of Program Coordination, Planning, and Strategic Initiatives
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. , 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: 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
Title of proposed idea Centers for Research and Training in Quantitative and Systems Pharmacology
Major obstacle/challenge to overcome: The focus of most of modern drug discovery has been on creating Ehrlich’s “magic bullet,” a drug that would hit only one target in the body effecting the desired change. This model has been enormously helpful and led to many drug successes. Yet even today, ninety percent of investigational drugs—i.e., those approved for trial—fail before being approved for use in patients. Many of these failures occur at the Phase II clinical trial level of development, making the failures very costly, and the majority fail for reasons of lack of efficacy. Furthermore, all drugs have unintended effects as well as intended ones, and drugs show person to person variability in their effectiveness and toxicities. Clearly, we can identify potential targets and make high affinity ligands for those, but we lack 1) a comprehensive knowledge of the role of these targets in human physiology and disease, 2) a quantitative and multi-scale understanding of how the targets modulate each other, and 3) how hitting more than one target sums to produce an observable phenotypic change. Industry scientists confirm that these are major impediments for drug discovery and development in most therapeutic areas.
We submit that there is a profound need to make a major shift in our approach to drug discovery and understanding drug action to fill in the context within which targets operate and how they produce their therapeutic action and side effects. This proposal is not meant to encompass all of systems biology or pharmacology, but to add a more quantitative and integrative perspective to allow a systems level understanding of drug action. Academic pharmacology, for the past thirty to forty years, has been largely focused on molecular pharmacology, providing an in depth understanding of individual molecular targets within the body. There has also been a diminished level of academic research in clinical pharmacology, the discipline most aligned with understanding drug action. There is now a timely and urgent need to stimulate Quantitative and Systems Pharmacology (QSP), an emerging discipline that proposes to build from an understanding of a drug's molecular interactions to an understanding of its temporal and dynamic modulation of cellular networks, impact on human pathophysiology, and optimal use in the clinic. QSP builds upon classical and molecular pharmacology by adding omics approaches not available in earlier periods and recent modeling approaches that enable the deciphering of high volume data analysis. It adds the horizontal integration and numerical quantitation of biological processes and mechanisms provided by systems biology and the vertical integration and statistical approaches characterized by PD/PK modeling and clinical pharmacology. It is necessarily multi-disciplinary and highly integrative, operating across the biological hierarchy from biochemistry and cells to tissues and whole organs to animal studies and human patients. Furthermore, for research to move rapidly in this emerging area, there must be a scientific workforce to drive it; currently, this is also an underserved need.
Emerging scientific opportunity ripe for Common Fund investment: This recommendation arises from two workshops and a follow up white paper held at the NIH in 2008 and 2010 (http://meetings.nigms.nih.gov/?ID=8316) with participants from academia, industry and government. The stimulus for the workshops was the lack of integration taking place between the pharmacology and the systems biology being supported by NIH and the need to address the poor success rate in drug discovery and development. The workshops brought together researchers in pharmacology, systems biology, pharmacokinetics/pharmacodynamics, computer modeling and related areas with a focus on how systems biology was contributing to drug discovery and understanding of drug action now and in the future. The major result of the first meeting was a strong recommendation to repeat the workshop. The attendees recognized that they had something to offer each other, but felt they were currently far apart. The second workshop focused on three different therapeutic areas with the idea that they could learn from each other’s successes and failures. That QSP encompasses cells, organs, and virtually all therapeutic areas, with underlying principles to be discovered that span all these makes it highly appropriate for a Common Fund proposal. The opportunity now exists to bring together researchers in these various areas in a common effort to expand our knowledge of drug action beyond drug target interactions to an understanding of how to use drugs alone or in combination to control biological systems that can produce shifts between disease and healthy phenotypes.
