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6
Future Considerations
Over the course of the workshop, several panel presentations concluded
with general discussions of the major issues affecting the development and
use of biomarkers. These discussions focused on three main issues: creating
incentives for organizations to collaborate, moving forward even when a
thorough understanding of biological mechanisms is lacking, and dealing
with different levels of risk in biomarker development.
CREATING INCENTIvES FOR COLLAbORATION
Janet Woodcock noted that approximately half a million reports of
drug-induced injuries are submitted to the Adverse Event Reporting System
annually. These injuries represent both a major public health problem and
substantial health care costs. At the same time, observed Daniel Bloomfield,
the expectations for safety and the amount of research needed to get a drug
approved have increased, even though the typical commercial life of a drug
has not changed. Given the reduced returns from drug development, fewer
companies are pursuing difficult projects with the potential to reduce the
toll of drug-induced injuries.
Woodcock emphasized that investing millions of dollars in basic research
and hoping that the resulting knowledge will automatically become avail-
able for use in human populations may be insufficient. Instead, special
initiatives often are necessary to translate new knowledge into results that
can have an impact on health care. Woodcock cited two such projects
that have been supported by the Foundation for the National Institutes of
Health (FNIH). One is the Alzheimer’s Disease Neuroimaging Initiative,
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FUTURE CONSIDERATIONS
which also has been supported by NIH and industry and in which the FDA
participates. Another is the Osteoarthritis Initiative, a prospective investi-
gation of a large number of potential biomarkers. In addition, Woodcock
noted that the FNIH helped establish the Biomarker Consortium, a major
public–private biomedical research partnership with participation from a
broad and diverse group of stakeholders, including government, industry,
academia, and patient advocacy and other nonprofit private-sector organi-
zations. The goal of the Biomarker Consortium is to collaborate in rapidly
identifying, developing, and qualifying potential high-impact biomarkers.
Many workshop participants stressed that such collaboration among
industry, the FDA, and academic researchers could yield much more rapid
progress in the development of biomarkers. The question then becomes
whether incentives could be established to promote such collaboration.
One important set of incentives, according to Frank Sistare, would be
clear agreement on the data that could be generated in regulated phases
of drug development that would not need to be submitted to regulatory
authorities. When drug development is in its earliest stages, companies need
freedom to operate without worrying about having to submit all such data
to regulators, who may then decide that the development process should be
slowed so that certain concerns can be probed more thoroughly. The FDA
has offered guidance on these decisions, and there is an ongoing dialogue
with the agency to clarify the issues involved. But the current lack of clarity
continues to inhibit industry from generating data that could be extremely
useful for fear that the data could be used to slow drug development.
Government and industry need to be creative in implementing incen-
tives and removing disincentives, Sistare continued. For example, could
a company be offered a reduction in user fees for the submission of data
related to the discovery or development of safety biomarkers deemed criti-
cally important by regulatory authorities, or could it gain a period of added
exclusivity for a product? Although both of these steps would require legis-
lation, they represent the kind of out-of-the-box thinking that is needed.
James Stevens of Eli Lilly suggested that incentives might include stag-
gered goals for what can be done in 1 year, 3 years, and 5 years. Some
research projects take relatively long to complete, and potential partners
in collaboration may be unwilling to participate unless they know when
particular goals should be achieved.
Other workshop participants questioned the practicality of establishing
new financial incentives to foster partnerships. Given the many financial
demands on the federal government, said Alastair Wood, incentives that
require additional funding probably will not succeed. Unless collaborations
have realistic objectives and expectations, the potential to make progress
through cooperation may be forfeited. Wood also questioned why incen-
tives are necessary if a partnership results in drugs being developed more
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0 DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
quickly and with less investment of resources. If a given partnership makes
sense, why are incentives needed to foster it?
According to Robert Califf of the Duke University Medical Center,
multi-institutional partnerships and collaborations with industry will be
necessary for substantial progress to occur. This requires both “big sci-
ence,” characterized by extensive cutting-edge technologies, and “big popu-
lations,” where associations can be detected and refined. An undertaking
of this magnitude, Califf observed, is too big for individual companies,
even large multinationals, no matter how global they are. The same is true
for individual academic centers, even those with broad, interdisciplinary
skills and knowledge. Califf described his own experience with the recently
launched David Murdock Research Institute as an example of the type of
partnerships that will be required in the future. Created by a major philan-
thropic gift, the Institute is a collaboration among Duke University, the Uni-
versity of North Carolina (UNC) system, and Dole Foods. The Institute also
has links to universities and industries throughout the United States, and
partnerships with organizations in India and Singapore. Substantial funding
has enabled the Institute to combine large-scale biobanking and state-of-
the-art technology with support for manufacturing and commercialization.
