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2
Overview of Key Issues1
As indicators of biological function or state, biomarkers have many
potential applications in research and medicine: they can provide informa-
tion useful for the diagnosis, treatment, and prognosis of disease; they can
indicate whether a drug is having an effect in an individual and whether
side effects can be anticipated; and they can be used to screen populations
for particular biological characteristics or environmental exposures. Bio-
markers also have many potential applications in the development of drugs.
As Janet Woodcock of the FDA pointed out, they can improve the predict-
ability of drug development, and increase the value of preventative and
therapeutic interventions by targeting individuals with a high probability
of benefit and screening out those at high risk of side effects. Biomarkers
can be used to screen compounds for toxicity before they enter clinical
trials, to inform decisions about whether to develop a drug, to monitor the
development of toxicity, to forecast adverse events given wider exposure,
or to understand the mechanism by which a drug works.
Tests to assess the variability of a patient’s drug-metabolizing enzymes
are already being used to adjust doses in individuals. Other biomarker-
based tests are being used to determine whether an individual is at increased
risk of having an adverse reaction to certain compounds, and to avoid
treatment if the balance of benefit and risk is unacceptable. These kinds of
applications can be expected to multiply rapidly.
1 This chapter is based on the remarks of Janet Woodcock, Director of the FDA’s Center
for Drug Evaluation and Research; Alastair Wood, Managing Director of Symphony Capital,
LLC; and Thomas Insel, Director of the National Institute of Mental Health.
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OVERVIEW OF KEY ISSUES
Biomarkers can take many different forms. In preclinical screening,
for example, they may entail studies of gene expression or cell systems.
Animal studies can make use of genomic and proteomic techniques, thereby
increasing the probability that initial administration to humans will be safe,
or help establish the relevance of animal findings to humans. Biomarker
findings in clinical trials and postmarket data also can provide informa-
tion about mechanisms of drug toxicity or benefit and suggest the need for
additional nonclinical studies to fully elucidate the relevant mechanisms.
In a clinical setting, such information can be used, for example, to monitor
reactions to drugs in individuals or to deselect individuals from trials who
may be at risk from a treatment.
In considering the use of biomarkers for drug development, additional
issues arise, said Alastair Wood of Symphony Capital, LLC. To be useful,
a biomarker for toxicity found to be elevated by an investigational drug
in preclinical studies must provide some level of confidence that carrying
such a drug forward into clinical trials will produce toxicity in a proportion
of patients. This proportion must be significant enough to alter decision
making about developing the drug, to point to a different course of action
in patient selection for clinical trials, or to necessitate more detailed studies
prior to marketing so that safety signals can be assessed. Conversely, the
absence of elevation of a biomarker should imply confidence that a safety
problem will not occur in more than a known (low) proportion of patients.
In this way, the use of a biomarker can provide risk assessment and risk
mitigation, both to patients who are likely to receive the drug clinically and
to the development program carrying that drug forward.
Beyond these broad considerations lie more detailed questions. If a
biomarker is elevated in a small number of people in early clinical studies,
what is the overall risk to any given individual or to a population? If the
absolute degree of elevation is small, does this mean that the likely toxicity
will be mild when the drug is given to a large population of patients, and/or
does it mean that only a small proportion of patients will develop severe
toxicity? Unfortunately, the answers to these questions are seldom known
with any degree of certainty. Does the absence of a biomarker signal neces-
sarily predict long-term safety?
The use of biomarkers potentially could address several major prob-
lems associated with drug development. The costs of new drug development
have risen rapidly even as the number of new molecular entities (NMEs)
submitted to the FDA has fallen (Figure 2-1). In addition, a number of
drugs have been withdrawn from the market because of safety concerns. By
enhancing the ability to assess whether drug candidates are promising early
in development, biomarkers could reduce the costs of developing drugs and
bringing them to the market, enhance the safety of new drugs, and improve
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
60
50
Number of NMEs submitted
40
30
20
10
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
FIGuRE 2-1 The number of new molecular entities (NMEs) submitted to the FDA
has fallen since the mid-1990s.
