of Program Coordination, Planning, and Strategic Initiatives
Title of proposed idea: Regulatory Science Initiative
Nominator: NIH Institutes/Centers
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: Meeting the Challenge of Big Data in Biomedical and Translational Science (see “Cross-Cutting Issues in Computation and Informatics” in Innovation Brainstorm ideas)
Major obstacle/challenge to overcome: The complexity of human biology in health and illness is increasingly being taken into account by research design, with individual studies collecting genomic, image, biosensor, and clinical data, along with information about sociocultural and environmental factors. And, these large amounts of diverse data are almost always collected in digital form. Thus, modern biomedicine is confronted at once by great opportunity and great challenge. The opportunity presented by collecting multiple measures is to understand disease and gain insight to its prevention, treatment and cure, from a broad, encompassing perspective more likely to bear fruit than from studies limited to a small number of measures. The opportunity presented by collecting digital data is the ability to share, compare, reaggregate, reuse, and integrate data, as well as to use these data for models and simulations in ways that have been heretofore impossible. The challenge, however, is to be able to organize, present, analyze and manage these data to fully realize such opportunities. The challenge is one of “big data,” where handling and working with complex data at large scale is both quantitatively and qualitatively different than at a smaller scale.
Emerging scientific opportunity ripe for Common Fund investment: As the translation of biomedical research results into improved human health accelerates, and as the diversity of clinically-relevant measures grows to include those of basic biology, new approaches to big data, drawing from information science, informatics, computer science, and computational biology, must be developed and used to maximize the return on the research investment. Advancing the science of big data and developing associated tools requires test-beds to stimulate and shape conceptual progress and its reduction to practice. While this has happened in other fields, such as astronomy (which has benefited greatly as a consequence), a concerted effort to move these ideas and tools forward has not yet been made in translational biomedicine. The number, size, and scope of biomedical and translational research projects collecting large amounts of different types of data is now sufficient to offer numerous test-beds that would be demanding enough to move forward the science of big data and developing a big data research environment. The time is right for seizing the opportunity these test beds offer to drive the development of big data approaches in the context and service of translational science.
Common Fund investment that could accelerate scientific progress in this field: This initiative would support the research and development of a big data research environment for each of several sites hosting translational science projects, collectively spanning analyses that might include genomic, image, sensor, clinical, sociocultural, environmental, and electronic medical record data. Some examples of elements likely to be developed under a given award include, but are not limited to:
Awards would be made to support such integrated efforts to advance the science of big data and build a big data research environment associated with sites at which large clinical research projects and clinical trials are typically ongoing. While this initiative could be implemented in any of a number of ways, one possible implementation would be the use of cooperative agreements. The methods, results, progress, setbacks, and lessons learned would be shared among all of these cooperative agreements in an ongoing way so as to allow for an adaptive project process.
Potential impact of Common Fund investment: Informatics approaches currently used have largely been developed in the context of more limited data types and amounts than large translational science projects are now producing. A big data research environment built around, and assuming, such large, multidimensional studies producing gargantuan amounts of complex data would represent not only a quantitatively different understanding, but a qualitatively different understanding of the basic biology of health and disease. This new understanding, based on an integrative perspective from omics to the environment, would, in turn, provide new insights to improve human health, as well as clinical and public health decision-making.
Tags: new tools, computational, database, biomedical data, translational, workforce
Title of proposed idea: Disruptive Proteomics Technologies: Comprehensive Protein Identification in Clinical Samples
Major obstacle/challenge to overcome: Our ability to detect and quantify proteins in complex (e.g., clinical) samples is progressing steadily, but it is increasingly clear that order-of-magnitude improvements in the associated technologies would enable very significant advances over a range of biomedical research areas. In other word, the current state-of-the-art is good, but limiting. A few of the specific limits are:
Although NIH does fund some technology development in this area, there are not programs specifically aimed at development of so-called “disruptive” technologies, i.e., those that could afford very rapid, very significant gains, similar to those that occurred in DNA sequencing technology.
Emerging scientific opportunity ripe for Common Fund investment: The history of technology development for genome sequencing teaches that successfully fostering very significant technological advances in basic methods and instrumentation requires a sustained effort, significant funds, encouragement of diverse approaches, a tolerance for taking risks (moderated by ongoing evaluation across the portfolio) and very focused, precisely articulated, assessable program goals.
