WASHINGTON, DC—Scientific research has always been intensely competitive and driven by a solo investigator with a promising hypothesis. But that is changing, as oncology researchers realize the value of early-stage team science in identifying potential targeted cancer therapies.
Recognizing that new findings in oncology are exploding, that drug development is increasingly costly and lengthy and that approval of new molecular entities is plummeting, the National Cancer Policy Forum (NCPF) of the Institute of Medicine sponsored a two-day workshop here to explore how precompetitive collaborations can speed cancer research and bring new therapies from bench to bedside faster. A written summary report from the workshop is planned.
Speakers highlighted the value of early-stage oncology research collaborations to advance knowledge in the areas of biomarker data; biological pathway and target information; informatics resources and tools; methods of data integration; and common data standards and formats.
But speakers also pointed out that research collaborations raise concerns related to intellectual property, how to standardize data, communication, assigning credit for new findings, sharing of revenues, public perception of conflict of interest, and management of partnering institutions. Despite these problems, “there is a demand for new research paradigms,” affirmed Neal H. Cohen, MD, MPH, MS, Vice Dean of the School of Medicine and Professor of Anesthesia and Perioperative Care and Medicine at the University of California San Francisco.
He pointed out that on average only one in 10 drugs that enter clinical trials ever becomes a marketed product; that there is increasing demand for new evidence-based therapies; and that genetics and genomics have focused a very bright spotlight on the need for personalized approaches to cancer treatment.
“We need to create non-exclusive consortia, alliances, and networks, particularly in precompetitive areas of research,” said Dr. Cohen. “We need to pool knowledge and resources to fill technology gaps, and encourage sharing of information to foster innovation—without compromising commercial development opportunities.”
Biologists as Hunter-Gatherers
Until recently, “the way biologists have functioned is as hunter-gatherers,” said Stephen H. Friend, MD, PhD, President and Chief Executive Officer of Sage Bionetworks, a nonprofit foundation in Seattle that provides an open-access platform for sharing and disseminating complex data on disease biology. But, he said, agreeing with Dr. Cohen, “We now need to jointly build evolving models of health and disease,” and these involve collaboration, cooperation, and sharing of data.
Dr. Friend, a former researcher at Fred Hutchinson Cancer Research Center's Seattle Project, pointed out that the first chemotherapy drug a cancer patient receives is usually the standard of care in oncology, but only 25% of patients have a significant response to that drug. Sage, which has a number of active partners, generates new disease models designed to increase drug efficacy through more individualized therapy, for example, such as its nonresponders project.
In an interview, Dr. Friend explained that he differentiates precompetitive research, which is designed to understand disease biology, from research on a particular pharmaceutical compound. Sharing data on disease biology is much easier than sharing data on a particular compound, where issues of intellectual property are more intense, he noted.
Asked how research collaborations will fit in with the US Food and Drug Administration new drug approval process, he predicted, “We will see more joint sponsors of new molecular entities…the FDA is going to have to deal with this.”
“Nobody is smarter than everybody; a convergence of multiple factors has led to the emergence of public-private partnerships in biomedicine,” said David Wholley, MPhil, Director of the Biomarkers Consortium at the Foundation for the National Institutes of Health (FNIH). He said the decline in NIH budgets, plunging productivity in biopharmaceutical research and development, and an increasingly challenging and complex regulatory environment have led to the emergence of viable collaborative models, such as the Biomarkers Consortium (whose members include the American Association for Cancer Research and the American Society of Clinical Oncology), the SNP (Single Nucleotide Polymorphism) Consortium, and the Gates Foundation.
Mr. Wholley noted that out of 1,261 putative cancer protein or peptide biomarkers described in the scientific literature, only nine are FDA-approved as “tumor-associated antigens” (fewer than one per year since 1998).
“This ‘biomarker barrier’ in which candidate biomarkers have not been validated needs to be overcome,” said Mr. Wholley, pointing out that research collaborations through a consortium are a good strategy to overcome this barrier. (A 2007 IOM report, “Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment,” recommended that industry and other funders of biomedical research establish international public/ private consortia, modeled after the SNP Consortium, to generate and share methods and precompetitive data on the validation and qualification of cancer biomarkers for specific uses.)
Prototype for Precompetitive Collaboration
For a prototype for precompetitive collaboration, Mr. Wholley cited the multi-site I-SPY 2 Trial for breast cancer (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis).
“We think this will potentially revolutionize the design of cancer trials,” he said. I-SPY 2 has an adaptive design intended to enable Phase II decisions on response to be made in months instead of years; it aims to further validate known stratifying biomarkers in breast cancer and move several other biomarkers toward qualification.
The adaptive trial design allows many drugs and combinations to be evaluated; successes graduate to Phase III, and underperformers are dropped. Patients will be assigned to a treatment agent based on their specific biomarker signatures; up to 12 new Phase II agents will be tested.
In this trial, “no single company stands to be the sole beneficiary of the I-SPY 2 project,” he said. Pre-existing intellectual property relating to biomarkers will remain with the generating companies, and will be licensed for use in I-SPY 2. New intellectual property will be managed by the “FNIH, acting as a trusted third party to ensure fair and appropriate licensing of new inventions arising from I-SPY 2.” FNIH will return a fair share of royalties to inventing organizations.
‘With Adaptive Design, You Learn as You Go’
“We wanted to rapidly learn how to tailor agents; with an adaptive design you learn as you go,” said Laura Esserman, MD, MBA, a principal investigator of I-SPY 2 who is Professor of Surgery and Radiology at UCSF and Director of the Carol Franc Buck Breast Care Center.
She pointed out that by very carefully profiling breast cancer patients according to biomarkers, the hope is to help speed promising agents to Phase III trials. Currently, she noted, “only 20% of drugs succeed in Phase III, and that's not a very good track record.”
Dr. Esserman added, “It's the power of the shared data that is so important. I am a clinician and I am highly motivated. The sooner we can find better solutions for patients, the better.”
Mark McClellean Weighs In
Mark B. McClellan, MD, PhD, Director of the Engelberg Center for Health Care Reform at the Brookings Institution, agreed with Mr. Wholley and Dr. Esserman that collaborations can be very helpful in qualifying biomarkers and in targeted cancer therapies: “Targeted cancer therapies are primed for action in precompetitive collaboration,” said Dr. McClellan, a former FDA commissioner and former administrator of the Centers for Medicare and Medicaid Services.
He said it helps to have a neutral safe harbor such as the FNIH to run such collaborations. But, he cautioned, “Don't underestimate the management time and money required for the infrastructure to make the collaboration work.” He also noted that with team science there need to be incentives to reward the development of shared data, such as specific ways of giving credit.