of Program Coordination, Planning, and Strategic Initiatives
Title of proposed idea: Venture Fund for Research and Development of New Medications to Treat Chronic Pain (see “NIH Award Strategies” in Innovation Brainstorm ideas)
Nominator: NIH Institutes/Centers
Major obstacle/challenge to overcome: Chronic pain, which affects 116 million Americans and is a significant public health burden, is not adequately managed by current therapies. Although opiates are the most commonly prescribed medications to treat chronic pain conditions (e.g. cancer pain), their use pose important clinical risks such as abuse liability, diversion, and overdose. Other types of chronic pain (e.g., neuropathic pain caused by diabetes) are not well managed by either opiates or other approved agents (e.g., antidepressants). Currently, a significant amount of dollars is being invested in testing medications to treat chronic pain but the results of the studies have not yielded any significant progress in the treatment of this condition. There is an urgent need to conduct research that helps to understand the neurobiological mechanisms of chronic pain, which in turn will help to identify new targets and thus new compounds to treat this condition.
Unfortunately, it has been challenging to develop collaborations and much more to share resources among industry, academia, and government investigators to advance the study of chronic pain. A concerted and synergistic approach among those three groups will greatly advance the understanding and management of chronic pain. It is expected that the development of a venture fund for research and development will facilitate the collaboration among industry, academia, and government which will result in the discovery of new targets and the development of new medications to treat this condition.
The purpose of this program is to support eligible institutions that enter into a joint venture or collaboration with other entities which concomitantly provides support in the form of funds or resources to conduct research to advance the development of medications to treat chronic pain.
Research may focus on the discovery of new potential therapeutic targets, new molecules with action on those targets, as well as Phase I safety/tolerability studies, single or multisite Phase II or III studies, or translational projects.
Emerging scientific opportunity ripe for Common Fund investment: Currently, there are multiple individual efforts from industry, academia and government to advance the knowledge of the mechanisms of pain as well as the discovery and development of new pharmacotherapies; however, most of those efforts are not coming to fruition because of the lack of a coordinated and synergistic approach. This initiative is very timely because it aims at channeling all those efforts and making them more synergistic in achieving an ultimate goal of having safer and more effective medications to treat chronic pain
Common Fund investment that could accelerate scientific progress in this field:
It is expected that the identification and validation of novel targets associated with chronic pain can lead to novel and effective therapies. A pilot phase is proposed that if successful would go on to a therapeutics development phase to be done in collaboration with private sector partners.
Identification and validation of new therapeutic targets
Therapeutics Development Phase:
Identification of bioactive compounds for the new targets identified during the pilot phase
Early stage clinical trials
Potential impact of Common Fund investment:
The ability to effectively treat chronic pain conditions will impact more than 116 million Americans. In addition, identifying and validating chronic pain targets – may also lead to diagnostic tests that may prevent or delay the onset of chronic pain conditions.
Tags: therapeutics, pain, neurobiology, mechanisms, collaboration, industry, preclinical, clinical
Title of proposed idea: Regulatory Science Initiative
Major obstacle/challenge to overcome: The rapid pace of scientific discovery coupled with development of new technologies presents a challenge to researchers, clinical investigators, and regulators as they work to translate basic scientific advances into approved medical products. Basic and preclinical research has been performed in large part independent of regulatory issues. In addition, it is clear that novel technologies and approaches to medical research are outpacing the ability of our regulatory system to incorporate them into current review practices and guidelines. To overcome these obstacles, NIH should support strategic initiatives that are essential to the translation of NIH funded discoveries into diagnostic and therapeutics.
Emerging scientific opportunity ripe for Common Fund investment: An investment in Regulatory Science will benefit all stakeholders by helping to advance and incorporate cutting-edge science into regulatory decision making and helping to develop improved tools, standards and approaches for assessing the safety, efficacy, quality and performance of medical products. Major advances in genomics and genomics-based medicine are also creating potential scenarios in the clinical setting that are relatively new to the FDA regulatory process. Moreover, the unprecedented partnership between the NIH and FDA through the Joint Leadership Council provides an extraordinary opportunity to coordinate therapy development efforts, including regulatory decision-making guidelines, between the two agencies.
