Frank L. Douglas, Ph.D., M.D.
Partner, Pure Tech Ventures; Founder, MIT Center for Biomedical Innovation;
Senior Fellow, Ewing Marion Kauffman Foundation
Recently, the Kauffman Foundation launched a new focus on translational
medicine—the process of turning scientific breakthroughs in the lab into new
drugs and other patient therapies, often delivered by startup companies.
Despite the growing sophistication and promise of health care technology
research, fewer and fewer breakthrough ideas are finding their way out of
research institutions and into the hands of experienced clinicians and medical
product development teams. Patients suffer as a result, because promising
research and innovation are not being translated into new treatments.
Progress is being stifled by a crucial gap in the current research and
development pipeline: expertise and funding for early-stage innovations. Many of
the biotech startups, academic institutions, and government research centers
that perform critical early-stage work do not have the resources to move their
breakthroughs further along the commercialization pipeline. At the same time,
large pharmaceutical companies and venture capitalists are reluctant to invest
in early-stage research that lacks proven market potential and requires a longer
period of time to produce returns on investment. And federal investment in
medical research is tight—the 2008 budget for the National Institutes of Health
is only about 1 percent higher than its 2007 budget.
In the following essay, Frank Douglas, a senior fellow at the Kauffman
Foundation and a physician with extensive experience in pharmaceutical
innovation and research, sheds light on the lengthy timeframe between
breakthrough discoveries and new treatment options. He also explores one idea
for catalyzing medical research to help bridge the gap between the laboratory
and the bedside.
New R&D Models Gaining Ground
The In Vivo article I authored with Tolman and McKenzie pointed out that the
major companies pursued one of two R&D models, which we called "scale-based"
and "capabilities-based." Scale-based organizations sought to have all research
and development disciplines at-scale in-house. Capabilities-based organizations
sought to have research and early development at-scale in-house, but in-house
late-stage development was restricted to critical capabilities, with other
development work performed by Contract Research Organizations (CROs).
Today, most companies are moving away from a scale-based R&D organization
and embracing a capabilities-based organization, for reasons including the
overall cost of R&D; the growth of biotechnology companies, many of which do
not have the resources and experience to perform the extensive late-stage
clinical development programs that are needed for regulatory approval of a drug;
the proliferation of CROs; the rise of low-cost alternatives in India and China;
and the increasing government-sponsored (NIH and FDA), academic-industry
consortia to find biomarkers or develop special models that will improve the
ability to predict efficacy and safety.
A potential third model is the "discovery cluster." In this model, large
pharmaceutical companies perform their discovery through loose consortia of
academic and institute laboratories, and small biotech companies focused on
technology platforms or therapeutic areas, each of which has common goals and
specified deliverables for integration and further development by the large
pharmaceutical company.
Capabilities-based R&D organizations and discovery clusters can improve
the development of novel drugs, but they’re not sufficient to accelerate the
horizon time. |
Several years ago, I coauthored (with Peter Tolman and Malcolm McKenzie of
the strategy consulting firm, Monitor) an article for the medicine and business
magazine In Vivo. Our article tackled the question of how to spend
a billion dollars in research and development. At the time, the highest yearly
R&D budget among pharmaceutical companies was $547 million. And most
companies, regardless of the size of their budgets, aspired to produce two
discoveries capable of being approved by the FDA as a "new chemical entity" each
year. More than ten years later, in spite of R&D budgets that routinely
range between $3 billion and $8 billion a year, few companies have been able to
reach this benchmark.
This lack of productivity has resulted in a mixture of public consternation
and more vocal calls for action by government agencies. The deciphering of the
human genome eighteen years ago increased hope that a genomics revolution would
accelerate the discovery of new drugs. Yet few genome-based drugs have made it
to market.
Why the Delay?
Why this delay in getting potential therapies from the laboratory to the
patient? Noted economist Manuel Trajtenberg described two timeframes that are
crucial to realizing the potential of a fundamentally new technology. One is the
time from discovery to the horizon—when new products are actually developed. The
second is the time from discovery to application—when a new concept can be
turned into a tool that can be used to produce a commercial product. These
timeframes are clearly evident in the use of new technologies to find novel
drugs.
The horizon timeframe in drug discovery and development comprises several
critical steps, including the selection of a target receptor or enzyme; the
molecular validation of the relevance of that target to the disease; the
identification of a lead compound that is selectively active against the target;
the optimization of that compound through pharmacological, toxicological, and
human in vivo studies (conducted on living patients); the clinical
proof of the concept in a target patient population; and, finally, the large
clinical trials needed to demonstrate efficacy and appropriate safety (the final
validation of the target) in patients. This process takes, on average, ten to
thirteen years—and only then is the new therapy submitted to regulatory agents
for marketing approval.
The application timeframe, on the other hand, can be as little as a few
months from discovery to impact. For example, the decoding of the genome enabled
the rapid growth of proteomics, the study of proteins, and metabolomics, which
are integral to cell metabolism. Proteomics, genomics, and their application to
systems biology are having a significant impact on identification and validation
of new targets. Pharmacogenomics and pharmacogenetics are improving the
understanding of patient susceptibility to specific pharmacological agents.
All of these "omics" are contributing to finding biomarkers that can
potentially predict and monitor the efficacy or safety of any specific drug
candidate. However, it will require the coordinated and simultaneous application
of all of these technologies against a disease to significantly shorten the
overall horizon time—the time it takes to create new therapies.
How can we facilitate a shortening of the horizon time?
What we need is a challenging, overarching problem that clearly requires the
coordinated engagement of academia, large pharmaceutical companies, therapeutic-
and technology-based biotechnology companies, and hospitals and specialized
clinics—a challenge such as curing cancer.
Cancer has common features, but also requisite complexity because many
mechanisms drive the disease. It also has a combination of genetic and
environmental factors that contribute to its etiology or cause, as well as a
high personal and societal burden. And, as yet, there are few adequate therapies
for treating cancer.
We often hear officials and advocates talk about our nation’s fight against
cancer, but a potentially more effective solution would be to wage the fight in
a state with the required medical-scientific infrastructure to accommodate it. A
state such as Massachusetts, which has more than 300 biotech companies and
startups, several renowned academic institutions and schools, renowned hospitals
and clinical centers, and research centers of large pharmaceutical companies,
would be an excellent candidate.
A real breakthrough in the horizon time for the cure of cancer could be
achieved if, for example, Massachusetts became the "Cure Cancer within a Decade"
state. It could do this by appointing a Cancer Czar who would bring together
multiple organizations to work collaboratively to solve specific cancers. This
would enable standardization of research methods and assays, and accelerate the
adoption of, for example, personalized health records, and stratified or
personalized medicine.
While we cannot predict the outcome of such a venture, it is reasonable to
believe that a concerted effort, with a widely agreed-upon goal on a fixed
timeline, would spur the kind of coordinated engagement necessary to accelerate
the development of new treatments and finally bring the promise of genomic
medicine to the patient’s bedside.
