Extending the Spectrum of Precompetitive Collaboration in Oncology Research Workshop Summary (2010) / Chapter Skim
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Workshop Summary
Workshop Summary
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These challenges create opportunities for improved collaboration between industry, academia, government, and philanthropic organizations at each stage in new drug development, marketing, and implementation. Perhaps the most appropriate initial step in addressing the need for collaboration is to consider more precompetitive relationships that allow sharing of scientific information to foster drug development.
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held a workshop titled Extending the Spectrum of Precompetitive Collaboration in Oncology Research on February 9 and 10, 2010, in Washington, DC. At the workshop, speakers addressed: • Current driving forces for precompetitive collaborations; • Benefits of such collaborations; • Challenges to collaborating; • Types of precompetitive collaborations and what can be shared; • Precompetitive collaboration examples; • Lessons learned and best practices formulated from these examples of collaboration; and • Next steps that could facilitate more precompetitive collaborations in oncology drug development.
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Many factors have made the standard model for developing drugs inadequate, they pointed out, including the growing complexity of research and far-ranging and uneven distribution of knowledge, patient variability that contributes to the uncertainty and low success rate, increasing emphasis on comparative effectiveness and evidence-based medicine, the increasingly long and expensive time lines of drug development, and declining research and development budgets. Increasing Complexity and Data Many speakers noted the growing complexity of basic and clinical research in oncology, much of which hinges on deciphering the intricate networks of molecular pathways involved in the formation and progression of various cancers, as well as predicting patients' likely responses to treatments aimed at the targets within those networks.
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This roadmap laid out the goals of the genetics, genomics, and proteomics into new drug development requires data repositories and much more sophisticated information technology (IT) to analyze data.
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Bryn Williams-Jones, associate research fellow and head of eBiology at Pfizer, concurred, noting that "in spite of knowing a lot more and having a lot more data to go on, we are actually getting worse at finding out anything, and are not much more productive." He called for more data analysis standards so that more valid conclusions can be drawn from the data acquired. Developing such standards will require a collaborative effort.
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own costly information technology infrastructures, but rather join a col laborative endeavor that provides that infrastructure, ideally in the virtual public domain. "We are a drug discovery industry, and none of us can afford to reinvent and source an entirely proprietary software system that is going to be able to help us deal with this.
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. Reprinted, with permission, from Eric Schadt and Stephen Friend.
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Drug development models that depend on simple pathway approaches are no longer appropriate, he pointed out, because studies indicate that when one pathway that fuels cancer growth is blocked, a redundant pathway will enable the cancer to thrive. He showed one slide that illustrated the complex transcriptional networks involved in the growth of brain tumors (see Figure 3)
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We have reached the limits and we have to work together because you can't do it alone." Lakhani noted that the widely distributed nature of knowledge is also evident within Joy's law,2 which states: "No matter who you are, most of the smartest people work for someone else." To illustrate the distributed nature of knowledge, he gave the example of Robert Langer, an expert in tissue engineering at Massachusetts Institute of Technology (MIT)
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Those clinical trials that do try to account for such variability with standard trial designs often need thousands of volunteers, which make the clinical trials costly, risky, and lengthy, noted Laura Esserman, director of the Carol Franc Buck Breast Care Center at UCSF. She pointed out that breast cancer has several different subsets of the disease that respond differently to the same breast cancer drugs.
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Authors highlighted in green have more than half of their articles coauthored by Robert Langer, an expert in tissue engineering. Central in the domain of his field, Langer collaborated with 40 percent of the most prolific authors; however, his publications represent only a fraction of the articles published during this 2-year time period.
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Thinh Nguyen, counsel for Science Commons, added that there is also an increasing reliance on a few blockbuster drugs, rather than diverse sources of revenue by drug companies. Precompetitive collaborations that combine datasets could provide enough data to validate both disease models and biomarkers, thereby reducing the uncertainty in current drug development, and improving the success rate and the willingness of drug companies to undertake the development of innovative therapies, according to McClellan.
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In addition to making drug development quicker and less redundant, risky, and expensive, precompetitive collaborations can foster a productive synergy that promotes thinking outside the box, brings in researchers with diverse expertise, and sparks innovation. By combining datasets and having more reviewers of the data analyses, research collaborations also have more statistical power and less bias, which makes their conclusions more reliable and amenable to regulation.
