U.S. Department of Health and Human Services
Food and Drug Administration
Center for Drug Evaluation and Research (CDER)
Center for Biologics Evaluation and Research (CBER)
April 2003
CP
Guidance
for Industry
Exposure-Response
Relationships — Study Design, Data Analysis, and Regulatory
Applications
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http://www.fda.gov/cber/guidelines.htm
U.S. Department of Health and Human Services
Food and Drug Administration
Center for Drug Evaluation and Research (CDER)
Center for Biologics Evaluation and Research (CBER)
April 2003
Guidance for Industry
Exposure-Response Relationships: Study Design, Data Analysis,
and Regulatory Applications
This
guidance represents the Food and Drug Administration's (FDA's)
current thinking on this topic. It does not create or confer
any rights for or on any person and does not operate to bind FDA
or the public. An alternative approach may be used if such
approach satisfies the requirements of the applicable statutes
and regulations. If you want to discuss an alternative
approach, contact the FDA staff responsible for implementing
this guidance. If you cannot identify the appropriate FDA
staff, call the appropriate number listed on the title page of
this guidance.
This document provides recommendations for
sponsors of investigational new drugs (INDs) and applicants
submitting new drug applications (NDAs) or biologics license
applications (BLAs) on the use of exposure-response information in
the development of drugs, including therapeutic biologics. It can
be considered along with the International Conference on
Harmonisation (ICH) E4 guidance on Dose-Response Information to
Support Drug Registration and other pertinent guidances (see
Appendix A).
This guidance describes (1) the uses of
exposure-response studies in regulatory decision-making, (2) the
important considerations in exposure-response study designs to
ensure valid information, (3) the strategy for prospective
planning and data analyses in the exposure-response modeling
process, (4) the integration of assessment of exposure-response
relationships into all phases of drug development, and (5) the
format and content for reports of exposure-response studies.
This guidance is not intended to be a
comprehensive listing of all of the situations where
exposure-response relationships can play an important role, but it
does provide a range of examples of where such information may be
of value.
FDA's guidance documents, including this
guidance, do not establish legally enforceable responsibilities.
Instead, guidances describe the Agency's current thinking on a
topic and should be viewed only as recommendations, unless
specific regulatory or statutory requirements are cited. The use
of the word should in Agency guidances means that something
is suggested or recommended, but not required.
Exposure-response information is at the heart
of any determination of the safety and effectiveness of drugs.
That is, a drug can be determined to be safe and effective only
when the relationship of beneficial and adverse effects to a
defined exposure is known. There are some situations, generally
involving a very well-tolerated drug with little dose-related
toxicity, in which the drug can be used effectively and safely at
a single dose well onto the plateau part of its exposure-response
curve, with little adjustment for pharmacokinetic (PK) or other
influences in individuals. In most situations, however, for more
toxic drugs, clinical use is based on weighing the favorable and
unfavorable effects at a particular dose. Sometimes with such
drugs, the doses can be titrated to effect or tolerability. In
most cases, however, it is important to develop information on
population exposure-response relationships for favorable and
unfavorable effects, and information on how, and whether, exposure
can be adjusted for various subsets of the population.
Historically, drug developers have been
relatively successful at establishing the relationship of dose to
blood concentrations in various populations, thus providing a
basis for adjustment of dosage for PK differences among
demographic subgroups or subgroups with impaired elimination
(e.g., hepatic or renal disease), assuming systemic
concentration-response relationships are unaltered. Far less
attention has been paid to establishing the relationship between
blood concentrations and pharmacodynamic (PD) responses and
possible differences among population subsets in these
concentration-response (often called PK-PD) relationships. These
can be critical, as illustrated by the different responses to
angiotensin-converting enzyme (ACE) inhibitors in both
effectiveness and safety between Black and Caucasian populations.
For the purposes of this guidance, we are
using the broad term exposure to refer to dose (drug input
to the body) and various measures of acute or integrated drug
concentrations in plasma and other biological fluid (e.g., Cmax,
Cmin, Css, AUC). Similarly, response refers to a direct
measure of the pharmacologic effect of the drug. Response
includes a broad range of endpoints or biomarkers ranging from the
clinically remote biomarkers (e.g., receptor occupancy) to a
presumed mechanistic effect (e.g., ACE inhibition), to a potential
or accepted surrogate (e.g., effects on blood pressure,
lipids, or cardiac output), and to the full range of short-term or
long-term clinical effects related to either efficacy or safety.
This exposure-response guidance focuses on human studies, but
exposure-response information in animal pharmacology/toxicology
studies is also a highly useful component of planning the drug
development process (Peck 1994; Lesko 2000).
This section describes the potential uses of
exposure-response relationships in drug development and regulatory
decision-making. The examples are not intended to be
all-inclusive, but rather to illustrate the value of a better
understanding of exposure-response relationships. We recommend
that sponsors refer to other ICH and FDA guidances for a
discussion of the uses of exposure-response relationships (see
Appendix A).
Many drugs thought to be of potential value
in treating human disease are introduced into development based on
knowledge of in vitro receptor binding properties and identified
pharmacodynamic effects in animals. Apart from describing the
tolerability and PK of a drug in humans, Phase 1 and 2 studies can
be used to explore the relationship of exposure (whether dose or
concentration) to a response (e.g., nonclinical biomarkers,
potentially valid surrogate endpoints, or short-term clinical
effects) to (1) link animal and human findings, (2) provide
evidence that the hypothesized mechanism is affected by the drug
(proof of concept), (3) provide evidence that the effect on the
mechanism leads to a desired short-term clinical outcome (more
proof of concept), or (4) provide guidance for designing initial
clinical endpoint trials that use a plausibly useful dose range.
Both the magnitude of an effect and the time course of effect are
important to choosing dose, dosing interval, and monitoring
procedures, and even to deciding what dosage form (e.g.,
controlled-release dosage form) to develop. Exposure-response and
PK data can also define the changes in dose and dosing regimens
that account for intrinsic and extrinsic patient factors.
