At the Intersection of Health, Health Care and Policy Cite this article as: Gregory W. Daniel, Alexis Cazé, Morgan H. Romine, Céline Audibert, Jonathan S. Leff and Mark B. McClellan Improving Pharmaceutical Innovation By Building A More Comprehensive Database On Drug Development And Use Health Affairs, 34, no.2 (2015):319-327 doi: 10.1377/hlthaff.2014.1019

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Tracking Innovation By Gregory W. Daniel, Alexis Cazé, Morgan H. Romine, Céline Audibert, Jonathan S. Leff, and Mark B. McClellan

Improving Pharmaceutical Innovation By Building A More Comprehensive Database On Drug Development And Use New drugs and biologics have had a tremendous impact on the treatment of many diseases. However, available measures suggest that pharmaceutical innovation has remained relatively flat, despite substantial growth in research and development spending. We review recent literature on pharmaceutical innovation to identify limitations in measuring and assessing innovation, and we describe the framework and collaborative approach we are using to develop more comprehensive, publicly available metrics for innovation. Our research teams at the Brookings Institution and Deerfield Institute are collaborating with experts from multiple areas of drug development and regulatory review to identify and collect comprehensive data elements related to key development and regulatory characteristics for each new molecular entity approved over the past several decades in the United States and the European Union. Subsequent phases of our effort will add data on downstream product use and patient outcomes and will also include drugs that have failed or been abandoned in development. Such a database will enable researchers to better analyze the drivers of drug innovation, trends in the output of new medicines, and the effect of policy efforts designed to improve innovation. ABSTRACT

O

ver the past several decades new drugs and biologics have had a tremendous impact on the treatment of a wide range of diseases. Better scientific understanding of diseases and their progression, as well as advancements in drug development and regulation, have led to increasingly targeted and sometimes breakthrough treatments. Despite this progress, however, the innovation ecosystem appears to be falling short of its full potential to improve health. Major advancements seen in some therapeutic areas, such as hepatitis C, melanoma, and cystic fibrosis, are not occurring for many other diseases, such as Alzheimer’s disease and drug-resistant Gram-

negative bacterial infections, for which treatment options remain limited despite promising basic research discoveries. In addition, substantial growth in research and development (R&D) spending by the pharmaceutical industry has not had comparable effects on industry output, which has been relatively flat as assessed by the conventional measure of annual new molecular entities (NMEs) approved by the Food and Drug Administration (FDA)1 (Exhibit 1). The apparent declining productivity implied by such measures (Exhibit 2) may have contributed to the decline in real R&D spending observed in recent years (Exhibit 1). Many recent policy reform efforts have tried to address this productivity decline and improve F eb ru a ry 2 0 15

10.1377/hlthaff.2014.1019 HEALTH AFFAIRS 34, NO. 2 (2015): 319–327 ©2015 Project HOPE— The People-to-People Health Foundation, Inc.

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Gregory W. Daniel (GDaniel@ brookings.edu) is a fellow and managing director for evidence development and innovation at the Engelberg Center for Health Care Reform at Brookings, in Washington, D.C. Alexis Cazé is managing director of the Deerfield Institute, in Epalinges, Switzerland. Morgan H. Romine is a research associate at the Engelberg Center for Health Care Reform at Brookings. Céline Audibert is director of European market research at the Deerfield Institute. Jonathan S. Leff is chairman of the Deerfield Institute, and a partner with Deerfield Management, in New York City. Mark B. McClellan is a senior fellow and director of the Health Care Innovation and Value Initiative at the Engelberg Center for Health Care Reform at Brookings.

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Tracking Innovation Exhibit 1 Annual New Drug Approvals By The Food And Drug Administration (FDA) And Industry Spending On Research And Development (R&D), 1994–2013 60

