Guidance for IndustryPopulation PharmacokineticsU.S. Department of Health and Human ServicesFood and Drug AdministrationCenter for Drug Evaluation and Research (CDER)Center for Biologics Evaluation and Research (CBER)February 1999CP 1
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`Guidance for IndustryPopulation PharmacokineticsAdditional copies are available from:Office of Training and CommunicationsDivision of Communications ManagementDrug Information Branch, HFD-2105600 Fishers LaneRockville, MD 20857(Tel) 301-827-4573http://www.fda.gov/cder/guidance/index.htmorOffice of Communication,Training, and Manufacturers Assistance (HFM-40)Center for Biologics Evaluation and Research (CBER)1401 Rockville Pike, Rockville, MD 20852-1448http://www.fda.gov/cber/guidelines.htm(Fax) 888-CBERFAX or 301-827-3844(Voice Information) 800-835-4709 or 301-827-1800U.S. Department of Health and Human ServicesFood and Drug AdministrationCenter for Drug Evaluation and Research (CDER)Center for Biologics Evaluation and Research (CBER)February 1999CP 1
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`TABLE OF CONTENTSTABLE OF CONTENTSI.INTRODUCTION..............................................................................................................1II.BACKGROUND.................................................................................................................2III.POPULATION PK ANALYSIS..........................................................................................3A.The Two-Stage Approach...................................................................................................................4B.The Nonlinear Mixed-Effects Modeling Approach..........................................................................4IV.WHEN TO USE THE POPULATION PK APPROACH...................................................5V.STUDY DESIGN AND EXECUTION...............................................................................6A.Sampling Designs..................................................................................................................................6B.Importance of Sampling Individuals on More Than One Occasion...............................................8C.Simulation..............................................................................................................................................9D.Study Protocol......................................................................................................................................9E.Study Execution..................................................................................................................................11VI.ASSAY..............................................................................................................................11VII.DATA HANDLING......................................................................................................12A.Data Assembly and Editing...............................................................................................................12B.Handling Missing Data......................................................................................................................12C.Outliers................................................................................................................................................13D.Data Type............................................................................................................................................13E.Data Integrity and Computer Software...........................................................................................14VIII.DATA ANALYSIS........................................................................................................14A.Exploratory Data Analysis................................................................................................................14B.Population PK Model Development.................................................................................................15C.Model Validation................................................................................................................................15
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`iiIX.POPULATION PK STUDY REPORT.............................................................................19A.Summary.............................................................................................................................................19B.Introduction........................................................................................................................................19C.Objectives, Hypotheses, and Assumptions......................................................................................19D.Materials and Methods......................................................................................................................19E.Results..................................................................................................................................................20F.Discussion............................................................................................................................................20G.Application of Results........................................................................................................................20H.Appendix..............................................................................................................................................21I.Electronic Files....................................................................................................................................21X.LABELING......................................................................................................................21XI.USING POPULATION PK STUDIES AND ANALYSIS IN DRUG DEVELOPMENTAND SUBMISSIONS..............................................................................................................22REFERENCES........................................................................................................................24GLOSSARY..............................................................................................................................29