Apart from
their role in helping design the well-controlled studies that will
establish the effectiveness of a drug, exposure-response studies,
depending on study design and endpoints, can:
·
Represent a well-controlled clinical study, in some
cases a particularly persuasive one, contributing to substantial
evidence of effectiveness (where clinical endpoints or accepted
surrogates are studied)
·
Add to the weight of evidence supporting efficacy
where mechanism of action is well understood (e.g., when an effect
on a reasonably well-established biomarker/surrogate is used as an
endpoint)
·
Support, or in some cases provide primary evidence
for, approval of different doses, dosing regimens, or dosage
forms, or use of a drug in different populations, when
effectiveness is already well-established in other settings and
the study demonstrates a PK-PD relationship that is similar to, or
different in an interpretable way from the established setting
In general, the more critical a role that
exposure-response information is to play in the establishment of
efficacy, the more critical it is that it be derived from an
adequate and well-controlled study (see 21 CFR 314.126), whatever
endpoints are studied. Thus, we recommend that critical studies
(1) have prospectively defined hypotheses/objectives, (2) use an
appropriate control group, (3) use randomization to ensure
comparability of treatment groups and to minimize bias, (4) use
well-defined and reliable methods for assessing response
variables, and (5) use other techniques to minimize bias.
In contrast, some of the exposure-response
studies considered in this document include analyses of
nonrandomized data sets where associations between volunteer or
patient exposure patterns and outcomes are examined. These
analyses are often primarily exploratory, but along with other
clinical trial data may provide additional insights into
exposure-response relationships, particularly in situations where
volunteers or patients cannot be randomized to different
exposures, such as in comparing effects in demographic subgroups.
A dose-response study is one kind of adequate and
well-controlled trial that can provide primary clinical evidence
of effectiveness. The dose-response study is a particularly
informative design, allowing observations of benefits and risks at
different doses and therefore providing an ability to weigh the
benefits and risks when choosing doses. The dose-response study
can help ensure that excessive doses (beyond those that add to
efficacy) are not used, offering some protection against
unexpected and unrecognized dose-related toxicity. Captopril, for
example, was a generally well-tolerated drug that caused dose and
concentration-related agranulocytosis. Earlier recognition that
daily doses beyond 75-150 milligrams were not necessary, and that
renal impairment led to substantial accumulation, might have
avoided most cases of agranulocytosis.
Dose-response studies can, in some cases, be particularly
convincing and can include elements of internal consistency that,
depending on the size of the study and outcome, can allow reliance
on a single clinical efficacy study as evidence of effectiveness.
Any dose-response study includes several comparisons (e.g., each
dose vs. placebo, each dose vs. lower doses). A consistent
ordering of these responses (most persuasive when, for example,
several doses are significantly different from placebo and, in
addition, show an increasing response with dose) represents at
least internal (within-study) replication, reducing the
possibility that an apparent effect is due to chance. In
principle, being able to detect a statistically significant
difference in pairwise comparisons between doses is not necessary
if a statistically significant trend (upward slope) across doses
can be established, as described in the ICH E4 guidance on
dose-response. It may be advisable, however, if the lowest dose
tested is to be recommended, to have additional data on that dose.
In some cases, measurement of systemic exposure levels
(e.g., plasma drug concentrations) as part of dose-response
studies can provide additional useful information. Systemic
exposure data are especially useful when an assigned dose is
poorly correlated with plasma concentrations, obscuring an
existing concentration-response relationship. This can occur when
there is a large degree of interindividual variability in
pharmacokinetics or there is a nonlinear relationship between dose
and plasma drug concentrations. Blood concentrations can also be
helpful when (1) both parent drug and metabolites are active, (2)
different exposure measures (e.g., Cmax, AUC) provide different
relationships between exposure and efficacy or safety, (3) the
number of fixed doses in the dose-response studies is limited, and
(4) responses are highly variable and it is helpful to explore the
underlying causes of variability of response.
Exposure-response
information can support the primary evidence of safety and/or
efficacy. In some circumstances,
exposure-response information can provide important insights that
can allow a better understanding of the clinical trial data (e.g.,
in explaining a marginal result on the basis of knowledge of
systemic concentration-response relationships and achieved
concentrations). Ideally, in such cases the explanation would be
further tested, but in some cases this information could support
approval. Even when the clinical efficacy data are convincing,
there may be a safety concern that exposure-response data can
resolve. For example, it might be reassuring to observe that even
patients with increased plasma concentrations (e.g., metabolic
outliers or patients on other drugs in a study) do not have
increased toxicity in general or with respect to a particular
concern (e.g., QT prolongation). Exposure-response data thus can
add to the weight of evidence of an acceptable risk/benefit
relationship and support approval. The exposure-response data
might also be used to understand or support evidence of subgroup
differences suggested in clinical trials, and to establish
covariate relationships that explain, and enhance the plausibility
of, observed subgroup differences in response.
Exposure-response data using short-term biomarkers or surrogate
endpoints can sometimes make further exposure-response data from
clinical endpoint exposure-response studies unnecessary. For
example, if it can be shown that the short-term effect does not
increase past a particular dose or concentration, there may be no
reason to explore higher doses or concentrations in the clinical
trials. Similarly, short-term exposure-response studies with
biomarkers might be used to evaluate early (e.g., first dose)
responses seen in clinical trials.
Exposure-response information can sometimes be used to
support use, without further clinical data, of a drug in a new
target population by showing similar (or altered in a defined way)
concentration-response relationships for a well-understood (i.e.,
the shape of the exposure-response curve is known), short-term
clinical or pharmacodynamic endpoint. Similarly, this information
can sometimes support the safety and effectiveness of alterations
in dose or dosing interval or changes in dosage form or
formulation with defined PK effects by allowing assessment of the
consequences of the changes in concentration caused by these
alterations. In some cases, if there is a change in the mix of
parent and active metabolites from one population (e.g., pediatric
vs. adult), dosage form (e.g., because of changes in drug input
rate), or route of administration, additional exposure-response
data with short-term endpoints can support use in the new
population, the new product, or new route without further clinical
trials.
a. New target populations
A PK-PD
relationship or data from an exposure-response study can be used
to support use of a previously approved drug in a new target
patient population, such as a pediatric population, where the
clinical response is expected to be similar to the adult
population, based on a good understanding of the pathophysiology
of the disease, but there is uncertainty as to the appropriate
dose and plasma concentration. A decision tree illustrating the
use of a PK-PD relationship for bridging efficacy data in an adult
population to a pediatric population is shown in Appendix B.
Possible use of PK-PD bridging studies assessing a well-described
PD endpoint (e.g., beta-blockade, angiotensin I or II inhibition)
to allow extension of clinical trial information performed in one
region to another region is discussed in the ICH E5 guidance on
Ethnic Factors in the Acceptability of Foreign Clinical Data.
b.