We propose an analogous technology development effort that aims to produce order-of-magnitude improvements in the detection, identification, and quantification of proteins in complex samples. Moreover, the effort would explicitly emphasize an end-point relevant to clinical applications.
Several NIH institutes do fund technology development in this area. However, the program proposed here is justified as a Common Fund effort both because its benefits will cut across NIH (see below) and because it requires concerted management of all the grants under one program towards
precise program goals (see below) to maximize the chances for success. Similarly, it is important that this proposed program not be combined with other technology development efforts.
Common Fund investment that could accelerate scientific progress in this field: In a long-term technology development effort such as the one proposed here, it is difficult to anticipate what basic methodology holds the best potential for very significant improvement. The current dominant methodology for high-throughput detection and quantification of proteins is mass spectrometry (MS); it holds good potential for further incremental improvement, and it is possible that order-of-magnitude improvements could be stimulated by a well-targeted program. In addition, there are other technologies that hold promise for significant improvements, though they are currently less developed than MS, and not well-supported. We therefore propose projects covering both MS and non-MS approaches.
Specific funding components proposed:
FOA 1: Technology Development: MS-based protein ID and quantitation . (Years 1-5)
i. 10-fold decrease in instrumentation cost (e.g., a $50,000 mass spectrometer)
ii. 100-fold or 1000-fold increase in dynamic range
iii. 10-fold increase in throughput
FOA 2: Technology Development: Non-MS-based protein ID and quantitation. )
i. Develop protein ID/quant technologies that approach/exceed MS-based methods with respect to: accuracy, dynamic range, throughput, cost, and ability to analyze PTMs.
ii. Demonstrate orders-of-magnitude improvements with respect to dynamic range, throughput, cost.
FOAs 1 and 2 would need to justify their approaches relative to eventual advantages for translational or clinical use, for example:
- Improved discovery and/or assessment of biomarkers
- Rapid sample turn-around time
- Small input volume (1 mL of blood, etc.)
- Stored or banked samples, resilience to sample handling variability
- Analysis of clinically relevant sample sizes (100s to 1000’s), with no loss of specificity
For FOA’s 1 and 2 it is likely that a phased approach with milestones will be advantageous for incenting rapid development and managing risks. For example these FOA’s might encourage many applications and a high level of risk/reward with an initial three-year period, followed by an option to renew for a larger amount of funds contingent on reaching milestones.
One issue not explicitly considered above is the development of computational tools for data analysis and integration for large protein datasets. There are advantages in asking that this be integrated into the development of the technologies, and also the alternate approach of writing a separate FOA. Staff will need to research this issue.
If FOA’s 1 and 2 are successful, a follow-on FOA focusing on specific clinical applications would be considered.
Potential impact of Common Fund investment: Orders-of-magnitude improvements in this area would enable very significant advances across the NIH portfolio. In basic research, it would enable the assessment of all proteins in a mixture; which in turn would enable, for example, more comprehensive assessment of gene expression, now largely inferred indirectly from RNA expression. In discovery research, it would enable a more comprehensive assessment of the molecular consequences of variation (eg, an addition to GWAS, GTEx, large cohort studies); for translational research it is likely to afford many advantages for disease biomarker discovery and assessment. Finally, if all the goals are realized, there are clear ramifications for the clinic (patient sample testing, drug response/disease progression, etc.).
Tags: new tools, computational, proteomics, protein complex, quantitative, clinical
Title of proposed idea: Developmental Origins of Health and Disease: Disease Prevention Across Generations
Major obstacle/challenge to overcome: It is clear that many complex diseases and conditions result from a combination of genetics and environment. What is not clear is when and how this interaction of genetics and environment actually leads to disease. The concept of developmental origins of health and disease (DOHaD) is a fundamental principle underlying many chronic diseases and conditions in children and adults. Decades of DOHaD studies suggest that a wide variety of early exposures occurring during periods of time where tissues and organ systems are developing markedly increase risk for (or even cause) disease across the life course. These “environmental” exposures are varied and include drugs, nutrition, chemicals, stress, microbes or viral infections. Examples of these non-communicable diseases and disorders (NCDs) include obesity, type II diabetes, insulin resistance, asthma, cardiovascular diseases, dyslipidemia, cognitive and behavioral disorders, neurodegenerative diseases, a variety of cancers, and reproductive disorders. Disadvantaged populations may experience greater exposure to these hazards and exhibit higher rates of disease incidence, morbidity and mortality. Understanding and modulating this risk in humans during critical windows of development offers the promise of primary prevention for many of these NCDs and may result in reducing health disparities.