Califf characterizes the Institute’s approach as a “factory approach to bio-
markers development.”
Califf further observed that existing public–private partnerships have
been inhibited by uncertainty about how to manage conflicts of interest
when public entities and for-profit corporations work together. A lack of
clarity about the terms of engagement can stifle creative solutions.
Interests and incentives will vary even from one federal agency to
another. For example, NIH has taken on important responsibilities, such as
the Drug Induced Liver Injury Network (DILIN) and the Biomarker Con-
sortium, that differ from the responsibilities of the FDA. Yet interagency
collaborations have already begun to emerge, as exemplified by FDA and
NIH interactions with respect to the DILIN initiative and the Biomarker
Consortium. Successful partnerships hinge on finding common ground
among agencies and between the federal government and industry. If impor-
tant tasks are being overlooked within the federal government, it may be
necessary to develop a new infrastructure within a federal agency to carry
out those tasks. For example, a new, independent, cross-agency institute
may be needed to foster biomarker development, suggested Richard Paules
of the National Institute of Environmental Health Sciences.
John Bloom pointed out that partnerships could help establish stan-
dards for submission databases, review databases, and electronic medi-
cal records. Greater standardization throughout the biomarkers field also
would encourage more sophisticated approaches to informatics. Bloom
expressed the opinion that biomarker development faces no insurmount-
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FUTURE CONSIDERATIONS
able barriers that cannot be overcome through a coordinated effort. But
opportunities need to be seen as worthy of the attention and resources of
institutions.
Wood also noted that many stages of biomarker development lend
themselves to a noncompetitive structure. The more information that is
shared among companies, the more productive research will be. Many
companies see secrecy as essential to gaining an advantage, but secrecy
also works in reverse. For example, other companies may have informa-
tion about problems with another drug in the same class as the drug under
development. A drug that proves to have problems early in the develop-
ment process often is not extensively discussed outside the company that is
developing it. Sharing such information could reduce the costs of research
without compromising competitive positions.
Concluding the discussion, William Mattes of the Critical Path Institute
suggested that any incentives put in place need to be carefully considered
and structured so they do not create the appearance of favoring individual
stakeholders. Incentives will be successful if they account for the vary-
ing interests of different groups. For example, academic researchers are
rewarded for publishing their work and are unlikely to share information
extensively before publication. Similarly, a company has incentives to work
on its own compounds rather than in partnership with other companies on
projects that are not directly product related.
MOvING FORWARD WITHOuT uNDERSTANDING MECHANISMS
As Califf pointed out, it is possible to make predictions with bio-
markers that are probabilistically quite accurate without knowing much
if anything about the mechanisms behind those biomarkers or the biologi-
cal processes they reflect. This is already the case with cancer treatment,
with physicians and patients being able to purchase multiple prognostic
tests, each based on somewhat different arrays of biomarkers. While such
options are available, however, it is always preferable to understand the
mechanism involved because of the possibility of developing new targets
for treatment or redesigning molecules to avoid toxicity by not engaging
the mechanism.
Ravi Iyengar of Mount Sinai School of Medicine, whose workshop
presentation addressed the role of systems biology in biomarker develop-
ment (see Box 6-1), put the issue in a different context. Often a general
mechanism is apparent for 90 percent of the cases of a disease or adverse
drug reaction, and most of the other cases can be accounted for by using
more tests and statistical associations. But 1 percent of cases may remain
mysterious unless a biological mechanism is understood extremely well. If
a signature for these outliers exists, Stevens asked, will clinicians be com-
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
BOX 6-1
Systems Biology and Biomarker Development
In his presentation, Ravi Iyengar described the challenges facing systems
b
iology, as well as the potential of this new perspective on biological processes
to aid in the development of biomarkers. There are several definitions of systems
biology. In the context of biomarker discovery, Iyengar described systems biology
as the use of computational approaches to drive understanding. Network and
statistical models that are implemented computationally are used to probe how
the parts of a biological system function together. An understanding can be gained
of how and why a complex biological function occurs as it does, although detailed
mechanistic understanding of a molecular interaction may require different kinds
of studies.
Biological systems exist at different levels—from the organ level, to tissues
and cells, to intracellular networks, to the molecular level. Many of the actual
physiological measures in medicine are made at the level of clinical analysis and
indicators. Systems biology models can often relate events at a lower level to clini-
cal outcomes. A great challenge for systems biology, said Iyengar, is to integrate
understanding of these different levels vertically.