Adjustment of dosages and dosing regimens in subpopulations
defined on the basis of intrinsic and extrinsic factors
Exposure-response
information linking dose, concentration, and response can support
dosage adjustments in patients where pharmacokinetic differences
are expected or observed to occur because of one or more intrinsic
(e.g., demographic, underlying or accompanying disease, genetic
polymorphism) or extrinsic (e.g., diet, smoking, drug
interactions) factors. In some cases, this is straightforward,
simply adjusting the dose to yield similar systemic exposure for
that population. In others, it is not possible to adjust the dose
to match both Cmax and AUC. Exposure-response information can
help evaluate the implications of the different PK profiles. In
some cases, exposure-response information can support an argument
that PK changes in exposure would be too small to affect response
and, therefore, that no dose or dose regimen adjustments are
appropriate.
c. New
dose regimens, dosage forms and formulations, routes of
administration, and minor product changes.
A known
exposure-response relationship can be used to (1) interpolate
previous clinical results to new dosages and dosing regimens not
well studied in clinical trials, (2) allow marketing of new dosage
forms and formulations, (3) support different routes of
administration, and (4) ensure acceptable product performance in
the presence of changes in components, composition, and method of
manufacture that lead to PK differences. Generally, these uses of
exposure-response information are based on an understanding of the
relationship between the response and concentration, and between
dose and concentration.
Exposure-response
data can sometimes be used to support a new dose or dosing
schedule (e.g., twice a day to once a day) that was not studied in
safety and efficacy clinical trials. Exposure-response
information can provide insight into the effect of the change in
concentrations achieved with these changes and whether or not this
will lead to a satisfactory therapeutic response. The new regimen
would usually be within the range of total doses studied
clinically, but in certain circumstances could be used to extend
an approved dose range without additional clinical safety and
efficacy data. For example, a once-daily dosing regimen could
produce a higher Cmax and a lower Cmin than the same dose given as
a twice-daily regimen. If exposure-response data were available,
it might be considered reasonable to increase the recommended
daily dose to maintain a similar Cmin, even without further
studies. Exposure-response data are not likely to be useful in
lieu of clinical data in supporting new dosing schedules unless
the relationship of the measured responses to relevant safety and
efficacy outcomes is well understood.
In some cases,
exposure-response data can support the approval of a new drug
delivery system (e.g., a modified-release dosage form) when the PK
profile is changed intentionally relative to an approved product,
generally an immediate-release dosage form. A known
exposure-response relationship could be used to determine the
clinical significance of the observed differences in exposure, and
to determine whether additional clinical efficacy and/or safety
data are recommended.
Exposure-response
data can also support a new formulation that is unintentionally
pharmacokinetically different from the formulation used in the
clinical trials to demonstrate safety, or efficacy and safety. In
the case of new drugs, in vitro and/or in vivo bioequivalence
testing alone is usually used to show that the performance of a
new formulation (e.g., to-be-marketed formulation) is equivalent
to that used to generate the primary efficacy and safety data. It
is possible to demonstrate differences in exposure that are real
but not clinically important, even when the 90% confidence
interval for the bioequivalence measures fall within the standard
of 80-125%. It is possible for these bioequivalence studies to
fail to meet the standard bioequivalence acceptance intervals of
80-125%. Rather than reformulating the product or repeating the
bioequivalence study, a sponsor may be able to support the view
that use of a wider confidence interval or accepting a real
difference in bioavailability or exposure would not lead to a
therapeutic difference. In other cases, where the altered
bioavailability could be of clinical consequence, adjustment of
the marketed dosage strength might be used to adjust for the PK
difference.
In the case of
biological drugs, changes in the manufacturing process often lead
to subtle unintentional changes in the product, resulting in
altered pharmacokinetics. In cases in which the change in product
can be determined not to have any pharmacologic effects (e.g., no
effect on unwanted immunogenicity), exposure-response information
may allow appropriate use of the new product. Exposure-response
data are not likely to obviate the need for clinical data when
formulation or manufacturing changes result in altered
pharmacokinetics, unless the relationships between measured
responses and relevant clinical outcomes are well understood.
Exposure-response
information could also be used to support a change in route of
administration of a drug. An established exposure-response
relationship would allow interpretation of the clinical
significance of the difference in PK related to the different
route. Such information about active metabolites could also be
important in this situation.
Depending on the purpose of the study and the
measurements made, exposure-response information can be obtained
at steady state without consideration of the impact of
fluctuations in exposure and response over time, or can be used to
examine responses at the various concentrations attained after a
single dose during the dosing interval or over the course of
treatment. Where effectiveness is immediate and is readily
measured repeatedly in the course of a dosing interval (e.g.,
analgesia, blood pressure, blood glucose), it is possible to
relate clinical response to blood concentrations over time, which
can provide critical information for choosing a dose and dosing
interval. This is standard practice with antihypertensives, for
example, where effect at the end of the dose interval and at the
time of the peak plasma concentration is routinely assessed and
where 24-hour automated BP measurements are often used.
Controlled-release decongestants have also been assessed for their
effects over the dosing interval, especially the last several
hours of the dosing interval.
Often, however, the clinical measurement is
delayed or persistent compared to plasma concentrations, resulting
in an exposure-response relationship with considerable hysteresis.
Even in this case, exposure-response relationships can be
informative. Furthermore, safety endpoints can have a
time-dependent concentration-response relationship and it could be
different from that of the desired effect.
As noted in the ICH E4 guidance for industry
on Dose-Response Information to Support Drug Registration,
dose-response information can help identify an appropriate
starting dose and determine the best way (how often and by how
much) to adjust dosage for a particular patient. If the time
course of response and the exposure-response relationship over
time is also assessed, time-related effects on drug action (e.g.,
induction, tolerance, and chronopharmacologic effects) can be
detected. In addition, testing for concentration-response
relationships within a single dosing interval for favorable and
adverse events can guide the choice of dosing interval and dose
and suggest benefits of controlled-release dosage forms. The
information on the effects of dose, concentration, and response
can be used to optimize trial design and product labeling.
Although dose
is the measurement of drug exposure most often used in clinical
trials, it is plasma concentration measurements that are more
directly related to the concentration of the drug at the target
site and thus to the effect. Relationships between concentration
and response can, of course, vary among individuals, but
concentration-response relationships in the same individual over
time are especially informative because they are not potentially
confounded by dose-selection/titration phenomena and
inter-individual PK variability.