Although the range of diseases and conditions believed to result (at least in part) from early life exposures spans nearly all of the NIH Institutes and Centers (ICs), the concept of developmental origins has yet to be broadly adopted as a new research paradigm. A trans-NIH program funded by the Common Fund (CF) will support research to 1) characterize early life exposures and their health effects in a comprehensive way, 2) encourage cellular and molecular research on the mechanisms of these triggers (or stressors) on development, and 3) encourage the development of targeted interventional studies in human subjects. The initiative would result in a new awareness among researchers funded across the NIH to consider the role of developmental stressors in triggering the diseases and conditions they study. This would also jumpstart the field of transgenerational inheritance, i.e., the transmission of environmentally-induced phenotypes to subsequent generations without direct exposure. This phenomenon is well-described in non-mammalian systems, but despite the existence of several published examples, it remains highly controversial whether it truly occurs in humans or rodents, or how common it might be. Finally, the initiative would build the body of science needed to strengthen prevention research, an important element of health care reform.
Emerging scientific opportunity ripe for Common Fund investment: No single IC has the capability or the expertise to integrate all the approaches and technologies needed to assess how genetics and multiple environmental triggers (or stressors) combine to affect health across the lifespan and even across generations. Furthermore, early life exposures even to a single stressor can lead to adverse health outcomes affecting multiple tissues and organ systems that are not readily appreciated in traditional single institute research programs. For these reasons, adopting a coordinated, trans-NIH approach is a critical step in changing existing paradigms about the etiology of a variety of diseases and conditions, and transforming this information into knowledge that targets the most advantageous times, and possible interventions, to prevent their occurrence. This program would also leverage and integrate current CF and trans-NIH initiatives such as those on epigenetics, exposure biology, genetics/genomics, the microbiome, bioinformatics, developmental biology and programming, stem cell development and differentiation, and animal, epidemiologic and clinical assessment of disease.
Common Fund investment that could accelerate scientific progress in this field: We propose a CF program that will transform thinking about disease prevention: a comprehensive investigation of the developmental basis of a wide range of diseases and conditions. Such a program would enable the NIH to identify a host of environmental stressors that increase disease risk, and the mechanisms by which these exposures alter normal developmental programs, manifesting in disease or conditions, years and even generations later. Moreover, a comprehensive research program focused on the developmental origins of disease would enable scientists to pinpoint susceptibility windows – unique developmental time points at which humans are most susceptible to the combined effects of environmental exposures and genetic factors. Identifying these developmental windows and developing predictive biomarkers of exposure will dramatically increase our ability not only to understand disease etiology, but also to develop intervention strategies that will ultimately prevent disease, by reducing exposure. Possible investments include:
1. Develop centralized, well-characterized, novel models and clinical research designs and analytic techniques that would promote effective multigenerational analysis and would leverage existing CF investments and infrastructure, such as that in mouse phenotyping (KOMP2).
2. Identify stressors (nutritional, environmental, social) and investigate early gene-environment interactions that may perturb the normative development of various tissues and organ systems (such as the cardiovascular, neurological, immune, gastrointestinal, skeletal, endocrine, reproductive systems), increasing the risk of disease or conditions later in life and across generations. Particular focus would be placed on diseases, conditions or syndromes that have been steadily increasing in incidence and where health disparities are apparent in the United States. A focus on exposure characterization during early life would be imperative and would include vulnerable populations defined by race, ethnicity, and socioeconomic status.
3. Apply state-of-the-art sequencing technologies to investigate epigenetic and genetic mechanisms by which early life events lead to developmental reprogramming, impacting disease risk both in both children and adults (e.g., somatic or cognitive changes) long after the stressor is gone, and how increased risk is transmitted to subsequent generations through various mechanisms (i.e., germline, mitochondrial, or other changes).