SOURCE: Frantz, 2004. 2-1
the cost-effectiveness of drugs by targeting treatment to those patients with
the best balance of risk and benefit.
A particularly valuable use of biomarkers would be to help bridge the
gap between the preclinical and clinical development of new drugs. For
example, a preclinical biomarker that produces similar results in tissue
cultures or model organisms and in clinical use in humans might reliably
predict human responses to a compound. Or a bridging biomarker might
predict toxicity very early in humans—before harm occurs—and at very
low doses. As the FDA white paper Innovation or Stagnation: Challenges
and Opportunity on the Critical Path to New Medical Projects states,
“A new product development toolkit—containing powerful new scien-
tific and technical methods such as animal or computer-based predictive
models, biomarkers for safety and effectiveness, and new clinical evalua-
tion techniques—is urgently needed to improve predictability and efficiency
along the critical path from laboratory concept to commercial product”
(FDA, 2005, p. ii).
The remainder of this chapter reviews several important issues involved
in the use of biomarkers in drug development: predictions based on bio-
markers, validation vs. qualification, mechanisms vs. patterns, regulatory
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OVERVIEW OF KEY ISSUES
approval of biomarkers, regulation of single biomarkers vs. panels of bio-
markers, and measures of success. It concludes with a specific example: the
use of biomarkers to improve the treatment of mental illness.
PREDICTIONS bASED ON bIOMARkERS
One critical issue involved in assessing the utility of biomarkers is
how well they predict relevant outcomes. Measures of the performance of
biomarkers include sensitivity, specificity, calibration, discrimination, and
reclassification:
• Sensitivity represents the proportion of truly affected cases (per-
sons) in a screened population who are identified as being diseased
by the test, and is a measure of the probability of correctly diagnos-
ing a condition.
• Specificity is the proportion of truly nondiseased persons who are
identified as such by the screening test. For example, if a biomarker
has high sensitivity but low specificity, most of the truly at-risk
cases will be correctly identified, but many of the not-at-risk cases
will also be identified as at-risk.
• Calibration refers to the agreement between the predicted prob-
ability of an outcome and the actual probability when measured
in a population.
• Discrimination refers to the ability of a biomarker to distinguish
those with a disease or event from those without. A biomarker
could have excellent calibration with poor discrimination and vice
versa.
• Reclassification has become a critical issue in assessing biomarkers.
It refers to the ability of a biomarker measurement to move the
probability of an outcome beyond a threshold that leads to a dif-
ferent diagnosis, prediction of outcome, or clinical decision than
would have been made based on prior information.
The synthesis of these measures is complex since biomarkers can be
excellent for some purposes and mediocre for others, thereby complicating
their use for decision making. One of the greatest challenges to the applica-
tion of biomarkers in drug development is that numerous and conflicting
arguments can be made for placing greater emphasis on specificity than
sensitivity or vice versa. For example, one could argue that a biomarker
that yields a high number of false negatives may fail in preclinical studies to
detect problems with drugs that go on to produce toxicity in clinical studies.
This lack of sensitivity not only puts patients at risk but also may result in
the waste of future development costs. On the other hand, false positives
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0 DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
can be equally damaging by causing large numbers of potentially successful
and safe drugs to be lost during development. Thus if sensitivity is too high
at the expense of specificity, false positives will result in denying patients
access to useful therapies. This complexity can be greatly exacerbated by
the simultaneous use of multiple biomarkers in screening. For example, if
every drug must be screened using 50 safety biomarkers, and if each bio-
marker has a false positive rate of 1 percent, up to half of all useful drugs
will be wrongly eliminated during an early stage of development.