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`1GUIDANCE FOR INDUSTRY1Population PharmacokineticsI.INTRODUCTIONThis guidance makes recommendations on the use of population pharmacokinetics in the drugdevelopment process to help identify differences in drug safety and efficacy among populationsubgroups. It summarizes scientific and regulatory issues that should be addressed usingpopulation pharmacokinetics. The guidance discusses when to perform a populationpharmacokinetic study and/or analysis; how to design and execute a population pharmacokineticstudy; how to handle and analyze population pharmacokinetic data; what model validationmethods are available; and how to provide appropriate documentation for populationpharmacokinetic reports intended for submission to the FDA. Although the information in thisguidance for industry focuses on population pharmacokinetics, the principles discussed here areequally applicable to population pharmacodynamic and toxicokinetic studies.2Because the analysis of drug safety and efficacy among population subgroups is a rapidly evolvingarea of drug development and regulation, frequent communication between the sponsor and theFDA review staff is encouraged throughout the drug development process.Pharmaceutical industry scientists and the FDA have long been interested in the use of populationpharmacokinetics/pharmacodynamics in the analysis of drug safety and efficacy among populationsubgroups (1). Reference is made to this subject in other FDA guidance documents, includingGeneral Considerations for the Clinical Evaluation of Drugs (FDA 77-3040) and in InternationalConference on Harmonisation (ICH) guidances, including E4 Dose-Response Information toSupport Drug Registration, and E7 Studies in Support of Special Populations: Geriatrics.3 These guidance documents support the use of special data collection and analysis methodologies,such as the population pharmacokinetic approach (population PK approach), as part of the 1 This guidance has been prepared by the Population Pharmacokinetic Working Group of the ClinicalPharmacology Section of the Medical Policy Coordinating Committee in the Center for Drug Evaluation and Research(CDER) in cooperation with the Center for Biologics Evaluation and Research (CBER) at the Food and DrugAdministration. This guidance document represents the Agency's current thinking on population pharmacokinetics indrug evaluation. It does not create or confer any rights for or on any person and does not operate to bind FDA or thepublic. An alternative approach may be used if such approach satisfies the requirements of the applicable statute,regulations, or both.2 A separate guidance on pharmacokinetic and pharmacodynamic modeling is in preparation.3 A guidance for industry on general considerations for pediatric pharmacokinetic studies is in preparation.
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`2evaluation of pharmacokinetics in drug development.II.BACKGROUNDPopulation pharmacokinetics is the study of the sources and correlates of variability in drugconcentrations among individuals who are the target patient population receiving clinicallyrelevant doses of a drug of interest (2). Certain patient demographical, pathophysiological, andtherapeutical features, such as body weight, excretory and metabolic functions, and the presenceof other therapies, can regularly alter dose-concentration relationships. For example, steady-stateconcentrations of drugs eliminated mostly by the kidney are usually greater in patients sufferingfrom renal failure than they are in patients with normal renal function who receive the same drugdosage. Population pharmacokinetics seeks to identify the measurable pathophysiologic factorsthat cause changes in the dose-concentration relationship and the extent of these changes so that,if such changes are associated with clinically significant shifts in the therapeutic index, dosage canbe appropriately modified.Using the population PK approach in drug development offers the possibility of gaining integratedinformation on pharmacokinetics, not only from relatively sparse data obtained from studysubjects, but also from relatively dense data or a combination of sparse and dense data. Thepopulation PK approach allows the analysis of data from a variety of unbalanced designs as wellas from studies that are normally excluded because they do not lend themselves to the usual formsof pharmacokinetic analysis, such as concentration data obtained from pediatric and elderlypatients, or data obtained during the evaluation of the relationships between dose or concentrationand efficacy or safety.The subjects of traditional pharmacokinetic studies are usually healthy volunteers or highlyselected patients, and the average behavior of a group (i.e., the mean plasma concentration-timeprofile) has been the main focus of interest. Interindividual variability in pharmacokinetics hasbeen viewed by many as a factor that needs to be minimized, often through complex study designsand control schemes, or through restrictive inclusion/exclusion criteria. In fact, the informationon the variability that will occur during clinical use is critical, and it is obscured by theserestrictions. Moreover, focusing on a single variable (e.g., renal function) in a traditionalpharmacokinetic study makes it difficult to study interactions among variables.In contrast to traditional pharmacokinetic evaluation, the population PK approach encompassessome or all of the following features (3):
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`• The collection of relevant pharmacokinetic information in patients who are representative ofthe target population to be treated with the drug. • The identification and measurement of variability during drug development and evaluation. • The explanation of variability by identifying factors of demographic, pathophysiological,environmental, or concomitant drug-related origin that may influence the pharmacokineticbehavior of a drug.