There are two fundamentally different
approaches to examining plasma concentration-response
relationships: (1) observing the plasma concentrations attained in
patients who have been given various doses of drug and relating
the plasma concentrations to observed response; and (2) assigning
patients randomly to desired plasma concentrations, titrating dose
to achieve them, and relating the concentration to observed
response. In some cases, concentration-response relationships
obtained from these studies can provide insight over and above
that obtained through looking at the dose-response relationship.
The first kind of study (# 1 above) is the
usual or most common way of obtaining exposure-response
information, but this kind of study can be misleading unless it is
analyzed using specialized approaches (e.g., Sheiner, Hashimoto,
and Beal 1991). Even when appropriately analyzed, potential
confounding of the concentration-response relationship can occur
and an observed concentration-response relationship may not be
credible evidence of an exposure-response relationship. (See ICH
E4). For example, if it were found that patients with better
absorption, and thus higher concentrations, had greater response,
this might not be related to the higher concentrations but to
another factor causing both the greater absorption and the greater
response. Similarly, renal failure could simultaneously lead to
increased plasma concentrations and susceptibility to adverse
effects, leading to an erroneous conclusion that concentration is
related to adverse effects. Also, a study that titrated only
nonresponders to higher doses might show a lower response with
higher concentrations (i.e., a bell-shaped
concentration-response (or dose-response) curve, a result that
would not reflect the true population exposure-response
relationship). Thus, although it is useful to look in data for
such relationships, we suggest that they be subjected to further
evaluation. The potential problem of interrelated factors leading
to both an effect on pharmacokinetics and an effect on response
and therefore an erroneous concentration-response relationship
when individuals are not randomized to concentrations generally
does not occur when concentration-response relationships in the
same individual are observed over time (e.g., over a dosing
interval).
The second kind of study (# 2 above) is the
randomized, concentration-controlled trial (e.g., Sanathanan and
Peck 1991). While less common than the first kind of study, it is
a credible controlled effectiveness study. Unlike the first
approach, this approach is not affected by the potential
confounding factors noted above, such as an unrecognized
relationship between pharmacokinetics and responsiveness, or by
the random imbalance of influential factors in the way patients
are chosen to receive higher doses.
As noted above, exposure-response studies can
examine the relationships between randomly assigned dose or plasma
concentration and PD response (biomarker, surrogate, or clinical
endpoint) or examine the relationship between attained plasma
concentration and PD response. The appropriate designs depend on
the study purpose. Randomization of patients to different doses
or concentrations is an essential aspect of the design of
well-controlled studies to establish efficacy, but other designs
can also be informative or can suggest further study. The designs
of exposure-response studies discussed here thus also include
nonrandomized approaches that can assume mechanistic models for
relationships and that do not rely on randomization for making
comparisons.
Exposure-response relationships based on data
from randomized parallel studies in which each treatment group
receives a single dose level provide an estimate of the
distribution of individual responses at that dose, but do not
provide information about the distribution of individual
dose-response relationships. Administration of several dose
levels to each study participant (crossover study) can provide
information about the distribution of individual exposure-response
relationships. The individual data allow examination of the
relative steepness or flatness of an individual exposure-response
relationship and the distinctions between responders and
nonresponders. In such crossover studies, it is important to take
sequence and duration of dosing into account, as well as the
possibility of sequence and carryover effects.
The various exposure-response study designs
and their strengths and limitations have been extensively
discussed in the ICH E4 guidance on Dose Response Information
to Support Drug Registration. The statistical considerations
in designing dose-response studies are briefly considered in the
ICH E9 guidance on Statistical Principles for Clinical Trials.
In this section, important study design
issues for exposure-response analyses are emphasized and
summarized without repeating details already described in the ICH
E4 guidance. In general, the rigor of the design (e.g., whether
or not the study is adequate and well-controlled) for an
exposure-response study depends on the purpose of the study.
During the drug discovery and development stage, the
exposure-response studies can be more exploratory, because they
are intended to gather information for designing later, more
definitive studies. In addition, as emphasized in the ICH E4
guidance, it is important to examine the entire drug development
database for potentially interesting exposure-response
relationships. For example, gender differences in response can
sometimes be explained by observed gender-related PK data obtained
during trials (population PK data) or in studies obtaining blood
samples for measuring plasma concentrations in patients with
adverse effects. When an exposure-response study is designed to
support regulatory decisions by providing evidence of efficacy,
randomization to exposure (dose or concentration) is critical.
The strengths and limitations of various
exposure-response study designs are described in the ICH E4
guidance and are briefly summarized in Table I.
Table 1. Points for Consideration in Different Study Designs
from the
Exposure-Response Perspective
Study Design |
Points to Consider in Study Design and
Exposure-Response Analysis |
Crossover, fixed dose, dose response |
·
For immediate, acute, reversible responses
·
Provide both population mean and individual
exposure-response information
·
Safety information obscured by time effects,
tolerance, etc.
·
Treatment by period interactions and carryover
effects are possible; dropouts are difficult to deal with
·
Changes in baseline-comparability between
periods can be a problem |
Parallel, fixed dose, dose response |
·
For long-term, chronic responses, or responses
that are not quickly reversible
·
Provides only population mean, no individual
dose response
·
Should have a relatively large number of
subjects (1 dose per patient)
·
Gives good information on safety
|
Titration |
·
Provide population mean and individual
exposure-response curves, if appropriately analyzed
·
Confounds time and dose effects, a particular
problem for safety assessment |
Concentration-controlled, fixed dose,
parallel, or crossover |
·
Directly provides group concentration-response
curves (and individual curves, if crossover) and handles
intersubject variability in pharmacokinetics at the study
design level rather than data analysis level
·
Requires real-time assay availability |
There are many important considerations in
selecting one or more active moieties in plasma for measurement
and in choosing specific measures of systemic exposure. Some of
these considerations are summarized below.
a. Active moieties
To the extent
possible, it is important that exposure-response studies include
measurement of all active moieties (parent and active metabolites)
that contribute significantly to the effects of the drug. This is
especially important when the route of administration of a drug is
changed, as different routes of administration can result in
different proportions of parent compound and metabolites in
plasma. Similarly, hepatic or renal impairment or concomitant
drugs can alter the relative proportions of a drug and its active
metabolites in plasma.