The acceptable sensitivity and specificity will vary from drug to drug
and from indication to indication. For example, the safety requirements
differ between a therapy for nasal allergy and a cancer drug. Wood stressed
that a nuanced approach is needed to answer specific questions.
A major potential use of biomarkers is to predict and monitor the toxic-
ity of a drug in a clinical trial. In these cases, an important issue is the extent
to which a negative or a positive test has predictive value. In other words, if
a person shows elevation of a biomarker and is deselected from a trial, how
likely was that person to have actually experienced a clinically significant
adverse event? Often the answer remains unknown, even when a drug is
on the market, because the only way to fully articulate the performance of
a biomarker is to measure the outcomes of the relevant population with an
adequate sample size to generate reliable probability estimates.
Assays that can make such determinations may already be on the
market with another indication or may need to be codeveloped with a drug.
An example is the drug abacavir, whose use is limited by a significant inci-
dence of adverse events. A randomized controlled trial demonstrated risk
reduction with the use of a human leucocyte antigen (HLA) region marker
for risk (HLA-B*5701), and this marker was recommended for use in a
black box on the drug’s label. This diagnostic test had been well established
because HLA markers are used for tissue typing.
With safety markers for new drugs, ethical considerations dictate ascer-
tainment of the value of a test as early as possible in drug development.
Explicit study designs are needed to answer safety questions, such as when
to stop enrolling patients who test positive or to discontinue treatment in
those with an elevated biomarker. It is critical to obtain definitive answers
about safety while keeping participants in a trial as safe as possible.
vALIDATION vS. QuALIFICATION
Currently, there is a lack of clarity regarding several terms commonly
used in the discussion of biomarkers. In particular, Woodcock urged that
standard definitions be adopted for the terms “validation” and “qualifica-
tion.” Validation, she said, should be used for analytic validation, which is
a measure of how well a test detects or quantifies an analyte under various
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OVERVIEW OF KEY ISSUES
conditions. Validation thus would require demonstration of the perfor-
mance characteristics of an assay. In contrast, qualification is a measure of
the use of a biomarker in a specific context. That context may be selecting
or deselecting people for a clinical trial, monitoring drug-induced toxicity,
or some other purpose. The amount of evidence needed to qualify a bio-
marker for a given purpose is related to the consequences of using the result
to make decisions, such as whether to pursue the development of a drug or
whether to withhold a drug from individuals in a clinical trial.
Analytic validation is necessary but generally not sufficient for a bio-
marker. It requires a stable platform and the establishment of standards
that facilitate the linking of results across laboratories. Validation also
requires study of variability among users and among laboratories. In addi-
tion, validation requires an understanding of the potential for drugs or
other conditions to interfere with results. These are not the kinds of activi-
ties that generally earn tenure for faculty members, Woodcock observed,
but they are critically important to understanding the performance of an
assay. In contrast, qualification requires context-specific measurement of
the performance of the biomarker in relation to an outcome or outcomes
of interest.
MECHANISMS vS. PATTERNS
Another important issue for the development of biomarkers is the dis-
tinction between mechanistic understanding and pattern recognition. For
some biomarkers, there may be a detailed understanding of the mechanism
that links the use of a drug to the elevation of a biomarker and thence to
the development of clinical toxicity. In other cases, a drug may produce an
effect pattern—such as a pattern of gene activity on a microarray—but the
mechanism linking the use of the drug to the change in the array and thence
to an adverse clinical effect is either unknown or poorly understood. In
these cases, decisions may have to be made on the basis of pattern recogni-
tion without a clear understanding of the mechanistic link.
