerging scientific opportunity ripe for Common Fund investment: An investment in Regulatory Science will benefit all stakeholders by helping to advance and incorporate cutting-edge science into regulatory decision making and helping to develop improved tools, standards and approaches for assessing the safety, efficacy, quality and performance of medical products. Major advances in genomics and genomics-based medicine are also creating potential scenarios in the clinical setting that are relatively new to the FDA regulatory process. Moreover, the unprecedented partnership between the NIH and FDA through the Joint Leadership Council provides an extraordinary opportunity to coordinate therapy development efforts, including regulatory decision-making guidelines, between the two agencies.
Common Fund investment that could accelerate scientific progress in this field: A number of scientific opportunities are ripe for investment in the area of Regulatory science and across the therapeutics development pipeline. For instance:
Potential impact of Common Fund investment: Pre-clinical and clinical investigators, and other researchers who are engaged in the diagnostics and therapeutics development industries will benefit from having a more rapid integration of evidence-based knowledge into a regulatory framework, thereby quickening the pace at which basic science advances can move into the therapy development realm. For instance, in the area of stem-cell technologies, the NIH and FDA are working together to identify and define markers and characteristics of “stemness”, thus providing standards that the entire field can use for purposes of comparing studies and preparing for regulatory considerations. The possibility of individualized, autologous utility of stem cell-derived therapeutics, organs, tissues, and other biomedical products are fast becoming a reality. Other emerging areas in regulatory science will advance, such as nanomedicine, personalized medicine, efficient and expeditious clinical trial designs, predictive toxicology, and biomimetic models that are able to simulate human conditions and better predict safety and efficacy. The NIH-FDA joint efforts in these areas would help to pave a clearer and more transparent scientific and regulatory path for the scientific community that will impact therapeutics product development and clinical practice.
Tags: new tools, computational, regulatory, predictive model, toxicology, diagnostics, clinical, translational
Title of proposed idea: NIH Global Research Administration and Training Networks (GRAT-Net)
Major obstacle/challenge to overcome:The NIH has dramatically increased its funding of scientific and research training programs in developing countries to tackle a wide variety of global health challenges. Evidence-based solutions to address these challenges require establishing a research infrastructure in resource-poor organizational settings that can employ the best research practices – both methodologically and administratively. While many foreign investigators are trained in the newest scientific theories and research technologies, they and their home institutions may have little direct knowledge of, or experience with, best practices for project management, research oversight, data management, and fiscal accountability. Advancing research and improving the health of all of the world’s people will demand not only investments in the best science, but also investments in a sustainable research enterprise that can effectively and efficiently use and monitor research resources. It takes collaboration and communication between skilled individuals from various functional backgrounds, with access to proper resources, to support the best research management practices.
Emerging scientific opportunity ripe for Common Fund investment: The increased NIH focus on global health only magnifies the need to develop networks of research administrative professionals and research-adept institutions within developing countries that can support and sustain investments by NIH and other funders over time. This initiative will use the NIH Common Fund and collaborative expertise to build Global Research Administration and Training Networks (GRAT-Net) to address this challenge. The GRAT-Net program would provide state-of-the-art information on grants/contracts management and policies and would train individuals from across the research enterprise to deal effectively with the evolving nature of research administration. GRAT-Net would also provide the newest technology for research administration and help to integrate research administrative capacity within and across institutions, while also forming a series of collaborative research networks in low- and middle-income nations.
Common Fund investment that could accelerate scientific progress in this field: The GRAT-Net program would build upon the success of the NIH International Extramural Associates Research Development Award (IEARDA), which uniquely targets administrative capacity building at public and private universities in sub-Saharan Africa and India. It would also build on the expertise and experience of other IC-related efforts, such as Fogarty International Center’s Medical Education Partnership Initiative (MEPI) and its work with the President’s Emergency Plan for AIDS Relief.
Phase I of GRAT-Net awards would focus on establishing core research management teams at international institutions (“nodal institutions”) that would then network with local or regional institutions (see Phase II below). A Principal Investigator would head an initial team of several individuals to be trained, through residency or other programs, in research administration. The team would develop strategic plans for their own institution and a future GRAT-Net consortium to serve a specified geographic area. In addition, the team and home institution would be:
Phase I would also include:
Phase II – Creating the GRAT-Net beyond the Nodal Institution
In Phase II, nodal institutions would be expected to develop working collaborations with institutions with varying levels of research capacity, locally or regionally. This could include:
Potential impact of Common Fund investment: The GRAT-Net program would allow the NIH to reduce duplication of fiscal and human capital currently dedicated to international research and consolidate resources to ensure the ongoing success of global research investments. Ensuring sustainable and knowledgeable research administrative capacity will increase the number of institutions, in low- and middle-income countries, that can contribute to the research enterprise successfully. The initiative would also increase the efficient and productive spending of NIH resources, expand the number of geographic areas in which the NIH could invest successfully, and encourage a broader array of funders, including non-governmental organizations, to collaborate in research efforts. Most importantly, the program will help the NIH more confidently target its global health research investments, not only to great science, but also to international institutions in areas with the greatest health needs.
Tags: global, workforce, training, infrastructure, administration, network, community
Title of proposed idea: NIH Award Strategies
Nominator: Innovation Brainstorm participants
Major obstacle/challenge to overcome: A common theme during the online discussion prior to the Innovation Brainstorm meeting and at the meeting itself was how to bring together disparate fields of science. Despite recent Common Fund (CF) efforts and programs across the NIH, the formation of teams and integration of multiple disciplines remains a major barrier.