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, because Lilly was able to reach outside its core competencies. Munos noted that this is in sharp contrast to the tightly scripted target-based drug discovery process that has emerged in the pharmaceutical industry over the past couple of decades: "Sciences that lie outside the field of what can typically be encountered in pharmaceutical companies have and have had significant contribution to the development of therapies, but you don't find them very often in drug companies today," Munos said.
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During the daylight phase, contestants can see codes submitted by others, and can modify and resubmit those codes. Once the contest enters the daylight phase, Lakhani noted that there is a dramatic improvement in performance as individuals build on other competitors' codes.
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If you can find a way to bring these people together in an environment where they can cooperate, the returns you get from that are much greater than those from individuals who are working solely inside their own companies," Spencer said. Improved Validity Cohen called for collaboration, not only in early stages of research, but in the clinical testing of potential drugs.
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"We measure variables prospectively in communities using what we believe will ultimately become a personalized discovery platform that has the ability for one person to use this platform alone, or instantly form collaborations with others, to identify novel and new disease biomarkers, and treatments that work," Heywood said. Heywood asserted that the data gathered in this manner can be of better quality than data gathered in typical clinical trials, as exemplified by the data the site gathered on whether lithium forestalled progression of amyotrophic lateral sclerosis (ALS)
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"We both have our ways of doing things, so bringing our two methods of management together strengthens the project and gives us a much more diverse set of expertise in the management staff." TCGA has four types of centers, three of which are funded by NCI, and the fourth by the NHGRI: • Genome Characterization Centers, including Cancer Genome Characterization Centers, will perform gene expression, copy number, SNP, DNA methylation, and microRNA characterization. • Genome Sequencing Centers will generate second generation sequence data for mutation detection.
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Hundreds of tissue accrual sites are providing retrospective samples for TCGA, as well as other sites that are providing tissues prospectively. All the samples initially go into two biospecimen core resources, which are funded by NCI, for processing before they are sent to the genome characterization and sequencing centers.
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0 PRECOMPETITIVE COLLABORATION IN ONCOLOGY RESEARCH BOX 2 Continued Center Name Type of Center or Resource Cancer Genome Characterization Genome Characterization Genome Data Analysis Genome Sequencing Biospecimen Core Data Coordination Baylor BCCA Brigham/Harvard Broad/Harvard Harvard Hopkins HudsonAlpha IGC ISB Lawrence Berkeley MD Anderson Nationwide Children's NCICB Sloan-Kettering SRA UC Santa Cruz UNC USC WUSM
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WORKSHOP SUMMARY NOTES: Baylor = Baylor College of Medicine; BCCA = British Columbia Cancer Agency; Brigham/Harvard = Brigham & Women's Hospital and Harvard Medical School; Broad/Harvard = Broad Institute of the Mas sachusetts Institute of Technology and Harvard University; Harvard = Harvard Medical School; Hopkins = Johns Hopkins University; Hudson Alpha = HudsonAlpha Institute for Biotechnology; IGC = International Genomics Consortium; ISB = Institute for Systems Biology; Lawrence Berkeley = Lawrence Berkeley National Laboratory; MD Anderson = MD Anderson Cancer Center; Nationwide Children's = Nationwide Children's Hospital; NCICB = National Cancer Institute Center for Bioinformatics; SloanKettering = SloanKettering Institute; SRA = SRA International; UC Santa Cruz = University of California Santa Cruz; UNC = University of North Carolina; USC = University of Southern California; WUSM = Washington University School of Medicine. SOURCES: Vockley presentation (February 10, 2010)
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I stumbled across a project where an academic investigator had a very good science question and Lilly and another pharmaceutical company had the raw data that could be used to answer it. We are missing out on many of these opportunities if we do not find these precompetitive spaces to work in." Altshuler added that collaborations can focus people's efforts on rare diseases or problems that may otherwise receive less attention (see Box 4)
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. Shorten Drug Development Time Lines and Improve Efficiency and Cost-Effectiveness Several speakers pointed out that economies of scale and scope generated by collaborations should speed up the pace of drug development simply
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The ultimate mission of Sage is to accelerate the elimination of human diseases. Sage has several active partners, including the National Cancer Institute's Clinical Trials Cooperative Group Program, universities, government agencies, foundations, pharmaceutical companies, informa tion technology and tool providers, and patient advocacy groups.