When a mechanism is unknown, considerable work is required to
define the level of specificity needed to influence decisions. Drug developers
may not know what preclinical signals of toxicity to look for until clini-
cal toxicity has been observed late in drug development or even in clinical
use. For example, many kinase inhibitors now used clinically in oncology
produce cardiac toxicity, perhaps because they inhibit a specific kinase
in the heart. Without knowing whether that is indeed the mechanism or
which specific cardiac kinase is responsible, however, mechanism-based bio-
markers cannot be used to screen for this toxicity in preclinical studies. If
the relevant kinase were discovered, a biomarker assay for that mechanism
would enable rapid screening of drugs for toxicity. Therefore, understand-
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
ing of the mechanisms of toxicity offers the best chance of both developing
safer drugs lacking that toxicity and defining useful biomarkers to detect
toxicity early in drug development, while purely empirical assessment of
biomarkers requires much larger samples with greater uncertainty.
An understanding of mechanism also can be critical in gauging the rele-
vance of animal findings to humans. Many drugs are lost from development
because of toxicity findings in animals that are seen infrequently or not at
all in humans. Because the mechanism often is not understood, however, it
is difficult to predict whether the same toxicity will occur in humans since
there is no way to determine, other than by empirical observation in large
numbers, whether the same systems are at play in human biology.
REGuLATORy APPROvAL OF bIOMARkERS
Biomarkers being developed for commercial uses have several paths
toward regulatory approval, each of which requires a different level of
evidentiary data. For novel diagnostics, a premarket approval (PMA)
application must be submitted, although the FDA can assign a “de novo
classification” to a diagnostic test that streamlines the approval process.
Other biomarkers used as in vitro diagnostics reach the market through
a 510(k) application, which demonstrates that a product is “substantially
equivalent” to some previous device. An important distinction between
these mechanisms is that a PMA application must include data showing
that the device is safe and effective, whereas a 510(k) application need
only include data supporting the performance standards and validity of the
device’s intended use. A third category of biomarkers reach the market as
laboratory-developed tests that are not submitted to the FDA for approval
but are marketed by laboratories overseen by the Clinical Laboratory
Improvement Amendments (CLIA) program. Most commercially available
genetic tests fall into this category.
If a biomarker or panel of markers is to be used to justify regulatory
decision making, the assay used to measure that marker(s) must demon-
strate validity and clinical utility. According to the FDA’s pharmacogenomic
guidance document (FDA, 2005, p. 4), a valid biomarker is “a biomarker
that is measured in an analytical test system with well-established per-
formance characteristics and for which there is an established scientific
framework or body of evidence that elucidates the physiologic, toxicologic,
pharmacologic, or clinical significance of test results.”
For in vitro diagnostics requiring a PMA, clinical utility must be dem-
onstrated along with validity. Clinical utility could be demonstrated, for
example, by adequate detection of an analyte if a clinical link is well-
established in the literature. It also could be established through other
means, such as the analysis of stored specimens. Again, the burden of proof
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OVERVIEW OF KEY ISSUES
is proportional to the risk; thus, for example, prognostic claims for a test
in the absence of a specific critical decision directly linked to the test result
have less of a burden than other claims.
REGuLATION OF SINGLE bIOMARkERS vS.
PANELS OF bIOMARkERS
Marketing standards are the same whether a diagnostic is a single assay,
a set of assays, or a panel of biomarkers. For example, in vitro diagnostic
multivariate index assays (IVDMIAs) use the results from multiple analytes
to create an “index,” “score,” or other measure. The method used to derive
a score is often algorithmic and not clinically transparent. This is typical of
several new technologies, such as the use of genomic or proteomic screens
to produce a result.
The FDA has proposed a regulatory framework for IVDMIAs that
involves submission to and review by the agency. Technical issues are often
significant for an IVDMIA because of decisions about which analytes to
include, how to weight those analytes, what cutoff values to use, how to
handle changes to a test once it has been developed, and what quality con-
trol methods to apply. The FDA proposal has been controversial because
of the conflict between the need for FDA review and the rapid evolution
of the industry.
Multiplexed assays raise issues of effectiveness in addition to safety. For
example, the National Cancer Institute is planning a prospective random-
ized trial for treatment or nontreatment of early-stage cancer based on a
gene expression panel. In such cases, efficacy must be definitively tested in
the intended population, and several trial designs for this purpose have been
proposed in the literature.