Common Fund investment that could accelerate scientific progress in this field: Potential CF Investments include:
Tags: workforce, mechanism, training, collaboration
Title of proposed idea: Molecular Phenotypes for Genome Function and Disease (see "Beyond Genome-Wide Association Studies (GWAS)" in Innovation Brainstorm ideas)
Major obstacle/challenge to overcome: Understanding how the human genome functions and how it is influenced by genetic variation in health and disease are major challenges of wide interest across NIH. The Innovation Brainstorm meeting suggested this area in “Beyond GWAS”: “Establish a functional genome project that leverages functional information to find causal variants — employing ENCODE, epigenomics, and functional genomics strategies”. Several projects are addressing pieces of these challenges but none in the comprehensive manner required. GWAS studies have found thousands of human genomic regions associated with disease, but definitively identifying which genomic variants and elements in these regions are causal, rather than simply correlated, is a major challenge for the field. Mapping GWAS hits to functional elements catalogued by ENCODE and other efforts are providing some insights, but determining the causal links and understanding the mechanistic underpinnings are still very difficult with current resources.
Several critical gaps exist, including limited knowledge of variability between individuals for a range of molecular phenotypes; the correlations in molecular phenotypes across tissues; variability in somatic genomic changes/mosaicism among tissues within individuals; the influence of environmental exposures (e.g., diet, toxins, stress) on molecular phenotypes; and the molecular phenotypes of cell types in vivo. Furthermore, integration of data across these and other projects (ENCODE, CF Epigenomics, CF GTEx, etc.) and with GWAS and other disease studies is lacking.
The field needs experimentally tractable systems to generate integrated and comprehensive data resources to study gene function and how genetic variation leads to differences in function and disease.
Emerging scientific opportunity ripe for Common Fund investment: Recent improvements in high-throughput molecular assays and the availability of rich model organism resources provide an opportunity to interrogate gene function in vivo at an unprecedented level of detail. The cost of this project is much lower than it would have been even a few years ago since many of the technologies for molecular phenotyping, such as RNA-seq, ChIP-seq, and DNase-seq, are based on sequencing, the cost of which continues to decline rapidly.
This project would be synergistic with existing and new projects, some of which are already supported by the Common Fund and by ICs:
Common Fund investment that could accelerate scientific progress in this field: The Common Fund could invest in the generation and analysis of multiple molecular phenotypes in model systems such as mice, rats, and flies. This resource would include measurement of gene expression and multiple additional molecular phenotypes (epigenomic marks, chromatin accessibility, transcription factor binding sites, etc.) in completely sequenced strains of model organisms. Using model organisms would allow access to a full range of tissues in different developmental, environmental, and disease states. The mouse Collaborative Cross (CC) and Knock-out Mouse Project (KOMP) are two resources upon which one could build this project, but they are not the only ones. The data set would show the correlations among the molecular phenotypes across tissues, to allow predictions based on the more accessible tissues.
The product of this project would be a public data resource to support work to interpret how variants, genomic elements, environmental factors, and molecular phenotypes are related, as well as proof-of-principle examples for predictive models of gene function. With this resource one could predict which genes and genomic elements are causal for phenotypes and how the elements interact. Experiments could test these predictions and determine the response to additional genetic or environmental perturbations in vivo. Relevance to humans could then be examined with focused studies, using resources like the Common Fund Genotype-Tissue Expression (GTEx) project. For example, mouse studies might show that correlated pancreatic, liver, and muscle chromatin states are associated with risk for Type 2 diabetes, in particular genetic strains and dietary environments. These states and associations could then be examined in humans with efficient, narrowly focused, molecular studies in the relevant tissues and donors.
Many strains, cell types, and developmental stages, in a range of environments (such as various diets, smoking, environmental toxins, sun, and psychosocial stress) could be studied. The molecular phenotypes that would be surveyed in the model organisms include:
The data would be freely available to the scientific community. The project would also require the development of improved computational analysis methods for integrating the multiple data types, predicting functional elements, and understanding how variation in function arises from sequence differences. Although the main data production effort would be generating the sequence and molecular phenotypes, some pilot projects would focus on using these data to predict which genomic elements are causal for some diseases or traits that are shared by humans and the model organisms.
This proposal is related to, but distinct from, the GTEx, ENCODE, and CF Epigenomics projects. While GTEx directly studies human tissues, it has limitations on the ability to control post-mortem effects, a limited range of developmental stages that can be studied, and an inability to control and manipulate environmental and genetic factors. The animal models proposed here, on the other hand, allow great flexibility to control and manipulate the genomes and environment in many animals, in order to identify mechanistic relationships between the genome and multiple phenotypes. The ENCODE and CF Epigenomics efforts are focused on developing the reagents and standards for characterizing functional elements in the genome and cataloging them in a small set of reference cell lines and tissues. The project proposed here leverages these efforts by applying them to experimental organisms in which to make causal inferences and testable hypotheses of genome function, by looking at a large set of tissues in many individuals, developmental stages, and in several environments. This proposed project is much more extensive and comprehensive than the current reference projects.
Possible extensions to this project:
This project could expand to include:
Potential impact of Common Fund investment: This project would produce a valuable resource of data sets and tools for understanding genome function, disease biology, and risk prediction in experimentally manipulable systems. Having these data sets in model organisms would allow researchers to study which genomic elements are mechanistically causal, not just correlated, for how the genome brings about phenotype. Once causal mechanisms in the model organisms are discovered, focused studies in humans could be carried out to test the predictions. Knowing the causal genomic elements and variants would allow researchers to study how they function in health and disease, to make accurate risk predictions, and, to develop therapies based on this mechanistic understanding.
Tags: genetics/genomics, epigenomics, molecular phenotype, animal, model organism, database, data integration