No. of new drugs approved

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SOURCE Authors’ analysis of data from items listed in the exhibit notes. NOTES Numbers of new molecular entities and new biologics (“new drugs”) approved are denoted by the blue bars and relate to the left-hand y axis. Estimated R&D spending is denoted by the orange line and relates to the right-hand y axis. Annual numbers of new drugs approved by the FDA in 1994–2010 were adapted from Scannell JW, et al. Diagnosing the decline in pharmaceutical R&D efficiency (Note 9 in text). Numbers for 2011–13 were retrieved from the following sources: (1) Food and Drug Administration, Center for Drug Evaluation and Research. 2011 novel new drugs [Internet]. Silver Spring (MD): FDA; 2012 Jan [cited 2014 Dec 19]. Available from: http://www.fda.gov/downloads/Drugs/DevelopmentApproval Process/DrugInnovation/UCM293663.pdf. (2) FDA, Center for Drug Evaluation and Research. 2012 novel new drugs summary [Internet]. Silver Spring (MD): FDA; 2013 Jan [cited 2014 Dec 19]. Available from: http://www.fda.gov/downloads/Drugs/Development ApprovalProcess/DrugInnovation/UCM337830.pdf. (3) FDA, Center for Drug Evaluation and Research. Novel new drugs: 2013 summary [Internet]. Silver Spring (MD): FDA; 2014 Jan [cited 2014 Dec 19]. Available from: http://www.fda.gov/downloads/Drugs/ DevelopmentApprovalProcess/DrugInnovation/UCM381803.pdf. Annual R&D expenditure data were adapted from the following sources: (1) Scannell et al. (Note 9 in text). (2) Pharmaceutical Research and Manufacturers of America. 2014 profile: biopharmaceutical research industry [Internet]. Washington (DC): PhRMA; 2013 Apr [cited 2014 Dec 19]. Available from: http://www.phrma.org/sites/ default/files/pdf/2014_PhRMA_PROFILE.pdf. Spending was inflation adjusted to 2013 US dollars.

the innovation process. In the United States, the Food and Drug Administration Safety and Innovation Act of 2012 and the 2012 reauthorization of the Prescription Drug User Fee Act of 1992 provided new tools for regulatory review. One of

those new tools is the breakthrough therapy designation, which provides a regulatory process to enhance FDA-manufacturer communication and to encourage the use of innovative drug development approaches for the most promising new

Exhibit 2 Number Of Drugs Approved Per $1 Billion Spent On Research And Development (R&D), 1994–2013

SOURCE Authors’ analysis of data from the following sources: (1) Scannell JW, et al. Diagnosing the decline in pharmaceutical R&D efficiency (Note 9 in text). (2) Pharmaceutical Research and Manufacturers of America. 2014 profile: biopharmaceutical research industry [Internet]. Washington (DC): PhRMA; 2013 Apr [cited 2014 Dec 19]. Available from: http://www.phrma.org/sites/default/ files/pdf/2014_PhRMA_PROFILE.pdf. NOTE The number of approved new molecular entities and new biologics per billion dollars spent (inflation adjusted to 2013) was updated as established by the methodology in Scannell et al.

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Our goal is to shed light on the factors that contribute to successful product development and on the underlying drivers of innovation.

drug candidates. The reauthorized law also encouraged collaborative efforts to advance key areas of regulatory science, such as the qualification and use of biomarkers and patientreported outcome measures in drug development. Policy proposals put forward by the President’s Council of Advisors on Science and Technology in 2012 and as part of the US House of Representatives’ ongoing “21st Century Cures” initiative include still more recommendations for streamlining the process of moving drugs and biologics from bench to bedside.2,3 The European Union’s adaptive licensing pilot program is designed to enable the European Medicines Agency (EMA) to grant early marketing authorization to pharmaceutical treatments for serious conditions with high unmet need. Similarly, the recently launched second round of the Innovative Medicines Initiative, a public-private partnership between the European Union and pharmaceutical companies, is designed to stimulate innovation in areas such as the identification of new diagnostic markers, treatments for Alzheimer’s disease, and new antibiotics. To realize their full potential, these well-intentioned efforts would benefit from a deeper understanding of opportunities for improvement in the drug development process, as well as from more research on the impact of reforms intended to influence how drugs are developed and approved. But such research is hampered by the limited availability of standard, consistent, and routinely collected measures of progress in pharmaceutical innovation. Existing metrics such as new drug approvals or R&D spending are useful, but they provide limited insight into the drivers of successful innovation. Other standard metrics are aimed not at measuring innovation but at tracking regulatory activity and performance. For example, the an-

