Population 
Pharmacokinetics 
Guidance for Industry 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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) 
 
February 2022 
Clinical Pharmacology 
 
    
Population 
Pharmacokinetics 
Guidance for Industry 
 
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Food and Drug Administration 
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https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances     
 
 
 
 
 
 
 
 
 
 
 
 
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) 
 
February 2022 
Clinical Pharmacology 
 
  Contains Nonbinding Recommendations 
 
TABLE OF CONTENTS 
 
 
 
I.  INTRODUCTION ...................................................................................................... 1 
II.  BACKGROUND ........................................................................................................ 2 
III.  APPLICATION OF POPULATION PK ANALYSIS ............................................... 2 
A.  Application of Population PK Analysis in Drug Development ........................................... 3 
B.  Application of Population PK Analysis to Inform Drug Use in Labeling ............................ 5 
IV.  DATA USED FOR POPULATION PK ANALYSIS ................................................. 7 
A.  Study Subjects and Covariates ....................................................................................... 7 
B.  PK Sampling Schedule .................................................................................................. 7 
V.  DATA ANALYSIS ..................................................................................................... 8 
A.  Preliminary Examination of the Data ............................................................................. 9 
B.  Model Development ...................................................................................................... 9 
C.  Model Validation .........................................................................................................10 
D.  Simulations Based on Population PK Models .................................................................12 
VI.  LABELING BASED ON THE RESULTS OF POPULATION PK ANALYSIS ..... 14 
VII.  POPULATION PK STUDY REPORTING ............................................................. 14 
A.  Format and Content of the Population PK Study Report ................................................14 
B.  Submitting Electronic Files to the Agency ......................................................................17 
VIII.  GLOSSARY OF SELECTED TERMS .................................................................... 19 
IX.  REFERENCES......................................................................................................... 20 
i  Contains Nonbinding Recommendations 
Population Pharmacokinetics 
Guidance for Industry1 
 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) on 
this topic.  It does not establish any rights for any person and is not binding on FDA or the public.  You 
can use an alternative approach if it satisfies the requirements of the applicable statutes and regulations.  
To discuss an alternative approach, contact the FDA office responsible for this guidance as listed on the 
title page.   
 
 
 
 
I.  INTRODUCTION  
 
This guidance is intended to assist sponsors and applicants2 of new drug applications (NDAs), 
biologics license applications (BLAs), abbreviated new drug applications (ANDAs), and 
investigational new drugs (IND) applications in the application of population pharmacokinetic 
(PK) analysis.  Population PK analysis is frequently used to guide drug3 development and inform 
recommendations on therapeutic individualization (e.g., through tailored dosing) (Marshall et al. 
2015; Lee et al. 2011; Bhattaram et al. 2005).  Adequate population PK data collection and 
analyses submitted in marketing applications have in some cases alleviated the need for 
postmarketing requirements or postmarketing commitments. 
 
This guidance includes common applications of population PK analysis to inform drug 
development and drug use.  This list of applications is not meant to be comprehensive, but rather 
provides illustrative examples.  This guidance also includes the FDAs current thinking on the 
data and model submissions to support regulatory decisions, recommendations on how to 
incorporate information from population PK analyses in labeling, and the general expectations 
regarding the format and content for population PK reports submitted to the Agency.  
 
The contents of this document do not have the force and effect of law and are not meant to bind 
the public in any way, unless specifically incorporated into a contract.  This document is 
intended only to provide clarity to the public regarding existing requirements under the law.  
FDA guidance documents, including this guidance, should be viewed only as recommendations, 
 
1 This guidance has been prepared by Office of Clinical Pharmacology in the Center for Drug Evaluation and 
Research (CDER) and the Center for Biologics Evaluation and Research (CBER) at the Food and Drug 
Administration. 
 
2 Hereafter, the term sponsor will be used to refer to both sponsors and applicants. 
 
3 For purposes of this guidance, references to drugs and drug and biological products includes drugs approved  
under section 505 of the Federal Food, Drug, and Cosmetic Act (the FD&C Act) (21 U.S.C. 355) and biological 
products licensed under 351 of the Public Health Service Act (PHSA) (42 U.S.C. 262) that are drugs.   
 