b. Racemates and enantiomers
Many drugs are optically active and are usually administered
as the racemate. Enantiomers sometimes differ in both their
pharmacokinetic and pharmacodynamic properties. Early elucidation
of the PK and PD properties of the individual enantiomers can help
in designing a dosing regimen and in deciding whether it can be of
value to develop one of the pure enantiomers as the final drug
product. Further description on how to develop information for a
drug with one or more chiral centers is provided in an FDA Policy
Statement, Development of New Stereoisomeric Drugs.
c. Complex mixtures
Complex drug
substances can include drugs derived from animal or plant
materials and drugs derived from traditional fermentation
processes (yeast, mold, bacterium, or other microorganisms). For
some of these drug substances, identification of individual active
moieties and/or ingredients is difficult or impossible. In this
circumstance, measurement of only one or more of the major active
moieties can be used as a “marker of exposure” in understanding
exposure-response relationships and can even be used to identify
the magnitude of contribution from individual active moieties.
d. Endogenous ligand measurements
The response to a
drug is often the result of its competition with an endogenous
ligand for occupancy of a receptor. For example, a beta-blocker
exerts its effect by competing with endogenous catecholamines for
receptor sites. Taking into account endogenous catecholamine
concentrations as well as drug concentrations may help explain the
overall physiological response in patients with different
concentrations of circulating catecholamines. Biorhythms can
affect the concentrations of endogenous compounds, which can make
adjustments in daily dosing schedule important, as seen in some
treatment regimens for hypertension. Consideration of the
endogenous ligand concentration and the drug concentration in
various tissues, and of the relative affinities of the ligand to
the drug can be important to explain concentration-response
relationships.
e. Unbound drug and/or active metabolite (protein
binding)
Most standard
assays of drug concentrations in plasma measure the total
concentration, consisting of both bound and unbound drug. Renal
or hepatic diseases can alter the binding of drugs to plasma
proteins. These changes can influence the understanding of PK and
PK-PD relationships. Where feasible, studies to determine the
extent of protein binding and to understand whether this binding
is or is not concentration-dependent are important, particularly
when comparing responses in patient groups that can exhibit
different plasma protein binding (e.g., in various stages of
hepatic and renal disease). For highly protein bound drugs, PK
and PK-PD modeling based on unbound drug concentrations may be
more informative, particularly if there is significant variation
in binding among patients or in special populations of patients.
A special case of
protein binding is the development of antibodies to a drug.
Antibodies can alter the pharmacokinetics of a drug and can also
affect PK-PD relationships by neutralizing the activity of the
drug or preventing its access to the active site.
2. Exposure Variables
Pharmacokinetic
concentration time curves for a drug and/or its metabolites can be
used to identify exposure metrics such as AUC, Cmax, or Cmin.
These simple measurements of exposure ignore the time course of
exposure, in contrast to the sequential measurement of
concentration over time. The most appropriate representation of
exposure will depend on the study objectives, the study design,
and the nature of the relationship between exposure and response.
If response varies substantially with time within a dosage
interval, then the maximum information on exposure-response will
normally be retrieved by relating response to concentration within
the group and individual subjects. When a single pharmacodynamic
response is obtained once on a given sampling day, it may be more
appropriate to represent the exposure by more simplified metrics
such as AUC, Cmax, or Cmin.
a. Area under the concentration-time profiles (AUC)
The area under the
concentration-time full profile is a typical pharmacokinetic
variable used to represent the average drug concentration over a
time period. It is also a variable that can be used to compare
exposure to a drug after multiple doses to single dose exposure.
It is frequently useful to correlate long-term drug effects to
steady-state AUC, as the effects usually reflect the daily
exposure to drug following multiple dosing.
b. Peak plasma concentrations (Cmax)
Peak plasma
concentrations of a drug can be associated with a PD response,
especially adverse events. There can be large interindividual
variability in the time to peak concentration, and closely spaced
sampling times are often critical to determining the peak plasma
concentration accurately in individual patients. It is important
to have a well-designed sampling plan for estimating peak
concentrations and be able to account for expected differences in
PK profiles (e.g., in Tmax, time to Cmax) due to demographics,
disease states, and food effects, if any.
c. Trough plasma concentrations (Cmin)
During chronic
therapy, collection of multiple plasma samples over a dosing
interval is often not practical. As a substitute, a trough plasma
sample can be collected just before administration of the next
dose at scheduled study visits. Trough concentrations are often
proportional to AUC, because they do not reflect drug absorption
processes, as peak concentrations do in most cases. For many of
the drugs that act slowly relative to the rates of their
absorption, distribution, and elimination, trough concentration
and AUC can often be equally well correlated with drug effects.
d. Sparse plasma concentrations
An increasingly
common sampling practice in clinical trials is to obtain plasma
samples at randomly selected times during the study, or at
prespecified but different times, to measure drug concentration
and, in some cases, response. With only two or three samples per
subject, the usual pharmacokinetic data analysis methods will not
be able to make precise estimates of individual PK parameters. In
these circumstances, a specialized technique, population PK
analysis combined with Bayesian estimation method, can be used to
approximate population and individual PK parameters, providing an
exposure variable that is more readily correlated to response than
the sparse plasma concentrations themselves. This approach is
particularly useful when relatively complete PK information is
desired, but it is difficult or unethical to sample repeatedly
C for
example, in pediatric and geriatric populations (see the FDA
guidance for industry on Population Pharmacokinetics).
e. Plasma concentration-time profiles
In traditional PK
studies (not sparse sampling), the concentrations of active
moieties are measured over time. This allows not only calculation
of AUC but also the determination of concentration versus time
profiles over a dosing interval for each individual, as well as
the population. This approach yields relatively detailed exposure
information that can be correlated to the observed response in
individuals. The exposure-response relationship based on
concentration-time profiles can provide time-dependent information
that cannot be derived from AUC or Cmin.
Broadly speaking,
both positive (efficacy) and negative (safety) effects of a drug
can be characterized using a variety of measurements or response
endpoints. These effects include clinical outcomes (clinical
benefit or toxicity), effects on a well-established surrogate
(change in blood pressure or QT interval), and effects on a more
remote biomarker (change in ACE inhibition or bradykinin levels)
thought to be pertinent to clinical effects. All of these
measurements can be expected to show exposure-response
relationships that can guide therapy, suggest efficacy or safety,
dose and dosing intervals, or suggest a hypothesis for further
study.
In many cases,
multiple response endpoints are more informative than single
endpoints for establishing exposure-response relationships.