nual number of NMEs approved by the FDA and the costs and success rates at each stage of drug development—which are among the most frequently surveyed and cited metrics—are useful for highlighting the overall output and efficiency of pharmaceutical innovation. However, they do not fully characterize the innovation process and its impacts on health outcomes and health care costs. Such metrics may also miss important dimensions of innovation, including new routes of drug administration, new or expanded drug indications, and the characteristics of investigational compounds that do not ultimately achieve regulatory approval. Moreover, these measures do not provide evidence on the actual clinical impact of the drugs once they have been approved, or on the underlying features of the pharmaceutical innovation ecosystem that helped the drugs achieve that impact. Building and maintaining a database that addresses these gaps could provide a more comprehensive picture of pharmaceutical innovation. Such a database would also support more extensive evaluations of whether policies to promote innovation are having their intended effects, as well as identifying areas where more investment could have demonstrable returns. These analyses, in turn, would support more effective policies to promote innovation. In this article we highlight key gaps in measuring pharmaceutical innovation and describe a novel database now being developed that will capture data related to the characteristics of NMEs approved over the past several decades. Our goals in developing this database are to shed light on the factors that contribute to successful product development and on the underlying drivers of innovation, and to provide a collaborative resource that can be used by policy makers, researchers, industry, investors, and patient advocates to guide initiatives to enhance innovative drug development and probe important questions relating to pharmaceutical innovation.

The Current Research Landscape Many studies have assessed the drug innovation process and trends in new drug development.4–10 In an effort to better understand available data and metrics, we reviewed some of the commonly cited innovation literature from the past decade (a representative summary of research questions and metrics from this literature is found in the online Appendix).11 Among the most commonly cited metrics are the number of NMEs approved, R&D spending, development time, failure rate of drugs in clinical development, and “downstream” measures F e b r u a ry 2 0 1 5

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Tracking Innovation such as drug sales and withdrawals of drugs from the market for safety reasons. Some researchers also break these analyses down for specific disease areas. Many seek to evaluate the impact of research and development funding, regulatory policies, and the activities of consortia or public-private partnerships on innovation. These studies have contributed greatly to an understanding of how well the innovation ecosystem is performing, and, indeed, they represent the best analyses the field has to offer. However, they have a number of shortcomings that impede a more in-depth understanding of the development and approval process. The analyses generally have been one-time retrospective looks at disparate data elements across varying windows in time, reducing comparability across studies and disease areas. Many of the analyses have relied on survey techniques and data reported by industry, which might not include information from the entire ecosystem (for instance, the data might emphasize large pharmaceutical companies and exclude smaller biotech start-ups). Because data on drug innovation are not routinely aggregated and made availably publicly, these studies have required primary data to be identified and assembled, which represents a substantial burden for individual researchers seeking to analyze a specific question. Furthermore, the data underpinning these efforts are often proprietary or not easily available and are not routinely updated. This leaves the research community without a standardized, timely, and accessible data source for further studies. Finally, most of these publications are based on US information only. We have found much less research using data related to pharmaceutical innovation in other parts of the world.12 We used our review and gap analysis to determine whether the available studies could support the development of a better database on pharmaceutical innovation. We sought to use both publicly available and for-profit data sources to compile consistent innovation metrics that were commonly used. Through this effort, we have identified three major hurdles to using available sources to build out a better database: incompleteness, inconsistency, and user-interface challenges. First, some of the databases we investigated did not include all NMEs or captured only a limited time span. Second, many of the databases have overlapping data elements, such as the name of the inventor company, patent date, primary regulatory application filing date, approval date, primary indication, and other elements.Yet in many instances this information was not con322

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No single routinely updated, publicly available database exists to evaluate pharmaceutical innovation.

sistent across databases. Lastly, the format used to present the data varied for each database, making it difficult and time-consuming for users to query and merge the data. Our conclusions from our preliminary research are that no single routinely updated, publicly available database exists to evaluate pharmaceutical innovation; existing databases are generally not well suited for this purpose; and repurposing and assembling the data into a comprehensive database is achievable, although it would require significant resources to identify and validate inputs against the original source data. In 2013 Michael Lanthier and colleagues at the FDA introduced an improved methodology for measuring innovation, using a more extensive framework for analyzing NMEs that could be a foundation for a more comprehensive database.13 They used FDA data and expertise to categorize by degree of innovativeness the NMEs that were approved by the agency from 1987 to 2011. These categories included “first in class” (first in a new drug class), “advance in class” (not first in class, but granted a Priority Review designation), and “addition to class” (little therapeutic advancement compared to other existing drugs). Using the resulting data, the researchers reported new insights related to drug innovation. For example, they found that first-in-class drug approvals have been mostly steady over the past several decades and that FDA tools such as Priority Review are being used to support different kinds of innovation. In other words, a good proportion of both first-in-class and advancein-class products are making use of Priority Review. The authors concluded by calling for further development of “methods for assessing medically meaningful innovation.”13(p 1438)