4. Use birth cohorts in human subjects, to identify sex-specific developmental susceptibility windows that are specific to common diseases and conditions in early development.
5. Identify biomarkers of developmental stress for single exposures and combinations that predict susceptibility to specific diseases and conditions later in life that could also be used to target and develop preventive interventions.
6. Develop bioinformatics and statistical programs to allow the assessment and integration of developmental exposure to a variety of stresses and their importance in the development of disease outcomes.
Potential impact of Common Fund investment: This would be the first CF program specifically designed to focus on disease prevention, and it will do it in an integrated and transformative manner. Once it is clear how certain diseases or conditions originate in early development – including which stressors or combinations of stressors are responsible for the altered programming, and during which developmental stages humans are most sensitive to these effects – effective strategies can be developed both to reduce exposure to the stressors and or intervene to reduce disease incidence. Such strategies have the potential not only to reduce the overall societal burden of disease but to reduce or eliminate health disparities.
Tags: new tools, computational, prevention, intervention, developmental, genetics/genomics, biomarker, disease, environmental
Title of proposed idea: Artificial Organs: From Lab Bench to the Body (see “Artificial Organs as Tools for Translation” in Innovation Brainstorm ideas)
Major obstacle/challenge to overcome:The long-term clinical objective of this Program is to make available artificial organs for in vivo replacement or in situ regeneration of non-functional ones due to aging, tissue degeneration, birth defects, and injury. Common Fund investments could facilitate accomplishing this goal by supporting a research Program in developing powerful in vitro tissue platforms for drug screening, toxicology testing, disease modeling, and diagnostics, as well as for in vivo strategies for organ replacement, by leveraging recent breakthroughs in cellular reprogramming, bioengineering, high-throughput technologies and pharmacogenomics.
Successful building of new organs will require overcoming a number of challenges. The nature of these challenges will depend on the intended application; whether, for example, the organ will be used for the development of simple or sophisticated in vitro platforms, or if it will be used for replacing diseased organs in vivo. The utility of the iPS cells for organ building is primarily related to their pluripotent nature, because pluripotency permits virtually unlimited expansion of patient-specific genetically matched undifferentiated cells. However, obtaining fully functional and homogeneous populations of stably -differentiated non-tumorigenic cells from iPS cells is notoriously difficult, and this limitation is considered to be a significant impediment to translational applications of these cells. Therefore, additional approaches are needed for obtaining abundant cell sources that do not posses limitations of the iPS cells.
Emerging scientific opportunity ripe for Common Fund investment: The proposed Program endorses and expands the idea of “Artificial Organs as Tools for Translation” that was derived from the Innovation Brainstorm meeting. However, we believe that building the CF Program solely on fully reprogrammed iPS cells, as currently planned will significantly limit the overall success of the effort. We also argue that the results of in vitro and in vivo work should and will synergistically benefit each other. Therefore, separating them, and focusing primarily on in vitro screening technologies with only minor emphasis on in vivo organ replacement, as currently proposed, will diminish the long-term translational impact of this CF investment.
We propose to initiate a Program to create unprecedented opportunities for basic research, and translational and clinical applications by anchoring on the rapidly-developing direct lineage reprogramming technologies that are widely recognized as paradigm shift approaches for creating artificial organs for in vitro assays and for organ replacement in vivo. Direct reprogramming can overcome limitations of iPS cell technologies, because it involves only partial reversal of terminal differentiation state (as opposed to full, as with iPS cells) thus leading to formation of patient-specific lineage-committed embryonic or adult progenitors. Such cells can be expanded and are amenable to robust differentiation into functional somatic cells. Moreover, with the help of advanced bioengineering tools, it will be possible to achieve safe direct reprogramming in vivo making in situ organ regeneration into a reality. The drawback of directly reprogrammed cells is in their relatively limited expansion capacity which is similar to that of adult stem cells or embryonic progenitors, and this complicates the task of obtaining sufficient cell numbers for building human-size organs. Therefore, the intent of the proposed expansion of the “Artificial Organs as Tools of Translation” Program is to enhance the Program by taking advantage of full and direct reprogramming to benefit translational application of these breakthrough technologies.