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She estimated that her collaborative clinical trial of biomarkers and treatments for breast cancer could cut the amount of clinical testing time for a new drug in half, 5 I-SPY 2 TRIAL (Inspection of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular analysis 2) is a Phase II multisite clinical trial testing multiple experimental drugs while simultaneously assessing the effectiveness of various biomarkers to predict response to the investigational agents (see section on what to share)
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OSDD structures its online forum by breaking down large, complex problems, such as how to develop effective therapies for tuberculosis, into smaller work packages, such as annotating the tuberculosis bacterium's genome, identifying drug targets and their expression, screening compounds to see if they inhibit targets, optimizing nontoxic compounds found to hit the targets, and pre clinical and clinical testing of the inhibitors. OSDD will accept any idea, software data, article, or molecule that might aid such drug discovery, and will provide funding for clinical testing of promising potential drugs.
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The two companies forged an agreement that enabled joint preclinical testing of combination therapy with them. When that testing showed promising results, the two companies agreed to do a continued
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Together the companies designed a testing plan that would assess the dose, sequence, and context of the combination, including subpopulations in which to test their combination of compounds. The collaboration agreement between Merck and AstraZeneca was staged so that initially it was just an agreement that covered preclinical rights and preclinical scope, and then expanded to include the clinical testing agreement.
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and Engelman, 2009. R01758 Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Cancer, Engelman, J
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Win–Win Situation Several speakers concluded their talks by noting that when precompetitive collaborations work, they can provide a win–win situation for drug companies, patients, academic researchers, and insurers by speeding up and by lowering the costs of drug development, and by reducing the risk and uncertainty of drug development. For example, the Myelin Repair Foundation's Accelerated Research Collaboration, described by Lakhani, aims to address systemic problems in medical research and commercial drug development through "a radical new process that recognizes the incentives and limitations of academic scientists, commercial biopharma, government regulators, and patients and their families, and fosters behavioral changes by adding tangible value to everyone" (MRF, 2010)
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This technical challenge has to be solved if you are going to do large-scale bioinformatics." McClellan added that addressing inconsistent or otherwise non-comparable data requires standards and infrastructure that can be costly to develop. Williams-Jones agreed, adding, "we are really good at building these big databases that won't talk to each other." He pointed out that even a big database with everyone's data interoperable within it is not sufficient without standard ways to analyze and interpret that data.
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Heywood added that PatientsLikeMe requires any companies it partners with for developing measures of disease to commit to putting those measures out in the public domain. "We don't want to be proprietary in the definitions of how we measure disease and ultimately want to make that a public resource," he said.
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According to the MRF website, patent protection can reduce the financial risks to pharmaceutical companies, which may increase the industry's interest in undertaking new drug development and clinical trials for myelin repair treatments. Income generated from patents is used to fund future research, with the aim to create a self sustaining research model.
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FDA Regulatory Issues Some speakers and attendees expressed hesitancy over their willingness to participate in new collaborative drug development models without knowing how well received those efforts will be by FDA. Munos pointed out what he called regulatory gaps.
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Now that the data from I-SPY 1 are maturing, they clearly show that complete pathological response can be a valid endpoint if you know how to analyze the data and include the right subsets of patients. If we work toward that as an industry, this will truly change drug development in the oncology world forever because this is not unique to breast cancer.
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medical science lead of emerging products at AstraZeneca-Oncology added that it is also important to understand what trial designs FDA would be willing to consider for the registration of combination therapies in development. Martin Murphy, chief executive officer of the CEO Roundtable on Cancer, noted that this was also a topic of discussion at the 2009 Conference on Clinical Cancer Research (see Box 7)
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Rewards and incentives in many of these institutions are set up to encourage competition, and do not recognize collaborative efforts. In addition to the traditional academic notions of academic freedom and intellectual autonomy, "merit, tenure, and promotion processes in the university undermine everything we are trying to accomplish here," Cohen said.
