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Potential benefits and challenges in incorporation of informatics approaches and/or integration of large protein datasets into the development of the proteomic technologies.

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.

Pilot Phase:

            Identification and validation of new therapeutic targets

Therapeutics Development Phase:

            Identification of bioactive compounds for the new targets identified during the pilot phase

            Pre-clinical studies

            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. 

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)

Nominator: NIH Institutes/Centers

 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. [2011], 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:

• Deep sequencing in the region of the identified risk variant, to examine more closely which sequence variants are related to disease risk and identify additional variants with similar or opposing effects
• Intensive clinical and physiologic phenotyping of individuals with and without the variant to ascertain potential pathways to target
• Determination of the effects of the sequence variant (either in a gene or in an intergenic region) on gene products, levels of expression, and/or differential responses to factors regulating expression
• Identification of tissues in which the gene is expressed and differences among tissues in the degree to which the variant affects gene expression and tissue function
• Development and use of in vitro assays or tests in animal models to study variants’ functional effects and effects on responses to drugs
• Assessing how the variant may differ in susceptibility to epigenetic modifications and the effects of these differences

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:

• Most obviously, it could advance subsequent therapeutic development and testing (either by private sector or NIH support) on the targets identified by the Common Fund program after this program was completed. (It is also worth noting that this approach could identify targets that might not have been pursued by the private sector for financial or other reasons, but which nonetheless merit further investigation because of their public health importance.)
• More generally, the project would allow acquisition of experience with this coordinated approach to target identification and validation, and the formation of research teams with expertise in these new approaches. Such teams, and others modeled on them, could provide important contributions to the translational missions of various individual ICs, which could be supported by these ICs long after the Common Fund project ended. 

Title of proposed idea: A synthetic cohort for the analysis of longitudinal effects of gene-environment interactions   

Nominator: NIH Institutes/Centers

 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.

Common Fund investment that could accelerate scientific progress in this field:

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.

• Catalog and prioritize the existing NIH cohorts and form a harmonization plan for these studies.
• Develop the analytical framework and bioinformatics infrastructure that would be required for the future synthetic cohort. This will include executing calibration studies to create crosswalks between measures, especially among longitudinal studies with rich behavioral, clinical, and biological phenotypes, and adding EHR information to the data where feasible.
• Develop plans for data-sharing and consent policies and, crucially, detailed scenarios for the cost of synthetic cohorts of varying sizes and intensities.

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:

• Consent would require data sharing, with the level of sharing (controlled versus generally available) determined by the degree of PII in the data.
• Data standards would be established by groups with appropriate expertise. Only data meeting the pre-defined standards would be accepted.
• Access and use criteria would be established consistent with levels of PII.
• Provision would be made for withdrawal of data from the Registry in response to participant request.  

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.

References

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

Title of proposed idea: Synergizing Omic Science with Patient Reported Outcomes  

Nominator: NIH Institutes/Centers

 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.

Common Fund investment that could accelerate scientific progress in this field:

• Identification of molecular biomarkers and other classifiers to assess individual susceptibilities to variations in patient reported outcomes such as symptoms and HRQL.
• Development of novel methods, including modeling approaches and use of emerging technologies to measure and predict associations among omic variants and patient reported outcomes, such as symptoms and HRQL.
• Design and testing of tailored interventions to improve treatment outcomes that are founded in the linkages among omic variants and patient reported outcomes, such as symptoms and HRQL.

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.