Common Fund investment that could accelerate scientific progress in this field: Recommended CF investments in this area (in addition to those proposed in original Program) include: (i) Developing strategies for induction of full and direct reprogramming using novel gene delivery and small molecule approaches that will eliminate the need for genetic modification; (ii) Developing platforms to study epigenetic, proteomic and transcriptomic changes for improved efficiency of reprogramming; (iii) Advancing science and technology for expansion and differentiation of reprogrammed cell; and (iv) Developing strategies for direct reprogramming in vivo.
Potential impact of Common Fund investment: The specific outcomes from this Program will include novel tools and standardized protocols for cellular reprogramming, expansion, controlled differentiation and repository of functional and well- characterized multipotential cell populations for a variety of applications, including functional assays, diagnostics and organ building in vivo and in vitro.
Since many general questions still need to be answered and new technologies, tools and platforms to be developed, this field will greatly benefit from the “incubator space” of the CF mechanisms. Once the original goals of the Program are achieved, the need for the CF involvement will decrease and it will become more appropriate for the individual ICs to carry on with their own Programs to build artificial organs for applications in their respective mission areas.
NIDCR is in excellent position to lead this CF effort because of its trans-NIH and multi-agency shared interests and collaborations through Armed Forces Institute for Regenerative Medicine (AFIRM), Nano Task Force and National Nanotechnology Initiative through the Office of Science and Technology Policy, Multi-Agency Tissue Engineering Science (MATES) Interagency Working Group, and the newly established Intramural National Center for Regenerative Medicine.
Tags: regenerative medicine, reprogramming, new tools, preclinical, toxicology, epigenomics, proteomics, diagnostics, drug screening
Title of proposed idea: Targeting the Dynamic Complexome
Nominator: Innovation Brainstorm participants
Major obstacle/challenge to overcome: The spatial and temporal dynamics of protein complexes and complex-drug interactions are difficult to characterize (and predict). In part, this contributes to the well known in vitro/in vivo discrepancy between predicted and actual drug action and efficacy. Primarily, this is due to current limitations of in vivo validation processes. Experimental mapping of the dynamic complexome in normal and disease states would add significantly to overcoming this obstacle. Much more rational design and screening methods are important for developing safe and effective drugs that specifically target complexes.
Emerging scientific opportunity ripe for Common Fund investment: Recent progress in the development of tools and methods to map dynamic protein-protein interactions provide a mechanism through which disease pathogenesis can be better understood and new drugs can be designed. Specific challenges must be overcome for these possibilities to become reality.
Common Fund investment that could accelerate scientific progress in this field: Common Fund (CF) investments in the following would have a transformative impact on the identification of new drugs, the functional annotation of existing drugs, and the identification and testing of candidates for polypharmacologic approaches:
Potential impact of Common Fund investment: Investment in this area would benefit basic and applied studies. Identification of new functionally distinct complexes that define cellular pathways will increase knowledge about pathways and signaling mechanisms shared among diseases and conditions. Mapping the complexome may have as much potential for distinguishing disease states as does mapping the genome. Progress in this area will also likely yield small molecules as probes for clinical samples and tissue engineering models. Clinically relevant impact also includes the identification of new drugs that are specific, effective, and which have better side-effect profiles than most currently used therapeutics.
Tags: new tools, computational, functional analysis, drug design, predictive model, protein complex
Title of proposed idea: Single Cell Analysis
Major obstacle/challenge to overcome: Population heterogeneity among cells in a given tissue is a critical issue whose importance bridges many areas of biomedicine: cancer, infectious disease, developmental processes, organs, and immune responses. However, it is well-known that current approaches are quite limited in that they can only achieve approximate ensemble analyses of cell populations. Roadblocks to progress in this area are biological and technological: Molecular and systems level description (and quantitation) of cells, organs, and disease processes requires a greater understanding of the behaviors of individual cells and the overall composition of the population.
Emerging scientific opportunity ripe for Common Fund investment: Advances in engineering and nanotechnology provide the opportunity for transformative methods in single-cell and population-based analyses. The need for ultra-sensitive analytical methods and sophisticated computational tools calls for expertise from physicists, engineers, and computer scientists. It is possible that existing theorems on organizational behavior could be re-purposed for single-cell studies.