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As an outcome of the 2009 conference, FDA has agreed to issue industry guidances on two of the topics -- data submission standards and evidence requirements, as well as the development of rational drug combinations with investigational targeted agents. The first guidance will discuss the type and extent of data collection required for supplemental indication trials and the second guidance will explore situations in which a largescale fourarm Phase III trial (Drug A vs.
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This collaborative effort collected data from eight trials run by four companies and one NCI Cooperative Group to determine whether important safety informa tion would be omitted by only gathering toxicity data on a subsample of patients enrolled in a supplemental indication clinical trial using a drug for which a substantial toxicity profile already exists. The results of the analysis suggested that data subsampling would not lose important information about the safety profile.
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launched the Critical Path Initiative, the agency's strategy to drive inno vation in the scientific processes through which FDAregulated products are developed, evaluated, manufactured, and used. In support of the Critical Path Initiative, the Critical Path Institute (CPath)
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The goal is to use these disease models to stratify drugresponsive frac tions in subpopulations. Then those tools can be used to model and simulate clinical trials that could improve the likelihood of suc cess.
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, for example, is much more hands-on with their funded investigators, he said, whereas the National Human Genome Research Institute (NHGRI) is less involved.
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Likewise, on the academic side, there is greater availability of high-quality data in the public domain. "The quality of data [in the public domain]
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Precompetitive is not what you've got, but what you do with it." Consequently, the methods, standards, and tools used in biomedical research and the early stages of development are not as proprietary. How that information is used by individual companies in the later stages of drug development is more important.
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Eck added that the "tools that we all build internally at great expense, such as PET [positron emission tomography] ligands and biomarkers, really would be better served if we just made them available for free because they would become validated more quickly, or their warts would become known more quickly." Speakers discussed several precompetitive collaborations involving biomarker data and biomarker standards, including the Biomarkers Consortium and its associated projects.
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Pistoia Alliance The Pistoia Alliance is an initiative to streamline precompetitive ele ments of the pharmaceutical drug discovery workflow, such as chemistry, biological screening, and logistics, by developing open standards for common business terms, relationships, and processes. More specifically, this collaboration of more than 20 mostly pharmaceutical companies aims to: to reducing the time and expense required to bring new drugs to market (Park et al., 2004; PCAST, 2008; Zerhouni et al., 2007)
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. The initiative's overall goal is to make Europe the world leader in pharmaceutical research for the benefit of the economy and society, by removing research bottle necks in the current drug development process.
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"The whole goal of the Consortium is to drive significant public health benefit," said Wholley. The Consortium has more than 50 contributing members, including 12 of the largest pharmaceutical companies, academic researchers, and numerous nonprofit organizations.
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. I-SPY TRIAL0 I-SPY 2 is a Phase II multisite clinical trial that was launched March 17, 2010, to test multiple experimental drugs while simultaneously assessing the ability of various biomarkers to predict response to the investigational agents.
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to collaborate on improving the development of cancer therapies and the outcomes for cancer patients through bio marker development and evaluation. Established in 2006, the goal of the OBQI is to validate particular biomarkers so they can be used to evaluate promising technologies in a manner that will shorten clinical trials, reduce the time and resources spent during the drug development process, improve the linkage between drug approval and drug coverage, and increase the safety and appropriateness of drug choices for cancer patients.
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Wholley was enthusiastic about the I-SPY 2 TRIAL. "This will potentially revolutionize the design of clinical trials," he said.
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To speed up the process, the Biomarkers Consortium, trial organizers, and FDA worked together to develop a plan in which the master IND being used by the trial is being held by the FNIH, who manages the Biomarkers Consortium along with several other large biomedical partnerships. The FNIH was chosen because it was seen as a trusted, neutral third party that can sponsor and manage the trial fairly and effectively.
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in the I-SPY 2 TRIAL. In I-SPY 2, inventing organizations grant exclusive licenses to new IP to the Foundation for the National Institutes of Health (FNIH)
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Curt suggested sharing standard contract language for clinical trials to expedite negotiations required between industry and publicly-funded investigators before the launch of a collaborative trial. He spoke about the CEO Roundtable on
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, has a suite of standard forms for sharing biological materials, and recently released a legal tool developed by Creative Commons called CC0 1.0 Universal13 that allows people to mark data as being in the public domain so that anyone can use them. Information on Failed Compounds or Those on the Market Munos suggested sharing everything that is known about a compound once it is approved for marketing so that physicians can use that knowledge 13 See http://creativecommons.org/publicdomain/zero/1.0/.