Common Fund investment that could accelerate scientific progress in this field: The purpose of this Common Fund idea is to promote the use of QSP approaches for the study and elimination of a major roadblock in drug discovery and development, the complexity of drug targeting. The centers mechanism is recommended to facilitate collaborative development of pioneering research, research training, and outreach programs in this emerging area and therefore stimulate the field as a whole. The focus of the centers should be the generation and testing of new ideas in QSP. The primary justification for centers is the need for integration of research plus training, and integration across levels of biological organization, across scientific disciplines, and across therapeutic areas. We believe that a community is emerging from the cognate disciplines that is highly motivated and ready for this effort to begin. Initially, there will be limiting factors as outlined below in areas for exploration, but there exists the opportunity to package what exists and leverage it to greatly boost research in this area. It is also suggested that the opportunity exists to employ industry academic partnerships to fully engage the expertise and creativity of industrial partners in idea generation and testing that will benefit all sectors.
Some of the immediate needs in QSP include the following areas thought to be ripe for exploration via collaborative/integrative centers: 1) the quantitative characterization of drug targets; 2) factors affecting patient response variability; 3) better animal and tissue models; 4) re-connection of medicinal chemistry and tissue pharmacology; 5) information exchange formats extending from chemistry to electronic medical records; 6) better computational models with pharmacological mechanisms; 7) development of systems approaches to failure analysis; 8) defining of core competencies for training in QSP; 9) development of pedagogical resources; 10) novel formats for training, including that for established investigators; and 11) novel academic industry partnerships covering research and training.
Potential impact of Common Fund investment: The primary results of a successful QSP centers program are expected to include: 1) major advances in the fundamental understanding of how drugs act; 2) more direct translation of discoveries made in cells to patients; 3) improved biomarkers that assay directly the effects of drugs in tissues and patients; 4) a stronger scientific basis for multi-drug therapy and re-purposing of existing drugs and drug candidates abandoned in development; 5) a more rational basis for polypharmacy and predicting drug-drug interactions; 6) a higher success rate for new drug candidates successfully entering the market place with acceptable toxicities and predictive variability among patient types; 7) higher rates of success of clinical trials; and 8) a stronger investigator pool for academia and industry and a new generation of leaders in academic and industrial pharmacology.
Tags: pharmacology, clinical, drug discovery, systems biology, quantitative, model organism, animal, computational, workforce
Title of proposed idea: Group Effects
Nominator: Innovation Brainstorm participants
Major obstacle/challenge to overcome: Exposures are highly variable and dynamic throughout the lifetime of an individual. Needed are systematic, unbiased screens for studying how multiple factors (e.g. microbiological, chemical, lifestyle and dietary exposures) interact to contribute to susceptibility to disease, disease progression, and treatment outcomes. In addition to curating/annotating data obtained using current models, improved testing systems are needed that are equipped to analyze multi-factorial issues.
Emerging scientific opportunity ripe for Common Fund investment: Several opportunities exist to address the need for better models and analytic tools. These include the availability of inexpensive exposure screening tools (e.g. virochip, protein adducts) and bioinformatic techniques that can handle large, clinical datasets to track exposures. The development of screening tools, methods, and model systems that are particularly well suited for studying mechanisms of environmental influence also provide opportunities in this area. Point-of-care tools are likely to be especially useful to monitor exposure in global and other low-resource settings. Expanding these toward multiplex capability is another opportunity.
Common Fund investment that could accelerate scientific progress in this field: The Common Fund (CF) could shift the curve to accelerate progress by expanding the number and quality of tools to systematically measure multiple exposures and by supporting the development of computational tools that will support multifactorial research: viral, bacterial, chemical, and dietary. Data handling for these types of studies is an enormous challenge. A database that catalogs and characterizes model systems that are suitable for studying multifactorial research would also be helpful.
Potential impact of Common Fund investment: Implementing projects in this area could have significant impact in helping to better clarify the age-old question of the relative influences of “nature and nurture;” yet, it would go further by ultimately explaining how complex mixtures of genetic loci and environmental exposures influence health and disease susceptibility. In time, these insights will point to preventive strategies that help to fulfill the goals of personalized medicine.
Tags: new tools, computational, model organism, drug screening, predictive model, environmental