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Opportunities To Improve Measurement Building on work like that of Lanthier and colleagues,13 we have begun efforts to develop a comprehensive database of elements that will enable the exploration of important research questions relating to pharmaceutical innovation. To prioritize our efforts, we have identified four categories of measures that contribute to an overall understanding of pharmaceutical innovation and its drivers. Each category has its own data requirements and corresponding research questions, and each contributes a different piece to the overall understanding of the innovation ecosystem. The four categories are summarized in Exhibit 3 and discussed in more detail below. Measuring Characteristics Of Approved Drugs Tracking successfully developed and approved NMEs has been the cornerstone of approaches to evaluating innovation and is the basis for the first phase of our efforts. Innovation measures related to approved NMEs could be made more useful through a comprehensive effort to collect, standardize, expand, and make available more underlying data for them. This effort would produce data on the characteristics of approved NMEs from publicly available sources and categorize them according to the definitions laid out in the work of Lanthier and colleagues13 (for first-in-class, advance-in-class, and addition-to-class drugs). The data collected would also move beyond simple counts of approved drugs by compiling additional data elements regarding an NME’s discovery, development, and approval. Such elements would include therapeutic area, sponsor, funding sources, clinical development times, and regulatory review times. The additional information would permit more expansive analyses of R&D productivity and the factors that influence it. Measuring The Impact Of New Drugs On Health Outcomes Additionally, we will collect data on the downstream consequences of pharmaceutical innovation. Relevant measures of a drug’s impact should track patients’ access to new drugs (which is affected by marketing, pricing, and reimbursement, among other factors), patients’ health outcomes, and the effect of new drugs on the cost of care. This research category builds on our first category, measuring characteristics of approved drugs, by integrating data and evidence on clinical use and ongoing postmarket efforts to generate evidence. Exploring the connections between new product approvals, utilization, and patient outcomes in a given therapeutic area would enable research on how public policy is supporting truly novel therapies and how the innovation

Exhibit 3 Proposed Research Categories For Drug Research And Development

Research category

Example questions

Data sets

Measuring characteristics of approved drugs

What is the impact of X policy on successfully marketed drugs? How are trends in development time lines and costs different by therapeutic area?

Measuring impact of new drugs on health outcomes

How does the postmarket/ clinical use environment change the way we view innovation?  How can we better define innovative by including more patient-centric parameters?

Measuring drivers of development success

Measuring “macro” influences on innovation

Public data on approved NMEs

Public data on approved NMEs

+

Clinical outcomes data

What are the drivers of success overall and at each stage of development?  What are the commonalities between drugs that are not approved, and how do these trends differ from those drugs that complete regulatory review?

Public data on approved NMEs

+

Data on nonapproved products

What are the broader underlying characteristics of therapeutic areas that enable innovative drug development?  How does the number or output of PPPs dedicated to a specific therapeutic area affect downstream innovation?

Data on therapeutic areawide characteristics

+

Public data on approved NMEs

Clinical outcomes data

+

Public data on approved NMEs

Data on nonapproved products

SOURCE Authors’ analysis. NOTE NME is new molecular entity.

process continues after approval—for instance, through expanded indications and additional formulations of approved drugs. A comprehensive effort to link data on approved NMEs to clinical outcomes data would require the use of standard measures of utilization and outcomes. These measures could include summary information from drug prescribing data, observational data on prescribing practices and patient populations, and relevant postapproval studies and comparative effectiveness research. This effort would require expert guidance on what data should be included and how any measures related to a drug’s impact on health outcomes should be constructed. Measuring Drivers Of Development Success Focusing solely on products that successfully navigate clinical development and regulatory review can lead to a “winner’s bias” in research on the determinants of successful drug development. To understand fully the barriers to innovation, researchers should also study those NMEs that fail or stall in development and ultimately do not receive regulatory approval. Tracking investigational products and their characteristics would help establish a more complete understanding of the factors that lead to successful development of innovative therapies. Doing so, however, poses significant methodological challenges. This is because of the proprietary nature of many of the required data elements and the much larger number of NMEs F e b r u a ry 2 0 1 5