Common Fund investment that could accelerate scientific progress in this field: Potential Common Fund (CF) investments in this area would go beyond most of the current emphasis on microscopic and imaging techniques (although those approaches are also useful and necessary). Potential new investments could be in mapping a single cell’s epigenome, proteome, and metabolome. In addition, CF investment is needed to extend recent proof-of-principle work in single-cell genome sequencing and transcriptomics that is highly innovative, but low-throughput and far from practice. CF investments should emphasize approaches that capture living (or recently living) cells in vivo without need for overexpression or artificial constructs.
Potential impact of Common Fund investment: The ultimate motivation for more research in single-cell analysis is the potential for in vivo application to disease. Developing a robust set of tools to assess (and ultimately manipulate) single cells in situ is a key step toward achieving that goal. This achievement would have broad applicability across biomedicine: both for basic studies and for clinical use.
Tags: new tools, computational, genetics/genomics, clinical, diagnostics
Title of proposed idea: Group Effects
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
Title of proposed idea: Cross-Cutting Issues in Computation and Informatics
Major obstacle/challenge to overcome: One common thread of nearly all the topics discussed at the Innovation Brainstorm meeting is data overload. In particular, there is an urgent need for integration of data sets, approaches, as well as of inquiry that addresses multiple states of health and disease. Improved data sharing, as well as access to secondary data sets, is paramount to progress.
Emerging scientific opportunity ripe for Common Fund investment: More interdisciplinary opportunities are required to tackle the data challenges in biomedicine, and as such, all efforts to ease these interactions would be well-spent.
One example of an underused opportunity is cloud computing, which provides shared computational resources on demand via a computer network. This approach could be broadened within the biomedical realm, although some fields (e.g. protein folding) have already implemented it. Since data users submit tasks without possessing the software or hardware locally, the approach promotes cost and labor efficiencies.
Developing new tools and opportunities for multi-disciplinary interactions will help integrate genomic and phenotypic data sets as well as advance the study and understanding of the broadly based “environment.”
Common Fund investment that could accelerate scientific progress in this field: Currently, NIH-supported resources in this area are helpful but not sufficiently broad and/or powerful enough to address the growing need to integrate multiple data sets. The NIH could “democratize” this area of research by:
Potential impact of Common Fund investment: Developing and sharing broad-based computational tools and making them freely available to the scientific community has the potential to vastly increase the interoperability of data sets currently being generated in ‘omics studies. Doing so is necessary for full integration of knowledge that can apply across NIH Institutes and Centers (ICs) and disciplines.
Tags: new tools, computational, data integration, workforce
Title of proposed idea: Bringing Difficult Structures into Reach
Major obstacle/challenge to overcome: Despite ongoing Common Fund (CF) efforts to develop new technologies and a better understanding of the structural biology of membrane proteins, many other proteins of intense biological interest (e.g., large proteins, multi-subunit proteins, glycosylated proteins, complexes of proteins, conformationally mobile proteins and transient interactions of proteins) remain intractable to structural biological investigation.
Emerging scientific opportunity ripe for Common Fund investment: Various opportunities and techniques appear ripe for investment as they are “stuck” at the inflection point of scientific progress over time. These include: i) small-angle X-ray scattering in solution (which requires experimental validation); ii) single-particle X-ray analysis (which needs engineering and refinement); iii) tomography (which needs improvement in resolution); and iv) powder and fiber diffraction (which need software and education).
Common Fund investment that could accelerate scientific progress in this field: Potential CF investments include workshops to define and understand the current limits of emerging technologies, prioritize those for development, and improve access to these techniques as well as education on how to use them. Input from these workshops could inform the development of Requests for Applications (RFAs) for methods- and software development and testing.
Potential impact of Common Fund investment: Advances in protein structure determination will provide greater availability of biologically relevant protein structures and complexes across diseases and NIH Institutes and Centers (ICs). Expanding the protein structure universe will also yield new templates for drug design, as well as three-dimensional maps for understanding protein function and mapping genomic variation.
Tags: new tools, 3-D structure, drug design, non-membrane, workforce