  1  Contains Nonbinding 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.  
 
 
II.  BACKGROUND 
 
Population PK analysis is a well-established, quantitative method that can explain some of the 
variability in drug concentrations among individuals (Sheiner, Rosenberg, and Marathe 1977; 
Grasela Jr and Sheiner 1991).  The focus of this guidance is on the use of this approach in 
explaining the variability in drug concentrations observed among human trial subjects from 
intrinsic factors, extrinsic factors, differences in dosing, and routes of administration.  Drug 
concentrations can vary significantly among individuals who are following the same dosing 
regimen.  This variability can be attributed to intrinsic factors, such as differences in body weight, 
presence and extent of liver or renal impairment, or the presence of genetic polymorphisms, or to 
extrinsic factors, such as effects of food on drug absorption or concomitant medications that can 
interact with the administered drugs absorption and/or metabolism.  In some cases, intrinsic or 
extrinsic factors lead to clinically relevant changes in drug concentrations that require clinical 
management strategies, such as a change in the dose or dosing regimen.  
 
Intrinsic and extrinsic factors that commonly influence drug exposure are often investigated in stand-
alone clinical pharmacology studies.4  Stand-alone studies are well controlled and can provide a 
robust assessment of these interactions.  However, stand-alone studies are usually designed to 
focus on intrinsic and extrinsic factors with the highest potential to affect drug exposure, leaving 
many possible interactions unstudied.  Such stand-alone studies also typically have a limited 
number of subjects, are designed as single-dose studies, and might not be conducted in the target 
patient population.  Population PK analysis typically includes data directly collected from both 
patients and healthy volunteers, allowing for an assessment of multiple intrinsic and extrinsic 
factors that are not otherwise evaluated in a single trial or population.  In addition, the relatively 
large numbers of patients included in population PK analysis can improve the precision of the 
estimated effect of the factors that affect drug exposures and confirm which factors do not 
change drug exposures.   
 
Population PK analysis has the potential to integrate all relevant PK information across a range 
of doses and populations to identify factors that can affect a drugs exposure.  Such information 
can come from studies with rich or sparse PK sampling, after a single dose or multiple doses, 
and/or at steady state, and from healthy individuals, subjects with organ impairment, or the 
patient population.  These analyses, in turn, can inform strategies to select initial dosage 
regimens, manage dosing and administration for a given subpopulation, plan subsequent studies, 
or support labeling.   
 
 
III.  APPLICATION OF POPULATION PK ANALYSIS 
 
 
4 See section IX for references for specific FDA guidances for industry that address stand-alone clinical 
pharmacology studies. 
  2  Contains Nonbinding Recommendations 
Sponsors seeking advice on the use of population PK analysis for drug development decisions 
are encouraged to do so at appropriate milestone meetings with the Agency.  While this guidance 
focuses on typical applications of population PK analysis, it is recognized there are many novel 
applications of population PK analyses beyond those described in this guidance.  For example, 
application of population PK, model-based approaches can be an alternative for evaluating the 
bioequivalence of long-acting injectables, products with sparse PK sampling, or other scenarios 
when non-compartmental analysis (NCA) becomes challenging (Zhao et al. 2019).  For such 
applications, the models intended use and its regulatory impact determine the level of robustness 
needed for model evaluation.  As an example, the model-integrated approach used should be 
expected to reliably control the error rate for confirmatory bioequivalence applications.  
Sponsors are encouraged to contact the Agency to discuss such novel applications of population 
PK analyses to inform drug development and use. 
 
Confidence in a given population PK analysis to support an intended objective is increased by 
the following:  
 
  Understanding of the drugs PK properties 
 
  Prespecified questions in the population PK data analysis plan that will be addressed by a 
population PK analysis  
 
  PK data of sufficient quantity and quality (Bonate et al. 2012) from subjects who 
represent the indicated population and subpopulations of interest 
 
  Good model performance (i.e., the model should describe the data with acceptable 
accuracy and precision and should be evaluated for model misspecification) and validity 
for the intended objective 
 