Common Fund investment that could accelerate scientific progress in this field: A number of scientific opportunities are ripe for investment in the area of Regulatory science and across the therapeutics development pipeline. For instance:
Potential impact of Common Fund investment: Pre-clinical and clinical investigators, and other researchers who are engaged in the diagnostics and therapeutics development industries will benefit from having a more rapid integration of evidence-based knowledge into a regulatory framework, thereby quickening the pace at which basic science advances can move into the therapy development realm. For instance, in the area of stem-cell technologies, the NIH and FDA are working together to identify and define markers and characteristics of “stemness”, thus providing standards that the entire field can use for purposes of comparing studies and preparing for regulatory considerations. The possibility of individualized, autologous utility of stem cell-derived therapeutics, organs, tissues, and other biomedical products are fast becoming a reality. Other emerging areas in regulatory science will advance, such as nanomedicine, personalized medicine, efficient and expeditious clinical trial designs, predictive toxicology, and biomimetic models that are able to simulate human conditions and better predict safety and efficacy. The NIH-FDA joint efforts in these areas would help to pave a clearer and more transparent scientific and regulatory path for the scientific community that will impact therapeutics product development and clinical practice.
Tags: new tools, computational, regulatory, predictive model, toxicology, diagnostics, clinical, translational
Title of proposed idea: Gene-Based Therapeutics: Manipulating the Output of the Genome to Treat Disease
Major obstacle/challenge to overcome: Gene-based therapeutics are tools to manipulate the output of the genome to treat disease. The most well-known gene-based therapeutic is gene therapy, which is most commonly done using viral vectors, although other vehicles (e.g. nanoparticles), can be used as well. Other gene-based therapies include small interfering RNA (siRNA) and oligonucleotide therapeutics, and zinc-finger nucleases and transposons to modify the genome directly.
For many gene-based therapies, development and proof of principle preclinical studies are within the budget of a typical RO1 grant award. The major obstacle is moving from preclinical research into clinical trials. Major hurdles at the preclinical level include limitations on the size and sequence of nucleic acids used in gene-based therapeutics, as well as tissue and cell-type specific targeting. Moving to the clinic, hurdles include the practical reality of scaling up production, funding for GMP drug manufacture and toxicology testing, and funding for clinical trials themselves.
What is needed to overcome this obstacle is a program dedicated to making gene-based therapies a clinical reality. Our proposal is for the Common Fund to support such mechanism, which would facilitate the translation of current gene-based therapies into clinical trials.
Emerging scientific opportunity ripe for Common Fund investment: Gene-based therapies are clearly ripe for investment by the Common Fund. It has now been established that viral vector based gene therapy is effective in humans. In addition, novel gene targeted therapies are being developed and validated at an accelerating pace. In 2011 alone, we have seen the first evidence that zinc-finger nucleases can be effective in a mouse model of hemophilia , and the use of exosomes to deliver therapeutic siRNA across the blood-brain barrier in mice .
NIH ICs have provided the majority of funding for the discovery and preclinical developments of multiple gene-based therapies, and will do so in the future. However, they not positioned to support translation to the clinic at the level that is necessary.
Common Fund investment that could accelerate scientific progress in this field: We envision a program that combines aspects of RAID (Rapid Access to Investigational Drugs) and TRND (Therapeutics for Rare and Neglected Diseases), but is focused exclusively on gene-based therapeutics. Projects will be chosen for support by streamlined competitive process, and funding provided in a step-wise manner dependent upon continued progress and meeting project targets (similar to RID). For some viral-vector based therapies, the new program could support investigators to utilize the NHLBI gene therapy resource program. For other types of therapeutics, the fund could provide support large scale production of GMP grade nucleic acids, or zinc-finger nucleases, as well as CROs for animal toxicology testing. By funding such a large effort, significant cost savings would be expected based upon economies of scale.
Another aspect of the program, modeled after the TRND program, would be to carry out Phase 0 and Phase 1 clinical studies to de-risk gene–based therapies, and thereby encourage adoption by industry. It is possible that industry could be involved with this program at an earlier stage in a public-private partnership.
It should be emphasized that TRND does not work with biologics or gene therapy, so the new program would complement TRND, rather than duplicate effort. While gene therapy and biologics are a part of RAID, given the rate of development of new technologies in this area, and the potential clinical impact, we believe that a much larger program, exclusively focused on gene-based therapeutics, is needed.
Potential impact of Common Fund investment: If the proposed Common Fund program were to achieve its objectives, the impact would be that one or more gene-based therapies would become established as a treatment option for patients with genetic disease. As a benchmark, gene-based therapy would become as common as bone marrow transplantation is currently at major academic medical centers. Such an outcome could transform the clinical outlook and lives of patients with genetic disease. This would be especially important for rare diseases where in most cases no other treatment options exist.