Specifically, less clinically persuasive endpoints (biomarkers,
surrogates) can help in choosing doses for the larger and more
difficult clinical endpoint trials and can suggest areas of
special concern. In most cases, it is important to standardize
the measurement of response endpoints across studies and between
study sites and/or laboratories.
Biological marker
(biomarker) refers to a variety of physiologic, pathologic, or
anatomic measurements that are thought to relate to some aspect of
normal or pathological biologic processes (Temple 1995; Lesko and
Atkinson 2001). These biomarkers include measurements that
suggest the etiology of, the susceptibility to, or the progress of
disease; measurements related to the mechanism of response to
treatments; and actual clinical responses to therapeutic
interventions. Biomarkers differ in their closeness to the
intended therapeutic response or clinical benefit endpoints,
including the following:
·
Biomarkers
thought to be valid surrogates for clinical benefit (e.g., blood
pressure, cholesterol, viral load)
·
Biomarkers
thought to reflect the pathologic process and be at least
candidate surrogates (e.g., brain appearance in Alzheimer’s
Disease, brain infarct size, various radiographic/isotopic
function tests)
·
Biomarkers
reflecting drug action but of uncertain relation to clinical
outcome (e.g., inhibition of ADP-dependent platelet aggregation,
ACE inhibition)
·
Biomarkers
that are still more remote from the clinical benefit endpoint
(e.g., degree of binding to a receptor or inhibition of an
agonist)
From a regulatory perspective, a biomarker is not
considered an acceptable surrogate endpoint for a determination of
efficacy of a new drug unless it has been empirically shown to
function as a valid indicator of clinical benefit (i.e., is a
valid surrogate). Theoretical justification alone does not meet
the evidentiary standards for market access. Many biomarkers
will never undergo the rigorous statistical evaluation that would
establish their value as a surrogate endpoint to determine
efficacy or safety, but they can still have use in drug
development and regulatory decision making. Changes in biomarkers
typically exhibit a time course that is different from changes in
clinical endpoints and often are more directly related to the time
course of plasma drug concentrations, possibly with a measurable
delay. For this reason, exposure-response relationships based on
biomarkers can help establish the dose range for clinical trials
intended to establish efficacy. In some cases, these
relationships can also indicate how soon titration should occur,
and can provide insight into potential adverse effects.
Biomarkers can also be useful during the drug discovery and
development stage, where they can help link preclinical and early
clinical exposure-response relationships and better establish dose
ranges for clinical testing.
Surrogate endpoints are a subset of biomarkers. A
surrogate endpoint is a laboratory measurement or physical sign
used in therapeutic trials as a substitute for a clinically
meaningful endpoint that is expected to predict the effect of the
therapy (Temple 1999). A well-validated surrogate endpoint will
predict the clinically meaningful endpoint of an intervention (Lesko
and Atkinson 2001), with consistent results in several settings.
FDA is able to rely on less well-established surrogates for
accelerated approval of drugs that provide meaningful benefit over
existing therapies for serious or life-threatening illnesses
(e.g., acquired immunodeficiency syndrome). In these cases, the
surrogates are reasonably likely to predict clinical benefit based
on epidemiologic, therapeutic, pathophysiologic, or other
scientific evidence. However, generally, in trials examining
surrogate endpoints, even where the endpoint is well correlated
with a clinical outcome, surrogates will be unable to evaluate
clinically relevant effects of the drug unrelated to the
surrogate, whether these are beneficial or adverse (Temple 1999).
Clinical benefit endpoints are variables that reflect how
a patient feels, functions, or survives. Clinical endpoints
reflect desired effects of a therapeutic intervention and are the
most credible response measurements in clinical trials.
Safety
information and adequate and well-controlled clinical studies that
establish a drug’s effectiveness are the basis for approval of new
drugs. Exposure-response data can be derived from these clinical
studies, as well as from other preclinical and clinical studies,
and provide a basis for integrated model-based analysis and
simulation (Machado et al. 2000; Sheiner and Steimer 2000).
Simulation is a way of predicting expected relationships between
exposure and response in situations where real data are sparse or
absent. There are many different types of models for the analysis
of exposure-response data (e.g., descriptive PD models (Emax model
for exposure-response relationships) or empirical models that link
a PK model (dose-concentration relationship) and a PD model
(concentration-response relationship)). Descriptive or empirical
model-based analysis does not necessarily establish causality or
provide a mechanistic understanding of a drug’s effect and would
not ordinarily be a basis for approval of a new drug.
Nevertheless, dose-response or PK-PD modeling can help in
understanding the nature of exposure-response relationships and
can be used to analyze adequate and well-controlled trials to
extract additional insights from treatment responses. Adequate
and well-controlled clinical studies that investigate several
fixed doses and/or measure systemic exposure levels, when analyzed
using scientifically reasonable causal models, can predict
exposure-response relationships for safety and/or efficacy and
provide plausible hypotheses about the effects of alternative
doses and dosage regimens not actually tested. This can suggest
ways to optimize dosage regimens and to individualize treatment in
specific patient subsets for which there are limited data.
Creating a theory or rationale to explain exposure-response
relationships through modeling and simulation allows interpolation
and extrapolation to better doses and responses in the general
population and to subpopulations defined by certain intrinsic and
extrinsic factors.
In the process of PK-PD modeling, it is
important to describe the following prospectively:
The objectives of
the modeling, the study design, and the available PK and PD data;
The assumptions of
the model that can be related to dose-response, PK, PD, and/or one
or more of the following:
·
The mechanism of the drug actions for efficacy and
adverse effects
·
Immediate or cumulative clinical effects
·
Development of tolerance or absence of tolerance
·
Drug-induced inhibition or induction of PK processes
·
Disease state progression
·
Response in a placebo group
·
Circadian variations in basal conditions
·
Influential covariates
·
Absence or presence of an effect compartment
·
Presence or absence of active metabolites and their
contribution to clinical effects
·
The PK model of absorption and disposition and the
parameters to be estimated
·
The PD model of effect and the parameters to be
estimated
·
Distribution of PK and PD measures and parameters
·
Distributions of intra- and inter-individual
variability in parameters
·
Inclusion and/or exclusion of specific patient data
The assumptions can be justified based on previous data
or from the results of the current analysis.
The answer to the question of what constitutes an
appropriate model is complex. In general, the model selected will
be based on the mechanism of action of the drug, the assumptions
made, and the intended use of the model in decision making. If
the assumptions do not lead to a mechanistic model, an empirical
model can be selected. In this case, the validation of the model
predictability becomes especially important. The available data
can also govern the types of models that can be used. The model
selection process can be a series of trial and error steps.