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Tracking Innovation that fail in development, compared to those that succeed. Collecting and providing access to this information would require collaborations among manufacturers, industry groups, the FDA, and other stakeholders. It might also involve the use of trusted third parties to handle sensitive drugspecific information and provide appropriately anonymized raw data for researchers and the public. Measuring ‘Macro’ Influences On Innovation In addition, it would be important to measure how policies and other initiatives bolster innovation at the level of the therapeutic area or across the broader innovation ecosystem. For example, organizations such as precompetitive consortia dedicated to biomarker development or strong patient advocacy groups that lay the groundwork for clinical research infrastructure could help build stronger foundations for efficient drug development in particular disease areas. The importance of tracking such cross-cutting measures can be seen in the development of AIDS treatments in the 1980s and 1990s. In that case, effective patient advocacy, scientific advancements such as viral load measurement, and regulatory innovation in the form of accelerated approval all came together to generate meaningful advancement in the development of effective treatments.14 In another example, detailed patient registries maintained by groups such as the Cystic Fibrosis Foundation have supported drug development and help track the impact of new treatments on patients. Similarly, the pioneering collaboration of the advocacy group Friends of Cancer Research with industry, the FDA, the National Institutes of Health, clinical experts, and others has led to the development of the Lung MAP, a master protocol for data collection in lung cancer studies that could have a significant impact on the cost and ease of conducting clinical trials for lung cancer drugs.15,16 Tracking these types of initiatives and measuring their impact within particular therapeutic areas would provide additional perspectives on the factors that influence drug development.

Building A Drug Innovation Database Initial Focus Our research teams at the Brookings Institution and Deerfield Institute have begun collaborating with leading experts from multiple areas of drug development and regulatory review to identify and collect comprehensive data elements that are related to the first category of improved innovation measures, measuring 324

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The importance of tracking cross-cutting measures can be seen in the development of AIDS treatments in the 1980s and 1990s.

characteristics of approved drugs (Exhibit 3). The initial efforts have included collecting data on NMEs approved by the FDA and the EMA during the past twenty years. We are capturing a broad set of attributes that describe each NME and its development and regulatory characteristics, as explained in detail below. This approach builds on the work of Lanthier and colleagues13 to provide an enriched source for analyzing approvals and will enable a wide range of analyses that would currently be too difficult or resource intensive to conduct. As the first demonstration of the utility of this approach, we will use these data for initial analyses that examine the time it takes to translate a successful new therapy from bench to bedside. For each NME in the database, specific data elements that will be needed to conduct this analysis include the discovery date for new compounds, date of initiation of clinical trials, and dates of approval in the United States and European Union. These elements will enable insights into research questions such as the following: How has the length of clinical development changed over time? How does it vary by therapeutic area? How does it vary across first-inclass, advance-in-class, and addition-to-class drugs? Does it vary for orphan versus nonorphan drugs? And does it vary for drugs granted full approval versus those that have accelerated approval? Specific Data Elements Better tracking of the development characteristics of approved NMEs is achievable in the near term because data on approved NMEs are largely publicly available and because there are existing methodologies for developing these data elements on which we can build. Through research and stakeholder outreach, we have identified a preliminary set of data elements to be targeted for initial inclusion. These elements fall into the seven categories of information shown in Exhibit 4. The elements will come from multiple sources,