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`• The quantitative estimation of the magnitude of the unexplained variability in the patientpopulation.The magnitude of the unexplained (random) variability is important because the efficacy andsafety of a drug may decrease as unexplainable variability increases. In addition to interindividualvariability, the degree to which steady-state drug concentrations in individuals typically vary abouttheir long-term average is also important. Concentrations may vary due to inexplicable day-to-day or week-to-week kinetic variability and/or due to errors in concentration measurements. Estimates of this kind of variability (residual intrasubject, interoccasion variability) are importantfor therapeutic drug monitoring. Knowledge of the relationship among concentration, response,and physiology is essential to the design of dosing strategies for rational therapeutics that may notnecessarily require therapeutic drug monitoring.Defining the optimum dosing strategy for a population, subgroup, or individual patient requiresresolution of the variability issues discussed above. Recognition of the importance of developingoptimum dosing strategies has led to a surge in the use of the population PK approach in newdrug development and the regulatory process. A recent survey of 206 new drug applications andsupplements reviewed by the Office of Clinical Pharmacology and Biopharmaceutics of the FDAin fiscal years 1995 and 1996 showed that almost one-quarter (i.e., 47) of the submissionscontained population PK and/or pharmacodynamic reports. Because of early integration ofpopulation PK studies with clinical studies, the population PK approach provided useful safety,efficacy, and dosage optimization information for the drug label in 83 percent of the 47submissions. In the other 17 percent of the 47 applications, the population PK approach providedresults that were in agreement with previous pharmacokinetic findings although it did not yieldmodifications in labeling (4). Population pharmacokinetics can be useful in the drug developmentprocess and should be considered where appropriate.III.POPULATION PK ANALYSISThe framework for a more formal definition of population pharmacokinetics can be found in thepopulation model of population analysis. The population model defines at least two levels ofhierarchy. At the first level, pharmacokinetic observations in an individual (such as concentrationsof drug species in biological fluids) are viewed as arising from an individual probability model,whose mean is given by a pharmacokinetic model (e.g., a biexponential model) quantified byindividual-specific parameters, which may vary according to the value of individual-specific time-varying covariates. The variance of individual pharmacokinetic observations (intrasubjectvariance) is also modeled using additional individual-specific pharmacokinetic parameters. Thepopulation model employs certain inferential approaches, which focus on providing estimates ofsome or all of the components of variability, along with estimates of the mean parameters. At thesecond level, the individual parameters are regarded as random variables and the probabilitydistribution of these (often the mean and variance, i.e., intersubject variance) is modeled as afunction of individual-specific covariates. These models, their parameter values, and the use ofstudy designs and data analysis methods designed to elucidate population pharmacokinetic modelsand their parameter values, are what is meant by population pharmacokinetics.