Different model structures or newly added or dropped components to
an existing model can be assessed by visual inspection and tested
using one of several objective criteria. New assumptions can be
added when emerging data indicates that this is appropriate. The
final selection of the model will usually be based on the simplest
model possible that has reasonable goodness of fit, and that
provides a level of predictability appropriate for its use in
decision making.
The issue of model
validation is not totally resolved. Generally, we recommend that
the predictive power of a model be dealt with during the study
design as well as in the data analysis stages and that the study
be designed to yield a predictive model. When plausible
exposure-response models are identified based on prior knowledge
of the drug before conducting an exposure-response study, the
predictive power of the final models derived from the study
results becomes a function of study design factors, such as number
of subjects and sampling plan. The predictive power can be
estimated through simulation, by considering distributions of
pharmacokinetic, pharmacodynamic, and study design variables. A
robust study design will provide accurate and precise model
parameter estimations that are insensitive to model assumptions.
During the analysis
stage of a study, models can be validated based on internal and/or
external data. The ultimate test of a model is its predictive
power and the data used to estimate predictability could come
from exposure-response studies designed for such a purpose. A
common method for estimating predictability is to split the data
set into two parts, build the model based on one set of data, and
test the predictability of the resulting model on the second set
of data. The predictability is especially important when the
model is intended to (1) provide supportive evidence for primary
efficacy studies, (2) address safety issues, or (3) support new
doses and dosing regimens in new target populations or
subpopulations defined by intrinsic and extrinsic factors or when
there is a change in dosage form and/or route of administration.
It is
advisable for the general format and content of a clinical study
report to be based on that presented in the ICH E3 guidance on the
Structure and Content of Clinical Study Reports, modified
to include measurements of exposure and response and planned or
actual modeling and simulation. It is helpful to include a
description of the assay methods used in quantifying drug
concentrations (if they are components of the exposure measure) as
well as assay performance (quality control samples), sample
chromatograms, standard curves used, where applicable, and a
description of the validity of the methodologies. The
report could also contain:
·
The response variable and all covariate information
·
An explanation of how they were obtained
·
A description of the sampling design used to collect
the PK and PD measures
·
A description of the covariates, including their
distributions and, where appropriate, the accuracy and precision
with which the responses were measured
·
Data quality control and editing procedures
·
A detailed
description of the criteria and procedures for model building and
reduction, including exploratory data analysis
The
following components of the data analysis method used in the study
would also ordinarily be described: (1) the chosen dose-response
or PK-PD model, (2) the assumptions and underlying rationale for
model components (e.g., parameterization, error models), (3) the
chosen model-fitting method, (4) a description of the treatment of
outliers and missing data, where applicable, and (5) diagrams, if
possible, of the analysis performed and representative
control/command files for each significant model building and/or
reduction step. In presenting results, complete output of
results obtained for the final dose-response, or PK-PD model, and
important intermediate steps can be included.
A complete
report would include a
comprehensive statement of the rationale for model building and
reduction procedures, interpretation of the results, impact of
protocol violations, discussion and presentation of supporting
graphs, and the ability of the model to predict performance.
It is helpful if an appendix is provided
containing the data set used in the dose-response or PK-PD
analysis, the programming codes along with the printouts of the
results of the final model, and any additional important plots.
Whether the analysis was performed as a
result of an add-on to a clinical study or as a stand-alone
exposure-response study, it is important that the original study
protocol and amendments be included in the appendix.
The FDA’s Center for Drug Evaluation and
Research (CDER) guidance for industry on Providing Regulatory
Submissions in Electronic Format
C NDAs
includes information on how to submit the exposure-response study
report in electronic format. Information on electronic
submissions to FDA’s Center for Biologics Evaluation and Research
(CBER) can be found in the guidance for industry on Providing
Regulatory Submissions to the Center for Biologics Evaluation and
Research (CBER) in Electronic Format
C
Biologics Marketing Applications (Biologics License
Application (BLA), Product License Application (PLA)/Establishment
License Application (ELA) and New Drug Application (NDA)).
FDA is still actively working on standardizing data file formats
for exposure-response and other clinical pharmacology data, and
plans to provide these standards in future versions of the
electronic guidance document. In the meantime, sponsors are
encouraged to submit both the reports and data files with BLA or
NDA submissions in electronic format. Until the details are
included in an electronic BLA or NDA guidance document, sponsors
can consult the clinical pharmacology and biopharmaceutics
reviewer or team leader on the data sets to be provided and
elements to be included in the data sets.
Lesko, L.J., M.
Rowland, C.C. Peck, T.F. Blaschke, 2000, “Optimizing
the Science of Drug Development: Opportunities for Better
Candidate Selection and Accelerated Evaluation in Humans,” J.
Clin. Pharmacol., 40:803-814.
Lesko, L.J. and
A.J. Atkinson, Jr., 2001, “Biomarkers and Surrogate Endpoints –
Use in Drug Development and Regulatory Decision Making: Criteria,
Validation, Strategies,” Ann. Rev. Pharmacol. Toxicol.,
41:347-366.
Machado, S.G., R.
Miller, C. Hu, 1999, “A Regulatory Perspective on Pharmacokinetic/Pharmacodynamic
Modelling,” Statistical Methods in Medical Research,
8(3):217-45.
Peck, C.C., W.H.
Barr, L.Z. Benet, J. Collins, R.E. Desjardins, D.E. Furst, J.G.
Harter, G. Levy, T. Ludden, J.H. Rodman, et al., 1994,
“Opportunities for Integration of Pharmacokinetics,
Pharmacodynamics, and Toxicokinetics in Rational Drug
Development,” J. Clin. Pharmacol., 34(2):111-119.
Sanathanan, L.P.
and C.C. Peck, 1991, “The Randomized Concentration-Controlled
Trial: An Evaluation of Its Sample Size Efficiency,”
Controlled Clin. Trials, 12(6):780-94.
Sheiner L.B., Y.
Hashimoto, S.L. Beal, 1991, “A Simulation Study Comparing Designs
for Dose Ranging,” Stat. Med., 10(3):303-21.
Sheiner L.B., J.L.
Steimer, 2000, “Pharmacokinetic/Pharmacodynamic Modeling in Drug
Development,” Ann. Rev. Pharmacol. Toxicol., 40: 67-95.