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To help achieve its first goal of expediting the R&D process, the Life Sciences Consortium acted on research that found the most rate limiting step in the development of clinical trials was contracting and budgeting. To expedite the contract and budget negotiations required between industry and publicly funded investigators before the launch of a collaborative trial, the Consortium and NCI reviewed copies of 78 redacted clinical trial agreements and identified 45 key concepts related to intellectual property, study data, subject injury, indemnifica tion, confidentiality, and publication rights.
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that analyzed cancer clinical trial data voluntarily submitted by the Consor tium's members to discern the optimal types and amounts of data that should be collected in Phase III trials for supplemental approvals of cancer drugs. The Food and Drug Administration is currently considering the recommendations and plans to develop a guidance for industry on this topic.
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These tools enable people and institutions to expand and enhance open access to published research and data. For example, the Personal Genome Project uses these tools to put the genomes they decode in the public domain.
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. nal has a policy of publishing the negative results of clinical trials so they will surface in PubMed searches.
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"The biggest gap is in really understanding the molecular basis of disease and matching these compounds with the patients, and that is one for which there is very little sharing," said Huang. TyPES OF PRECOMPETITIVE COLLABORATIONS Several different precompetitive collaboration types have been developed to date.
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In contrast, prizes and contests are collaborations with open participation but restricted access to outputs, which are only used by the sponsors of the prize or contest. Altshuler charted the four types of collaborations, according to how open access is to participation and outputs, along with the four main goals of collaborations, to define eight basic models of precompetitive collaborations (see Box 14)
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The eight models of precompetitive collaboration, and examples of each, as analyzed by Altshuler: open-source initiatives, industry consortia for process innovation, discovery-enabling consortia, public–private consortia for knowledge creation, prizes, innovation incubators, industry complementors, and virtual pharma companies. NOTE: Alliance for Cell Sig = Alliance for Cellular Signaling; AZ = AstraZeneca; Biogen bi3 = Biogen Idec Innovation Incubator; C-Path = Critical Path Institute; CCMX = Competence Centre for Materials Science and Technology; CDISC = Clinical Data Interchange Standards Consortium; CERN = European Organization for Nuclear Research; CHDI = CHDI Foundation; GSK = GlaxoSmithKline; HapMap = International HapMap Project; HGP = Human Genome Project; MMRF = Multiple Myeloma Research Foundation; NLP = Natural Language Processing; OD = Open Database; OSDD = Open Source Drug Discovery; P&G = Procter and Gamble; RNAi = RNAi Consortium; SAEC = International Serious Adverse Event Consortium; SLAC = SLAC National Accelerator Laboratory; SNP = Single Nucleotide Polymorphism; Tech to Bus = Technology-to-Business.
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Diabetes Genetics Init. consortia for Restricted par ticipation C-Path SNP Consor tium Innovative Medicines Init.
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• Public–private consortia for knowledge creation, which include the Biomarkers Consortium and the Innovative Medicines Initiative. Such partnerships provide industry and academia with the oppor tunity to work more closely together within a framework other than the traditional sponsored research relationship, Altshuler said.
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As the leading funder of multiple myeloma research, the MMRF has facili tated basic research, clinical trials, and correlative studies, including collaborative studies. For example, the Multiple Myeloma Research Consortium (a sister organization to the MMRF)
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• Actively manage the collaboration; consider using a trusted third party in the management of the collaboration. • Make sure to address important legal details of collaborations, such as antitrust issues, intellectual property, and contracts.
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Such a road map was crucial to the success of SEMATECH, Spencer said. He also suggested planning for globalization because drug development is an international effort.
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Involve the Right People Several people at the workshop stressed that the leadership of a participating organization, company, or institution needs to actively participate in and be committed to the collaboration. Curt pointed out that because drug company CEOs were involved in the Life Sciences Consortium, they could use their authority to dictate that other divisions of their companies, such as the contracts and legal departments, collaborate with NCI and the other participants of the Life Sciences Consortium.