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`4There are two common methods for obtaining estimates of the fixed-effect (mean) and of thevariability: the two-stage approach and the nonlinear mixed-effects modeling approach. Thetwo-stage approach involves multiple measurements on each subject (the data-rich situation),which will be described briefly below. The nonlinear mixed-effects modeling approach, which canbe used in situations where extensive measurements will not be made on all or any of the subjects(data-sparse situation), will be the main focus of this guidance.4A.The Two-Stage ApproachThe traditional method of pharmacokinetic data analysis uses a two-stage approach. Thefirst stage of this approach involves the estimation of pharmacokinetic parameters throughnonlinear regression using an individual's dense concentration-time data (data-richsituation). Individual parameter estimates obtained during the first stage serve as inputdata for the second-stage calculation of descriptive summary statistics on the sample,typically, mean parameter estimates, variance, and covariance of the individual parameterestimates. Analysis of dependencies between parameters and covariates using classicalstatistical approaches (linear stepwise regression, covariance analysis, cluster analysis) canbe included in the second stage. The two-stage approach, when applicable, can yieldadequate estimates of population characteristics. Mean estimates of parameters areusually unbiased, but the random effects (variance and covariance) are likely to beoverestimated in all realistic situations (5-8). Refinements have been proposed (e.g.,global two-stage approach) to improve the two-stage approach through bias correction forthe random effects covariance and differential weighting of individual data according tothe data's quality and quantity (8-10). The two-stage approach has been applied in the new drug development and evaluationprocess for more than 20 years, and because it is described elsewhere, it will not becomprehensively discussed in this document.B.The Nonlinear Mixed-Effects Modeling ApproachWhen properly performed, population PK studies in patients combined with suitablemathematical/statistical analysis, for example, using nonlinear mixed-effects modeling, is avalid, and on some occasions, preferred alternative to extensive studies. In sparse datasituations, where the traditional two-stage approach is not applicable because estimates ofindividual parameters are, a priori, out of reach, a single-stage approach, such as nonlinearmixed-effects modeling, should be used.In the context of drug evaluation, the nonlinear mixed-effects modeling approachdeveloped from the recognition that, if pharmacokinetics and pharmacodynamics are to beinvestigated in patients, pragmatic considerations dictate that data should be collectedunder less stringent and restrictive design conditions. This approach considers the 4 Other approaches, such as the naive averaged-data approach, which provides mean populationpharmacokinetic parameter estimates without variability estimates, are available for use, but will not be discussed.
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`5population study sample, rather than the individual, as a unit of analysis for the estimationof the distribution of parameters and their relationships with covariates within thepopulation. The approach uses individual pharmacokinetic data of the observational(experimental) type, which may be sparse, unbalanced, and fragmentary, in addition to, orinstead of, conventional pharmacokinetic data from traditional pharmacokinetic studiescharacterized by rigid and extensive sampling design (dense data situation). Analysisaccording to the nonlinear mixed-effects model (11) provides estimates of populationcharacteristics that define the population distribution of the pharmacokinetic (and/orpharmacodynamic) parameter (12).In the mixed-effects modeling context, the collection of population characteristics iscomposed of population mean values (derived from fixed-effects parameters) and theirvariability within the population (generally the variance-covariance values derived fromrandom-effects parameters). A nonlinear mixed-effects modeling approach to thepopulation analysis of pharmacokinetic data, therefore, consists of estimating directly theparameters of the population from the full set of individual concentration values. Theindividuality of each subject is maintained and accounted for, even when data are sparse. The mixed-effects modeling approach is discussed in more detail and is referred to belowas the population PK approach.IV.WHEN TO USE THE POPULATION PK APPROACHIn drug development, use of the population PK approach can help increase understanding of thequantitative relationships among drug input patterns, patient characteristics, and drug disposition(12). This approach is helpful when wishing to identify factors that affect drug behavior, orexplain variability in a target population. The nonlinear mixed-effects modeling approach isespecially helpful in certain adaptive study designs, such as dose-ranging studies (e.g., so calledtitration, or effect controlled designs).Population modeling is most likely to add value when a reasonable a priori expectation exists thatintersubject kinetic variation may warrant altered dosing for some subgroups in the targetpopulation. Likely circumstances would include (1) when the population for which the drug isintended is quite heterogeneous and (2) when the target concentration window is believed to berelatively narrow.The population PK approach can be used to estimate population parameters of a response surfacemodel in phase 1 and late phase 2b of clinical drug development, where information is gathered onhow the drug will be used in subsequent stages of drug development (12). The population PKapproach can increase the efficiency and specificity of drug development by suggesting moreinformative designs and analyses of experiments. In phase 1 and, perhaps, much of phase 2b,where patients are sampled extensively, complex methods of data analysis may not be needed. Two-stage methods can be used to analyze the data, and standard regression methods can be usedto model dependence of parameters on covariates. Alternatively, data from individual studies inphases 1 and 2b can also be pooled and analyzed using the nonlinear mixed-effects modelingapproach.