Sheiner L.B.,
1997, “Learning Versus Confirming in Clinical Drug Development,”
Clin. Pharmacol. Ther., 61(3):275-91.
Temple, R.J.,
1995, “A Regulatory Authority’s Opinion About Surrogate
Endpoints,” in Clinical Measurement in Drug Evaluation,
Nimmo and Tucker, Eds., Wiley & Sons.
Temple R.J., 1999,
“Are Surrogate Markers Adequate to Assess Cardiovascular Disease
Drugs?” JAMA, 282(8):790-5.
APPENDIX A: RELATED GUIDANCES
The use of exposure-response relationships is
considered in many FDA guidances for industry as well as in
various ICH guidances. These guidances can be divided into those
that provide general advice and those that provide specific
recommendations about the use of exposure-response information to
adjust a dosage regimen based on intrinsic and extrinsic factors.
The ICH Common Technical Document (ICH M4, Efficacy) suggests a
structure to organize the submission of exposure-response
information. In addition, the statistical considerations for
dose-response studies are briefly described in the ICH E9
Guidance on Statistical Principles for Clinical Trials.
A. Guidances Providing General Statements
The value of
understanding exposure-response has been recognized in numerous
domestic and international guidances. Brief abstracts of these
guidances are provided below to focus on exposure-response
relationships and the impact of intrinsic and extrinsic factors on
these relationships.
1.
Providing
Clinical Evidence of Effectiveness for Human Drugs and Biological
Products
This guidance
provides general information about the efficacy standard (section
I) and comments further on the quantity (section II) and quality
(section III) of efficacy information needed for a regulatory
determination of efficacy based on both statutory and scientific
considerations. The guidance focuses on (1) when efficacy for a
new product can be extrapolated entirely from existing efficacy
studies, (2) when one adequate and well-controlled study of a
particular condition, regimen, or dose supported by information
from other adequate and well-controlled studies may be
appropriate, and (3) when information from a single multicenter
study may be appropriate.
2.
Guideline for the
Format and Content of the Clinical and Statistical Sections of an
Application
This guidance provides a
description of the format and content of the clinical and
statistical data package required as part of a new drug
application under Title 21, Code of Federal Regulations (CFR) §
314.50. It emphasizes the importance of conducting an integrated
analysis of all clinical and preclinical exposure-response data
that forms the foundation for dose and dosing regimen
determinations and dose adjustments for subpopulations.
3.
ICH E4, Dose
Response Information to Support Drug Registration
This guidance describes the purpose of exposure-response
information and the uses of dose-response and/or
concentration-response data in choosing doses during the drug
development process. The guidance emphasizes the importance of
developing exposure-response data throughout development. It
further comments on the use of population and individual
dose-concentration, and concentration- and/or dose-response
relationships to provide dosage and administration instructions in
product labeling. The guidance notes that these instructions can
include information about both starting dosages and subsequent
titration steps based on response to the drug, as well as
information on how to adjust dose in the presence of factors that
are intrinsic (age, gender, race, organ dysfunction, body size,
differences in absorption, distribution, metabolism, and
excretion) and extrinsic (diet, concomitant medications). The
guidance emphasizes the importance of early exposure-response data
to allow efficient design of later studies and the value of
examining the entire database to assess exposure-response
relationships. The guidance further comments on strengths and
limitations of various study designs to assess exposure-response.
The guidance comments briefly on the use of models to amplify
understanding of exposure-response-relationships and, consistent
with 21 CFR 314.126, indicates that a well-controlled
dose-response study may be one type of study that supports
efficacy.
4.
ICH E5, Ethnic
Factors in the Acceptability of Foreign Clinical Data
This guidance provides descriptions of PK and PD studies
and expresses PD endpoints as safety and/or efficacy measures of
activity thought, but not documented, to be related to clinical
benefit (biomarkers), surrogate endpoints, and clinical benefit
endpoints. The guidance further defines a PD study as one that
describes the relationship between a pharmacological effect or
clinical benefit effect in relation to dose or drug
concentration. The guidance establishes a classification system
of intrinsic (genetic polymorphism, age, gender, height, weight,
lean body mass, body composition, and organ dysfunction) and
extrinsic (medical practice, diet, use of tobacco, use of alcohol,
exposure to pollution and sunshine, practices in clinical trial
design and conduct, socioeconomic status, compliance with
medication) ethnic factors that can affect safety, efficacy,
dosage, and dosage regimen determinations. The guidance provides
an additional set of factors that indicate whether a drug may be
sensitive to ethnic factors (linear PK, flat PD curve, wide
therapeutic range). It focuses on the bridging studies that may
be critical for an application in a new region based on a clinical
data package developed in another region. These bridging studies
range from those that establish similarity of exposure-response
relationship in the two regions for a well-established PD effect
(e.g., ACE inhibition or short-term blood pressure response) to a
controlled trial in the new region, preferably a dose-response
study, using the pertinent clinical endpoint.
B. Guidances Providing Specific Statements
FDA has issued
final or draft
guidances that focus on how to adjust dosages and dosing regimens
in the presence of selected intrinsic and extrinsic factors. A
general theme of these guidances is that information relating
exposure to response can be used to adjust dosages and dosing
regimens in the presence of influences on PK such as age, gender
(demographic factors), impaired organ function (intrinsic
factors), or concomitant medications and diet (extrinsic
factors). In many circumstances, where the assumption can be made
that the exposure-response relationships are not disturbed by
these factors, PK data alone can be used to guide dosages and
dosing regimens. This principle is articulated in the following
FDA guidances:
1.
ICH E7, Studies in Support of Special Populations:
Geriatrics
2.
Study and Evaluation of Gender Differences in the Clinical
Evaluation of Drugs
3.
General
Considerations for Pediatric Pharmacokinetic Studies for Drugs and Biological Products (draft)
4.
Pharmacokinetics in Patients with Impaired Renal Function:
Study Design, Data Analysis and Impact on Dosing and Labeling
5.
Pharmacokinetics in Patients with Hepatic Insufficiency:
Study Design, Data Analysis and Impact on Dosing and Labeling
(draft)
6.
In Vivo Metabolism/Drug Interactions Studies: Study
Design, Data Analysis and Recommendations for Dosing and Labeling
(draft)
7.
Population Pharmacokinetics
APPENDIX B: PEDIATRIC DECISION TREE
INTEGRATION OF PK-PD