Importantly, we anticipate that this program would dramatically impact basic science as well. A commitment to gene-based therapeutics by the Common Fund, would certainly stimulate even more preclinical studies in this field, which would in term feed into the new program, and also provide new tools for basic science. As an example, if the program achieved its objectives, tools could be available that would make manipulating the genome of a mouse, in a specific cell population, as routine as transformation of bacteria is today. If this were to become reality, it would dramatically change the way biomedical science is done. With support from the Common Fund, these impacts are feasible within the 10 year time frame specified by the Common Fund criteria.
Simonelli F, Maguire AM, Testa F, et al. Gene therapy for Leber's congenital amaurosis is safe and effective through 1.5 years after vector administration. Mol Ther. Mar 2010;18(3):643-650.
Aiuti A, Roncarolo MG. Ten years of gene therapy for primary immune deficiencies. Hematology Am Soc Hematol Educ Program. 2009:682-689.
Li H, Haurigot V, Doyon Y, et al. In vivo genome editing restores haemostasis in a mouse model of haemophilia. Nature. Jul 14 2011;475(7355):217-221.
Alvarez-Erviti L, Seow Y, Yin H, Betts C, Lakhal S, Wood MJ. Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nat Biotechnol. Apr 2011;29(4):341-345.
Tags: gene therapy, therapeutics, preclinical, clinical, industry, viral vector, biologics
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 Chronic Pain Conditions: A Transformative Classification for Stimulating Research, Improving Diagnosis, and Personalizing Treatment
Major obstacle/challenge to overcome: Chronic pain conditions afflict as many as one-third of the US population and incur $560-635 billion per year in incremental healthcare costs and lost productivity (IOM Report June 29, 2011). The long term clinical goal in alleviating chronic pain is to develop targeted therapies and identify patients responsive to these therapies, both of which are supported by etiological- and mechanism-based case definitions and diagnostic criteria of disease. A major challenge in the field is the lack of a mechanism-based case definition and diagnostic criteria for multiple chronic pain conditions. Common Fund investments could facilitate the development of a new objective, biopsychosocial classification system for chronic pain disorders to overcome this major obstacle. This new system will accelerate research by standardizing research diagnoses used across laboratories, enhance clinical diagnoses by developing more objective, mechanism-based measures of disease, and identify subjects responsive to new therapies by developing novel biomarkers of disease and clinical outcomes.
Emerging scientific opportunity ripe for Common Fund investment: We propose a research program to develop a new, comprehensive, mechanism-based, biopsychosocial classification of chronic pain conditions. Three opportunities are ready for Common fund investment. This proposal endorses the ideas and sharpens the focus of “Molecular Classification of Disease”, a topic that emerged from the Innovation Brainstorm meeting, and takes on sophisticated data management and analysis elements of the topics on “Beyond GWAS” and “Cross-Cutting Issues in Computation and Informatics”.
Common Fund investment that could accelerate scientific progress in this field: This program would create a centralized data bank/repository containing information from a large chronic pain cohort to include study subjects with Temporomandibular Joint Disorders, Fibromyalgia, Chronic Fatigue Syndrome, Vulvodynia, Endometriosis, Irritable Bowel Syndrome, Interstitial Cystitis, Headache, Low Back Pain, Arthritis, etc., recruited and identified using today’s best diagnostic criteria. Many of these subjects will have multiple, comorbid chronic pain conditions. This cohort would be genotyped as well as phenotyped extensively using molecular, imaging and psychosocial methodologies. All data would be agnostically analyzed via pathway analyses and new algorithms for lumping and splitting in order to subtype and re-classify these chronic pain patients. Results emerging from the Common Fund incubator space would lead to a breakdown in the current “walls” separating these disorders (and researchers) and a transformation of diagnostic criteria based on a completely new classification of chronic pain conditions. After an intense 5 year effort, the data bank/repository and analytical tool set would become self sustaining with support from Pharma, the genotyping industry, and the NIH Pain Consortium.
Potential impact of Common Fund investment: The outcome of this project will be a completely new way of discovery and management of chronic pain conditions: researchers currently housed in different laboratories collaborating in multidisciplinary teams to study pain, rapid discovery of therapeutic targets, development of novel analgesic therapies based on common mechanisms of disease, introduction of individualized medical treatments and identification of those likely to respond to therapy. Ultimately, results from this project will lead to an overall reduction in the burden of chronic pain, currently $560-$635 billion/year in the US in incremental healthcare costs and lost productivity.