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including public records of the FDA and EMA, government-maintained patent databases in the United States and European Union, and various commercially available databases. As a result, discrepancies may arise. We expect to address any such discrepancies in source data through additional research and expert input, and by ensuring that any resulting judgments made about discrepancies are transparent and reproducible. Information on approved NMEs can be further broken down into objective and subjective data elements. For objective elements, such as the date of submission of an Investigational New Drug Application and which regulatory pathway a drug follows (such as full approval versus accelerated approval), it is relatively straightforward to ensure data accuracy and consistency. Subjective data elements, however, raise methodological issues that will need to be addressed systematically. Examples of subjective data elements are items such as the innovation category, which was assessed by Lanthier and colleagues13 by accessing FDA expertise, as well as items in which the subjectivity may initially be less apparent, such as the initial patent approval date. Some molecules may have been researched for years before being developed commercially. Because such efforts may have been proprietary, tracking down these data in a consistent manner is challenging. Our review of data available from for-profit third-party sources has revealed many inconsistencies related to the question of which patent should be considered the initial one for a given product. When these types of issues arise, we intend to follow an approach similar to that of Lanthier and colleagues,13 using a standard process for gathering input from experts in relevant fields to establish consistency in the data. By clearly describing our methods for defining subjective elements and by providing data (for example, on relevant patents) that could be used by others to consider alternative definitions, we expect to be able to support more robust research on questions related to approved NMEs than has been possible in the past. Feasible Data Sources The data elements targeted for this first phase of database development are available from multiple public sources. For example, elements related to the initial discovery and development of NMEs can be found in the academic literature and in patent filings. A wealth of information on clinical trials is available through ClinicalTrials.gov and third-party vendors such as Citeline17 and BioPharm Solutions18, among others. Characteristics of an NME’s regulatory review process can be compiled from publicly available

Exhibit 4 Initial Data Elements For The Proposed First Phase Of A Database On Approved New Molecular Entities Category of information

Data elements

Background

Unique drug identification number Trade name Active ingredient Drug class Therapeutic area Innovation categorya Sponsor company size

Patent and early-stage development

Inventor name Country of origin Initial patent approval date Patent sponsor Country of origin for mechanism of action Companies that played a role in development Funding sources

Clinical trial

Date first used in humans Number of healthy trial volunteers Number of enrolled patients Number of trials sites in the US, EU, Japan, and rest of world Number of centers enrolling patients

Regulatory review

Company filing in the US or EU Primary filing day Primary FDA or EMA approval day FDA review tools used (such as Priority Review) EMA priority review status Primary indication in US or EU Additional indications in US or EU

Academic publication

Number of articles Average impact factor First appearance in literature Type of uptake curve Initial price Current price Peak year for sales Blockbuster statusb Date of market entry Pricing Uptake Development characteristics

Price and uptake

Competitors and generic products

SOURCE Authors’ analysis. NOTES EMA is European Medicines Agency. EU is European Union. FDA is Food and Drug Administration. aSee Lanthier et al., An improved approach to measuring drug innovation finds steady rates of first-in-class pharmaceuticals, 1987–2011 (Note 13 in text). bA drug is commonly considered to reach blockbuster status once yearly drug sales reach US$1 billion.

regulatory data, either through reports and formal decision letters authored by the FDA or the EMA or potentially through collaborations with the agencies to generate this information. As we move to our second category, measuring the impact of new drugs on health outcomes, databases maintained by for-profit, third-party companies may be valuable sources for postapproval information on pricing, uptake, and outcomes. These initial activities are under way and will be completed through collaboration with data partners, dialogue with key experts and stakeF e b r u a ry 2 0 1 5

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Tracking Innovation holders, and public forums on key research questions and the most appropriate methodologies to support this research. This foundational phase, including data collection and maintenance, is currently supported by resources from Brookings and Deerfield.

Beyond Approved New Molecular Entities Assembling the data necessary to extend our framework beyond approved NMEs is also intended to be an inclusive and collaborative process, building on the initial efforts and collaborations in our foundational work on approved NMEs. Engaging Regulators Any effort to collect data on NMEs that were not approved will best be enabled by collaboration with researchers at the FDA, the EMA, and potentially other international regulatory bodies, with involvement from pharmaceutical developers and other stakeholders. In addition to working with regulatory agencies to collect nonproprietary retrospective data, researchers could significantly enhance the breadth and comprehensiveness of data collection efforts by a prospective data collection mechanism for tracking drugs from the Investigational New Drug Application phase forward. Creating an agreed-upon set of measures that could be collected prospectively would present an opportunity for regulatory agencies, industry, academics, and policy makers to gain insights over time into the drivers of innovation beyond regulatory process measures. This would most likely require setting up a neutral third-party advisory group to help establish and define the measures or characteristics that would be useful to track. Further, a neutral third-party would also be needed to establish a trusted site—which could be maintained by a government agency, an academic institution, a not-for-profit organization, or some combination thereof—to house the data and release them to the public in a manner that respects commercial confidentiality. The collection of data on innovation could be a valuable complement to the agency performance metrics that the FDA is already required to produce as part of prescription drug user fee goals in the United States. Indeed, data collection could make use of the infrastructure that is already in place to collect and report the FDA’s performance metrics. Academic Partnerships Many academic groups have evaluated important aspects of the drug innovation process, and their continued input and ongoing research will be critical to ensuring that our proposed innovation database 326