MEASuRES OF SuCCESS
A general issue in the use of safety biomarkers is how success should be
defined. In the broadest possible terms, success is measured by improvement
in the clinical safety of drugs being developed. As there is no way of prevent-
ing every drug that proves to have a toxic effect from proceeding into clinical
trials, however, definitions and measures of safety must be established.
An unintended consequence of biomarker development may be a
decrease in the number of available drugs. Once a biomarker has been
developed and marketed, it may inhibit the development of drugs if it
generates a positive signal that indicates potential future problems. Many
companies would hesitate to proceed with the development of such a bio-
marker, even if there were a poor correlation between the biomarker and
toxicity. One way to help establish definitions of success would be to look
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
back at drugs that have shown toxicity and identify which biomarkers were
elevated in preclinical models. Such an approach would require that compa-
nies share compounds for study after clinical development or marketing has
ended. This retrospective approach would be valuable as there is substantial
knowledge of actual clinical experience with such drugs. In contrast, when
elevation of a biomarker results in a company’s preemptive termination of
development, there is limited evidence to evaluate.
Much of the publicity regarding drug safety has focused on the detec-
tion of events that are rare, such as acute hepatic failure, which recently
was a cause for concern with the drug troglitazone. But a bigger problem,
according to Wood, is the drug that produces an increased incidence of a
frequent event, such as the Cox-2 inhibitors, which caused an increase in
myocardial infarctions. A substantial increase in the rate of myocardial
infarction with a drug could produce hundreds of thousands of cases, yet
it could be difficult to detect the problem in preclinical work, especially if
a mechanistic hypothesis were not available. In addition, the postmarket
reporting system is ill qualified to detect an increased frequency of such
events that are common in the background population.
The challenge, Wood concluded, is to develop safety markers that are
reliable and validated across drugs and across companies, both prospec-
tively and retrospectively. Regardless of whether the mechanism of action
is known or unknown, it is necessary to develop systematic methods for
exploring the biological and clinical implications. Thus, improved under-
standing of biomarkers must be coupled with improved epidemiological
surveillance methods and randomized trials, when needed to elucidate
modest differential effects of a drug on common outcomes. Meeting these
needs will allow for the development of increasing numbers of drugs that
are safer and less expensive to bring to market.
AN EXAMPLE: bIOMARkERS FOR TOXICITy
OF PSyCHIATRIC DRuGS
Thomas Insel of the National Institute of Mental Health discussed the
use of biomarkers in addressing a major problem in the United States, as
well as globally—mental illness (see Box 2-1). Responses to both drugs
and other types of therapy used to treat mental illness vary greatly. Today,
there is no way to determine, a priori, which patients will respond well to
which treatments or will experience adverse side effects with medication.
The hope is that biomarkers will provide guidance for interventions at all
stages of a mental illness. Biomarkers may even make it possible to predict
future problems arising from mental illnesses such as schizophrenia and to
use medications preemptively.
A major emphasis in recent years has been pharmacogenomics—the
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OVERVIEW OF KEY ISSUES
BOX 2-1
The Toll of Mental Illness
Mental illness is the leading cause of medical disability for people between the
ages of 15 and 44. Mental illness is often chronic, can start early in life, is highly
prevalent, and may be severely disabling.
More than 30,000 suicides occur each year in the United States. By com-
parison, only three forms of cancer kill more than 30,000 people per year, and
homicides and AIDS kill 18,000 and 20,000 people, respectively. Life expectancy
for people with major mental illnesses is only 56 years, more than a quarter cen-
tury less than the average. Most of this excess mortality is not due to suicide, but
to general medical disorders that are secondary to the mental illness, such as
pulmonary and liver disease. According to one estimate, for example, 44 percent
of all cigarettes are smoked by people with mental illness.