As an example of a correlation without detailed understanding, Iyengar cited
an FDA-approved breast cancer diagnostic that is based on 70 genes, while an
a
lternative diagnostic is based on 76 genes. Yet the two sets have only three
genes in common, which raises the question of how the sets are related. Research
in Iyengar’s laboratory has shown that both sets of genes are linked to overlapping
sets of upstream transcription factors and signaling. In turn, transcription factor
activity profiling and network analyses can help identify relationships between
mutated disease genes and prognostic gene expression signatures. This is one
way to connect events at different levels, enabling oncologists to use molecular
markers in treatment decisions.
Iyengar’s laboratory also has been looking at congenital and drug-induced
arrhythmias. Using genes identified as being related to long-QT (LQT) syndrome,
he and his colleagues built a disease gene network to see how the genes are
related. From a very large network of 15,000 nodes and 70,000 interactions, they
identified an LQT gene “neighborhood” of about 1,400 nodes. They found that
unique networks can be constructed around genes involved in disease states, and
the properties of these networks can help explain some of the characteristics of
different states.
Iyengar said that these networks also can explain drug side effects because
there is a relationship between the genomics and systems pharmacology of
LQT syndrome. Networks of biomarkers are likely to perform better than single
biomarkers for complex diseases because networks across genes integrate mul-
tiple sources of information. In this way, systems biology approaches can provide
insight into the pathogenesis of adverse events and suggest alternative targets
for treatment. It may even be possible to predict clinical outcomes 2–5 years into
the future on the basis of information from cellular or molecular networks.
SOURCE: Iyengar, 2008.
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FUTURE CONSIDERATIONS
fortable using it to make clinical decisions without knowing the mechanism
behind a response?
Several workshop participants responded that biomarkers can pro-
vide valuable information even when biological mechanisms are largely
unknown. At a fundamental level, Califf observed, many biological mecha-
nisms remain at least partly unknown. Woodcock stated that medicine is
conducted among many uncertainties, and reliable information that can
distinguish who is and is not at risk is an advance beyond not having such
information. Also, Woodcock pointed out that the discovery of predictive
biomarkers can lead to research on their reliability and on their association
with outcomes.
Bloom emphasized the importance of not interpreting the term “bio-
marker” too narrowly. A biomarker is a piece of information that can be
used correctly or incorrectly in making a decision or seeking additional
information. The term “biomarker” can even be misleading if it is inter-
preted as denoting a single measurement without a broader biological
context.
DEALING WITH DIFFERENT LEvELS OF RISk
Bloomfield described a hypothetical scenario involving a drug that is
effective at treating depression but causes a mean blood pressure rise of
2 millimeters (mm) of mercury in a test treatment population. Should such
a drug be approved? The ultimate question in such cases, he said, is the level
of risk that patients, physicians, and society are willing to accept.
Woodcock emphasized the complexity of this issue. The older anti-
psychotics, for example, posed major risks, but at one point they were
the only available treatments, so they were widely used. Regulators know
that a 2 mm rise in blood pressure will translate to a mortality difference
if a drug that causes it is used long enough. In the past, calculations of
risks and benefits were left largely to physicians and patients; today, other
groups play a role in these calculations as well. This is one example of how
biomarkers could be pivotal. If it were possible to identify subgroups who
would experience the 2 mm rise in blood pressure or would have a good
response to the antidepressant, the risk/benefit calculation would be easier
to make.
Califf suggested that an effective drug for depression would save lives,
and therefore should be available on the market. At the same time, how-
ever, an outcome study should be done to determine the true effect of the
drug on the balance of risk and benefit. The more biomarkers that can be
identified to gauge the effects of a drug, the stronger the signal will be as
long as the research reflects an awareness of the complex methodology that
must be applied to understand the joint effects of multiple markers. Iyengar
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
pointed out that most predictions take the form of probabilities, which do
not tell a patient or physician exactly what to do, and proper decisions will
be more likely if all parties involved understand the role of probabilities in
decision making.
Insel proposed a promising way to involve the public in the biomedical
enterprise and inform them about its results. He suggested that every
patient should become a partner in a research program addressing the con-
dition affecting that patient. This has already happened in some areas, such
as cystic fibrosis and particular kinds of childhood cancer. It could occur
as well for much broader groups, such as everyone with cardiovascular
disease.
Califf responded by saying that one of the most encouraging aspects
of establishing the David Murdock Research Institute is that the organizers
have been overwhelmed by calls from people in the surrounding region who
want to be enrolled in epidemiological studies. Involving these volunteers
in research will take careful planning, but they represent a largely untapped
resource that could speed the pace of scientific progress.
REFERENCE
Iyengar, R. 2008. Systems biology of biomarker sets. Speaker presentation at the Institute of
Medicine Workshop on Assessing and Accelerating Development of Biomarkers for Drug
Safety, October 24, Washington, DC.