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is a complete, accurate, and useful tool for the broader research community. Direct collaboration with academic researchers could lead to more comprehensive analyses by identifying the additional data elements that would be most useful for enhancing their research. Industry Outreach And Collaborations Participation by drug developers will also be important for addressing gaps in data, particularly the provision of nonproprietary data (summary or otherwise) on drug candidates still in development or abandoned during the development process. Organizations such as the Biotechnology Industry Organization, Pharmaceutical Research and Manufacturers of America, TransCelerate BioPharma, and other industry groups could provide valuable platforms for collecting, centralizing, and standardizing such data so that they can be queried while protecting confidential and proprietary aspects of the underlying data sets. Quality, Outcomes, And Postmarket Data Collection Efforts to link NMEs to clinical outcomes could be coordinated with the work of groups focused on health care quality and patient benefit. Many organizations—including publicly funded ones such as the Patient-Centered Outcomes Research Institute, public-private collaborations such as the FDA’s Sentinel Initiative, and private organizations such as IMS Health—are making major and increasingly sophisticated efforts to track and quantify the utilization, outcomes, and value associated with pharmaceutical innovation. Payer groups are reinforcing these efforts through reforms in health care financing that promote more evidence on outcomes and payments based on results. Patients are helping establish standards and practices for integrating patient-reported outcomes into drug development and regulatory review. Incorporating insights and results from such initiatives will be a critical contribution to the further development of the data and measures relevant to evaluations of pharmaceutical innovation.

Conclusion As policy makers, industry, patient groups, and academic researchers increasingly focus on how to improve the productivity of the pharmaceutical R&D ecosystem, improved tools and methods are needed to better understand the existing drivers of innovation and to evaluate the impact of policies. Creating a comprehensive database of NMEs approved in the United States and European Union during the past twenty years, along with their development characteristics, can serve as a valuable first step toward improv-

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ing how innovation is tracked and evaluated. It is feasible now to build such a data infrastructure by combining and validating existing data from a variety of sources, and developing best practices for the ascertainment of subjective data elements. To be successful, our effort will require collaboration with stakeholders who influence pharmaceutical innovation. Our initial work provides a foundation for integrating other critical information: better measures of the downstream clinical impact of innovative new

medicines, analyses of the characteristics of drugs that fail or are abandoned in development, and evaluations of opportunities to enhance drug development at the macrotherapeutic area level. The result will be a publicly available platform for improved analyses of pharmaceutical innovation, better-informed public policy for innovation, and ultimately a more efficient R&D ecosystem that delivers better results to patients. ▪

NOTES 1 DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ. 2003;22(2):151–85. 2 President’s Council of Advisors on Science and Technology. Report to the president on propelling innovation in drug discovery, development, and evaluation [Internet]. Washington (DC): Executive Office of the President; 2012 Sep [cited 2014 Dec 19]. Available from: http:// www.whitehouse.gov/sites/default/ files/microsites/ostp/pcast-fdafinal.pdf 3 Upton F. Curing disease with a little help from our friends. US News and World Report [serial on the Internet]. 2014 Mar 5 [cited 2014 Dec 19]. Available from: http://www.usnews .com/opinion/articles/2014/03/05/ fda-drug-approval-must-keep-upwith-modern-technology 4 Berndt ER, Gottschalk AH, Philipson TJ, Strobeck MW. Industry funding of the FDA: effects of PDUFA on approval times and withdrawal rates. Nat Rev Drug Discov. 2005;4(7): 545–54. 5 Munos B. Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009;8(12):959–68. 6 Kaitin KI, DiMasi JA. Pharmaceuti-

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Improving pharmaceutical innovation by building a more comprehensive database on drug development and use.

New drugs and biologics have had a tremendous impact on the treatment of many diseases. However, available measures suggest that pharmaceutical innova...
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