Although medications are widely used to treat mental illness—more than
200 million prescriptions per year are written for antidepressants, more than
for any other class of drugs—currently available drug therapies are much less
effective than desired. The total direct and indirect costs of mental illness in the
United States are estimated at more than $300 billion, or more than $1,000 per
American, yet only about $5 per American is spent on efforts to understand the
causes, treatment, and potential preventive measures for these conditions. If
these heterogeneous problems could be better understood and classified using
biomarkers, substantial impact on mortality and morbidity in the U.S. population
might be realized.
SOURCE: Insel, 2008. Data: WHO, 2002.
use of high-throughput resequencing to associate particular genetic vari-
ants with responses to medications. For example, variants in a protein that
transports compounds across the blood–brain barrier can influence whether
a medicine will be effective. Similarly, variants in neurotransmitter recep-
tors can predict some of the variation in response. Thus far, however, the
observed effects of genetic variants have been relatively small. In addition,
the predictive power of genomics is limited by the heterogeneity of the
disorders being treated and by individual variations in choice of treatment,
response, toxicity, and adherence to a therapeutic regime.
A key problem has been predicting adverse effects in patients treated
with psychiatric drugs. In a study involving 1,742 patients, 120 developed
suicidal ideation while receiving antidepressants. Variants in two receptor
genes were associated with increased thoughts of suicide, but these findings
need to be replicated and extended.
While an individual marker may be informative, a combination of
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DEVELOPMENT OF BIOMARKERS FOR DRUG SAFETY
several markers related to different parts of a pathway could be far more
useful. Some of these markers may not be genetic—they may be “down-
stream markers” such as protein or metabolite levels in cells or the blood,
or imaging of active brain regions. For example, imaging of a region of
the brain known as “area 25” has revealed that it is overly active before
treatment for depression and less active after treatment. This is the case
whether the treatment consists of medication, cognitive-behavioral therapy,
or even placebo. Conversely, in those who do not respond to an interven-
tion, activity in this area does not decrease. This decrease in activity in
area 25 thus appears to be necessary, and possibly sufficient, for the anti-
depressant response. Perhaps by combining a better understanding of brain
circuitry from imaging with genetic and proteomic data, a panel of diverse
biomarkers could be developed that would predict responses.
NIH supports research to discover potential biomarkers using a variety
of approaches. The development and use of biomarkers can contribute to
what Insel called the 3D pathway, which stands for discovery, development,
and dissemination. Once potential indicators of clinical response or toxicity
have been identified, these predictors need to be studied through prospec-
tive development studies. Finally, predictors need to be cost-effective so that
they will be adopted and change the standard of care. Too often, powerful
evidence-based interventions are neglected in medical practice because they
either are not reimbursed or are not well understood.
Insel noted that, while biomarkers could have an enormous impact on
the prevention, diagnosis, and treatment of mental illness, their benefits
and costs need to be carefully weighed. The emphasis today is on making
health care more efficient and less expensive, not more high-tech and more
expensive.
REFERENCES
FDA (Food and Drug Administration). 2004. Innovation or stagnation: Challenges and op-
portunity on the critical path to new medical products. http://www.fda.gov/oc/initiatives/
criticalpath/whitepaper.html (accessed October 17, 2008).
FDA. 2005. Guidance for industry: Pharmacogenomic data submissions. http://www.fda.
gov/downloads/RegulatoryInformation/Guidances/UCM126957.pdf (accessed October
17, 2008).
Frantz, S. 2004. FDA publishes analysis of the pipeline problem. Nature Reviews Drug
Discovery 3:379.
Insel, T. 2008. Biomarkers for psychiatric drug toxicity. Speaker presentation at the Institute
of Medicine Workshop on Assessing and Accelerating Development of Biomarkers for
Drug Safety, October 24, Washington, DC.
WHO (World Health Organization). 2002. The world health report 00: Reducing risks,
promoting healthy life. Geneva, Switzerland: WHO.