Chronic pain should be thought of as a disease unto itself like other chronic conditions such as diabetes and heart disease, and not merely a symptom of disease. Research approaches to and management of chronic pain conditions must consider that, like other chronic conditions, disease progression and complexity, early identification and intervention, and effective therapies, all influence patient burden and economic costs of disease. A transformative classification of chronic pain conditions will ultimately reduce long-term morbidity and decrease the economic impact of these wide-spread conditions.
Tags: pain, diagnostics, therapeutics, clinical, classification system, biomarker, molecular mechanism
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: Biomarkers for chronic pain using functional brain connectivity
Major obstacle/challenge to overcome: Chronic pain is a debilitating condition affecting at least 116 million American adults, resulting in significantly reduced quality of life and an estimated annual cost of $560 – 635 billion 1. Unfortunately, its assessment is based solely on subjective self-report, using limited scales or measures, which are unsuitable for elucidating the different types and causes of pain (i.e., pain endophenotypes) and for rigorously evaluating the impact of targeted interventions. Self-report measures also hamper progress in the monitoring required to precisely dose a medication and then evaluate its comparative effectiveness among different individuals. Also, importantly, the field of pain management has been long challenged by the twin fears of undertreating pain in those who are suffering vs. triggering or facilitating a drug problem. Because of all these obstacles, there is a pressing need for a standardized, brief and simple measurement that can translate, or at least reproducibly correlate, subjective pain experience into objective and quantitative readings for both clinical and research purposes.
Emerging scientific opportunity ripe for Common Fund investment: In functional neuroimaging, there has been a recent explosion of findings on functional connectivity (FC) between brain regions, especially in the resting-state (RSFC), which is defined as the signal coherence between discrete brain regions in the absence of a cognitive task. RSFC has uncovered discrete functional networks, where the strength or activity coherence can be quantified. Based on recent reports of differences in intrinsic brain network connectivity between patients with chronic pain and controls, it has been suggested that RSFC could be a suitable platform to develop objective biomarkers of pain. Moreover, recent expansion of neuroimage data-sharing, especially of RSFC data in the 1000 Functional Connectomes Project, has demonstrated that data from different sources can be pooled to define subtypes of populations stratified by age, gender, medical conditions, and other variables, to enhance statistical power for discovery. If this level of between-labs consistency turns out to also apply to pain related measurements, RSFC could revolutionize the field of pain research and management.
Common Fund investment that could accelerate scientific progress in this field: Chronic pain is a clinical condition characteristic of a wide range of physical syndromes that collectively span the programmatic purview of many different ICs. A request for applications (RFA) on this topic to fund five or six research project grants would enable multi-disciplinary teams (comprised of pain clinicians, functional neuroimagers, and computational/network neuroscientists) to 1) develop techniques for image time-series analysis to identify brain RSFC signatures of different types of chronic pain, and 2) test the value of said signatures in a clinical context. For example, R21/R33 phased-innovation awards would enable initial collaborations to assess basic cross-sectional differences between controls and patients with different syndromes of chronic pain, and to develop and optimize new analytical tools for better identification of sensitive and specific RSFC biomarkers of pain. The common fund program concept would also enable comparative effectiveness research, data harmonization across funded projects, foster a consortium on pain RSFC biomarkers, and inform prospective evidence-based, personalized care of pain.
Potential impact of Common Fund investment: Advances in image acquisition and data analytic approaches could yield a level of objectivity, sensitivity, and specificity that would be unprecedented for chronic pain. In theory, a single resting-state functional MRI scan could serve as a diagnostic procedure akin to a head MRI for brain cancer or other neurological diagnoses. Data derived from that scan may not only provide an objective and reliable marker, but also help identify optimum therapeutic approaches, lowering the costs and loss of productivity associated with ineffective pain treatments. Validation of pain biomarkers is critical in the development of pain medications and for the adequate use of prescription analgesics matched to the needs of individuals. When the proposed program achieves its objectives, the collaborative effort among funded projects will complete the characterization and validation phase of functional brain connectivity as biomarkers for chronic pain, helping to bring evidence-based, personalized management of pain closer to reality.
Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education
and Research. Committee on Advancing Pain Research, Care, and Education Board on Health Sciences Policy. Institute of medicine of the national Academies (2011).
