Guidance for Industry Exposure-Response Relationships — Study Design, Data Analysis, and Regulatory Applications

A. Population vs. Individual Exposure-Response

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.

B. Exposure-Response Study Design

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

C. Measuring Systemic Exposure

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.

1. Chemical Moieties for Measurement

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.²

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  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.