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`6The population PK approach can also be used in early phase 2a and phase 3 of drug developmentto gain information on drug safety (efficacy) and to gather additional information on drugpharmacokinetics in special populations, such as the elderly (12-14). This approach can also beuseful in postmarketing surveillance (phase 4) studies. Studies performed during phases 3 and 4of clinical drug development lend themselves to the use of a full population pharmacokineticsampling study design (few blood samples drawn from several subjects at various time points (Seesection V). This sampling design can provide important information during new drug evaluation,regulatory decision making, and drug labeling.V.STUDY DESIGN AND EXECUTIONThe population PK approach is useful for looking at the influences of physiological as well aspathophysiological conditions on parameters of a model with a well-established structure. Thequalitative aspects of the model should be well known before embarking on a population PKstudy. When a population PK study is proposed, certain preliminary pharmacokinetic informationand the drug's major elimination pathways in humans already should be known. Preliminarystudies should establish the basic pharmacokinetic model of the drug because the sparse datacollected during population PK studies may not provide adequate information for discriminatingamong pharmacokinetic models. In addition, a sensitive and specific assay (see section IX)capable of measuring the parent drug and all metabolites of clinical relevance should be availablebefore a population PK study is undertaken. When properly performed, population PK studiescombined with suitable mathematical/statistical analysis can be a valid alternative to extensivestudies.Because it will determine the study design, the objective of a population PK study should bedefined clearly from the outset. When designing a population PK study, practical designlimitations, such as sampling times, number of samples per subject, and number of subjects,should be considered. Obtaining preliminary information on variability from pilot studies makes itpossible through simulation (see section C, below) to anticipate certain fatal study designs, and torecognize informative ones. Optimizing the sampling design becomes particularly important whensevere limitations exist on the number of subjects and/or samples per subject (e.g., in pediatricpatients or the elderly) (15). Use of informative designs for population PK studies is encouraged(15-20). Such designs should include enough patients in important subgroups to ensure accurateand precise parameter estimation and the detection of any subgroup differences.A.Sampling DesignsIn the population pharmacokinetics context, three broad approaches (with increasinginformation content) exist for obtaining information about pharmacokinetic variability: (1)the single-trough sampling design, (2) the multiple-trough sampling design, and (3) the fullpopulation PK sampling design.1. Single-Trough Sampling Design
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`7In the single-trough sampling design, a single blood sample is obtained from eachpatient at, or close to, the trough of drug concentrations, shortly before the nextdose (21), and a frequency distribution of plasma or serum levels in the sample ofpatients is calculated. Assuming that (1) the sample size is large, (2) the assay andsampling errors are small, and (3) the dosing regimen and sampling times areidentical for all patients, a histogram of the trough screen will give a fairly accuratepicture of the variability in trough concentrations in the target population. If thethree conditions are not met, a histogram will not represent strict pharmacokineticvariability because the data will include other sources of random fluctuation thatsignificantly contribute to the observed spread (22). When related to therapeuticoutcome and occurrence of side effects, such histograms can provide informationabout the optimal concentration range of a given drug.The relationship between patient characteristics and trough levels can be exploredusing simple statistical procedures, such as multiple linear regression. Althoughsimple, the trough (pharmacokinetic) screen can yield information about apparentclearance, but not about other parameters of interest (e.g., apparent volume ofdistribution, half-life). Components of variability — interindividual and residualvariability — cannot be separated. This method will identify, qualitatively,pharmacokinetically relevant covariates and their differences amongsubpopulations.When implementing single-trough sampling, the difficulty of getting patients andphysicians to adhere to the sampling strategy should be kept in mind. Compliancewith at least the last two doses before trough level measurement should besufficient for this type of study, but the drug should be dosed to steady state. Because of possible uncertainties in compliance and sample collection times, themethod can be reasonably applied only to drugs dosed at intervals less than orequal to one elimination half-life, unless the timing and level of the dose can beensured, as in inpatient studies (23). Large numbers of subjects would be neededfor this type of study because the data would be noisy.With the single-trough sampling design, it is not advisable to measure peakobservations unless the drug is given intravenously or is a certain type ofsustained-release formulation. The time for achieving maximum concentrationdepends on rates of all processes of drug disposition and may vary among subjects. Thus, the simple estimation of peak levels is subject to large uncertainty. Sampling peak levels also yields information on variability of largely irrelevantkinetic processes for drugs whose effects relate to steady-state meanconcentrations, or the area under the concentration curve.The single-trough sampling design is discussed in this guidance because it is usedcommonly. Considering its limitations, however, the use of this design is notencouraged except in situations where it is absolutely necessary. When single-trough sampling is implemented, the above limitations should be kept in mind.