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He pointed out that participants in the Life Sciences Consortium's effort to develop standard clauses for research contracts included individuals who do the actual contracting for their institutions and could indicate what type of contract language would be acceptable for their institutions. Altshuler added that "if you are trying to establish a new standard for your industry, you need a critical mass of players behind it -- that is quite important." Friend was critical of many consortia, "which often end up being driven by consensus and bring together the lowest common denominator for the longest period of time," he said.
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"I think our current granting system does not really acknowledge the kinds of infrastructure that are needed, and our Congress has not given FDA the funding it needs to participate in these kinds of precompetitive discussions." Nguyen added that many resources for supporting collaborative efforts, particularly those that involve creating and maintaining a public domain (commons) where data or other information is shared, are ephemeral.
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The FNIH, for example, acts as a trusted third party for the I-SPY 2 TRIAL. The FNIH holds the master IND for the project, and it also manages the trial through the Biomarkers Consortium.
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Department of Justice when devising collaborations if there are concerns with antitrust issues. Curt noted that the Justice Department approved the Life Sciences Consortium's START clauses for academic and industry collaborations.
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Because if you do not get people feeling in academia like they have credit, then they are not going to collaborate either," said Esserman. Other speakers suggested that industry also has a culture of competition and secrecy, even though most pharmaceutical companies have the same information on basic biology and thus are pursuing the same targets for their drugs.
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NExT STEPS Participants at the conference suggested several next steps to take to foster precompetitive collaborations, including • Seeking public support for collaborations and advocating for funding; • Holding a meeting with key constituents in oncology to determine how to move the field forward; • Having an appropriate authoritative body establish a set of stan dards for the sharing of data, material, and tools, and/or general standards for collaboration; • Publicizing collaboration success stories and management plans; and • Developing innovative business models for collaborations. Seek Public Support for Collaborations Some speakers offered specific suggestions for fostering more public support and funding for collaborative ventures.
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Woosley suggested advocating that the implementation of electronic medical records that will soon be supported by the federal government stimulus bill include common data elements that could be useful in research and would help ease research collaborations. Publicize Collaboration Success Stories and Management Plans McClellan suggested publicizing collaborations that have been done successfully and others that show promise, as well as the specific pathways for doing collaborations effectively.
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Develop Innovative Business Models Some speakers suggested devising innovative business models that support collaborations. Woosley suggested creating "innovative ways for companies to come together to pool their diagnostics and drugs, and to develop more comprehensive strategies, rather than just a single agent." Esserman noted that for the I-SPY 2 TRIAL she had to come up with a new business model to support the trial because she did not want to adhere to the old model of having a drug company, whose drug was being tested, financially back the study.
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"Oncology could take the lead in looking at something that was funded by the government and by private industry, and you have got a CEO Roundtable already, so you are light years ahead of where we were in the semiconductor industry in 1985 when we were trying to get SEMATECH rolling." Cohen pointed out that a centralized support organization for collaborations in oncology or biomedicine could not only support scientific pursuits, but also, or instead, efforts to develop new regulatory pathways for collaboratively developed drugs and other public policy advances needed to support collaborative research.
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Altshuler said there is a false dichotomy of centralized support versus support for more entrepreneurial ventures. "Certain types of problems can be handled one way, and certain ones can be handled another way.
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SuMMARy After 2 days of presentations and lively discussion, during which Washington, DC, was blanketed in a crippling snowstorm, it became apparent that a number of factors are currently driving precompetitive collaborations, including declining R&D budgets combined with the growing complexity of biomedical research. Several participants viewed precompetitive collaboration as a means to solve some of the problems that currently plague the drug development process both in oncology and in other therapeutic areas.
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2006. Invisible barriers to clinical trials: The impact of structural, infrastructural, and procedural barriers to opening oncology clinical trials.
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2010b. The Biomarkers Consortium launches I-SPY breast cancer clinical trial.
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2004. Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development.
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2009. Utility of adiponectin as a biomarker predictive of glycemic efficacy is demonstrated by collaborative pooling of data from clinical trials conducted by multiple sponsors.
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