Tags: neuroimaging, biomarker, pain, clinical
Title of proposed idea: Single Cell Analysis
Nominator: Innovation Brainstorm participants
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: Molecular Classification of Disease
Major obstacle/challenge to overcome: Currently, “clinical syndromes” are often used to classify disease. The problem with this approach is that a given patient syndrome may contain significant heterogeneity with regard to molecular mechanisms of pathogenesis. As a result, the ability to identify pathogenic mechanisms in population studies is limited, as is the ability to quickly and efficiently identify who will benefit from therapeutic interventions. Thus, new approaches are needed for classifying patients and disease states that are more tied to the molecular basis of disease. Intermediate markers or “endophenotypes” may be helpful in this regard.
Another obstacle to translation is a general lack of willingness to challenge dogma, which can perpetuate stale thinking and practice.
Emerging scientific opportunity ripe for Common Fund investment: Progress in this area promises to fill gaps between molecular characterization and patient disease states, as well as to identify heterogeneity in classical clinical syndrome classifications. Recent advances in technologies that allow comprehensive profiling of patients at the molecular level and association of these profiles with clinical data provide an opportunity to completely redefine the way we think about and understand disease. However, these capabilities need to be developed further and expanded for regular use in the clinic.
Common Fund investment that could accelerate scientific progress in this field: Innovation is needed in the way in which we classify patients. Examples include:
The NIH could establish a well-characterized, central sample database to encourage data sharing and integration. New approaches to finding “lenses” to view complex biomedical problems could include funding coherent, high risk programs, as well as considering the relevance and ability of existing networks to pursue this work [e.g., Clinical and Translational Science Awards (CTSAs)].
Potential impact of Common Fund investment: Molecular characterization of disease has obvious benefit across the board for diagnosis and treatment of all diseases. In addition, progress in this area would catalyze the transition from one-size-fits-all medicine to personalized medicine. Clinical trials could be done more quickly and efficiently, and the resources harbored by population studies may be better utilized.
Finally, encouraging a mandate to challenge dogma would likely introduce broader thinking that will undoubtedly open new avenues for exploration.
Tags: computational, genetics/genomics, database, disease phenotype, clinical, diagnostics
Title of proposed idea: Beyond GWAS (genome wide association studies)
Major obstacle/challenge to overcome: Although GWAS (genome wide association studies) have uncovered many genetic loci for a range of conditions and diseases, a major challenge is translating this knowledge into functional insights. One key roadblock is the inability to capture precisely various and diverse environmental measurements. Incomplete, nonstandardized, and shallow collection of phenotype data contributes to the difficulty of using GWAS data to define mechanisms and/or suggest potential interventions. Insufficient sample sizes prohibit the clarification of the role and relevance of complex traits in health and disease. In some cases, valuable opportunities may be missed, as in harnessing genotyping data from randomized clinical trials that have rich phenotypic data. For the massive amounts of data that already exist, practical and effective strategies for integration lag behind. Possible remedies include new algorithms for performing higher-order ‘omics studies, a repository of rare knockouts, and more complete sharing of data and biospecimens.
Fig. 1. Scientific progress over time
Emerging scientific opportunity ripe for Common Fund investment: Further progress in GWAS requires both persistence and innovation. While GWAS execution is routine and fairly well-established (in the “D” portion of the graph below), others are in a period of rapid growth [in the “C” region of the curve: single trait analysis, expression quantitative trait loci (eQTLs)], and still others require a substantial push to reach their potential (in the “A” and “B” areas below: functional annotation of genetic variants, annotation of a reference genome (ENCODE, the ENCyclopedia Of DNA Elements), whole-genome analyses in unrelateds/families, large-scale phenotyping, and clinical translation). This last group is likely to be the most ripe for Common Fund (CF) investment.
Common Fund investment that could accelerate scientific progress in this field: Three proposed projects (each independent but complementary and potentially synergistic) could overcome some of the current roadblocks in this area.
Potential impact of Common Fund investment: Moving GWAS beyond its current capability offers faster movement from association to function, which will likely accelerate discovery for multiple traits. Clinical relevance of most GWAS to date is lacking: The proposed projects aim to lead to better clinical decision support, new diagnostics and therapeutics, improved coordination with industry, as well as the realization of meaningful use criteria of the HITECH act (The Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009, was signed into law on February 17, 2009, to promote the adoption and meaningful use of health information technology).
Tags: genetics/genomics, functional analysis, clinical, cohort, environmental