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`82.Multiple-Trough Sampling DesignIn the multiple-trough sampling design, two or more blood samples are obtainednear the trough of steady-state concentrations from most or all patients. Inaddition to relating blood concentration to patient characteristics, it is possiblenow to separate interindividual and residual variabilities. Since patients are studiedin greater detail in this design, the design requires fewer subjects, and therelationship of trough levels to patient characteristics can be evaluated with greaterprecision. To estimate interindividual variability in clearance, nonlinear mixed-effects modeling should be used. When using pharmacokinetic models forparameter estimation, a sensitivity analysis (24) should be done by fixing aparameter, such as absorption rate constant, to estimate other parameters and todetermine the fixed parameter value that has the least effect on the estimation ofthe remaining parameters. Many of the drawbacks of the single-trough screendesign apply here as well. Although the estimates of intersubject and residualvariability may be biased or unbiased, they will not be precise unless a largenumber of patients are studied.3. Full Population PK Sampling DesignThe full population PK sampling design is sometimes called experimentalpopulation pharmacokinetic design or full pharmacokinetic screen. When usingthis design, blood samples should be drawn from subjects at various times(typically 1 to 6 time points) following drug administration (7). The objective is toobtain, where feasible, multiple drug levels per patient at different times to describethe population PK profile. This approach permits an estimation ofpharmacokinetic parameters of the drug in the study population and an explanationof variability using the nonlinear mixed-effects modeling approach. The fullpopulation PK sampling design should be planned to explore the relationshipbetween the pharmacokinetics of a drug and demographic and pathophysiologicalfeatures of the target population (with its subgroups) for which the drug is beingdeveloped. B.Importance of Sampling Individuals on More Than One OccasionThe variance of the pharmacokinetic observations of an individual about the individual-specific pharmacokinetic model on a given occasion (i.e., the intra-individual variability)can be factored conceptually into two components: (1) variability of pharmacokineticobservations due to variability of the pharmacokinetic model from occasion to occasion(interoccasion variability) and (2) variability of pharmacokinetic observations about theindividual pharmacokinetic model appropriate for the particular occasion (noise;pharmacokinetic model misspecification). Some interoccasion variability can be explainedby interoccasion variation in individual time-varying covariates, but the unexplainedvariability represents, along with the noise, the irreducible uncertainty in predicting, andtherefore controlling, drug concentrations. Drugs with narrow therapeutic indices andlarge interoccasion variability, for example, will be very difficult to control. If a
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`9population PK study consists of pharmacokinetic observations solely from individuals whoare studied on a single occasion, the interoccasion variability will appear incorrectly in theinterindividual variability term and not in the intraindividual variability term. This couldlead to inappropriate optimism about the ability to control individual therapy within thetherapeutic range by using feedback (e.g., therapeutic drug monitoring, or simply adjustingdose according to observed drug effects). It also could lead to a fruitless search forinterindividual covariates that might explain the (spuriously inflated) interindividualvariability. It is important to avoid this situation by ensuring that at least a moderatesubset of subjects in a population PK study contribute