A.  Application of Population PK Analysis in Drug Development 
 
1.  Selecting Dosing Regimens To Be Tested in Clinical Trials 
 
Population PK analyses are capable of identifying intrinsic and extrinsic factors that can 
significantly impact the pharmacokinetics of the drug and inform dosing regimens for testing in 
clinical trials to help minimize the variability of treatment response in patients.  For example, an 
observed strong relationship between body weight and drug exposure can provide support for 
body weight-based dosing schemes (e.g., mg/kg dosing or categorical dosing based on body 
weight cut points).  Such analyses should be combined with a robust understanding of the 
relationship between drug exposure and drug effect (e.g., by using pharmacodynamic biomarkers 
or clinical endpoints), target engagement (e.g., receptor occupancy), or drug toxicity to inform 
the clinical relevance and further refine dosing. 
 
Population PK models can also be used to simulate drug exposures that are expected to occur 
following doses or dosing regimens that have not been directly investigated in prior clinical 
studies (see section V.D for a discussion on simulation strategies).  For example, a population 
PK analysis can be used to predict PK changes resulting from the inclusion of a loading dose, 
  3  Contains Nonbinding Recommendations 
changing the dose, or altering the dosing frequency of a dosing regimen for later trials in the drug 
development program.  When appropriately justified, such analyses, in conjunction with 
exposure-response data, have been used to approve dosing regimens that have not been directly 
evaluated in the clinical trials (Kimko and Peck 2010).  In such cases, sponsors are encouraged  
to seek Agency input in advance of performing the analyses.   
 
2.  Deriving Sample Size and Sampling Scheme Requirements 
 
Simulations and optimal design methods can maximize the utility of population PK data 
collection and analyses.  For example, the trial sample size and PK sampling schedule can be 
optimized so that the PK parameters and the estimates for major covariate (bolded terms are 
defined in section VIII upon first use) effects of interest can be estimated with a defined degree 
of precision (see section IV.B for a discussion on various sampling schedules).  Simulations with 
population PK models can help determine the number of patients in a subpopulation that 
achieves sufficient power to detect a significant covariate given a defined covariate effect size 
(e.g., the number of patients receiving a concomitant medication that should be included in an 
analysis to detect a significant drug-drug interaction (DDI); see section III.B.2).   
    
3.  Deriving Exposure Metrics for Conducting Exposure-Response Analysis 
 
The importance and application of exposure-response (E-R) relationships for new drugs are 
outlined in the FDA guidance entitled Exposure-Response Relationships  Study Design, Data 
Analysis, and Regulatory Applications (May 2003).5  Population PK analysis can be used to 
derive patient PK exposure metrics that are appropriate for sequential E-R analyses.  When 
relevant, the derivation of exposure metrics should account for:  (1) dose interruptions or 
modifications; and (2) variations in the pharmacokinetics of a drug over time due to intrinsic or 
extrinsic subject factors (e.g., time-varying exposure due to disease state or anti-drug antibodies 
for some monoclonal antibodies). 
 
Population PK models can predict individual patient exposures at specific time points regardless 
of the spread in sampling times (e.g., trough concentrations can be predicted for all subjects).  
When PK data are missing in a small number of subjects, population PK models can predict the 
most likely concentration-time profile based on individual covariates including both time-
varying and subject-level covariates (e.g., body weight, genetic polymorphism, sex) (Wahlby U 
2004).  Such predictions are useful, assuming that the residual error and the between-subject 
variability are low, and the effect of observed covariates on the PK characteristics of the drug is 
considerably larger (see section V.D).  
 
Individual patient exposure metrics are generated based on Empirical Bayes Estimates (EBEs).  
When parameter shrinkage is high (i.e., the standard deviation-based shrinkage is usually 
greater than 20 to 30 percent), EBEs might be considered less reliable (Savic and Karlsson 
2009).  The informativeness of individual data (e.g., in cases with sparse data) should be 
assessed using individual shrinkage estimates.  In addition to parameter shrinkage, the reliability 
 
5 We update guidances periodically.  For the most recent version of a guidance, check the FDA guidance web page 
at https://www.fda.gov/regulatory-information/search-fda-guidance-documents. 
 
  4  Contains Nonbinding Recommendations 
of individual PK patient exposure metrics is dependent on the nature of the collected PK data and 
the validity of model assumptions.  See section V.C for discussions on model validation and 
section IV for discussions regarding the adequacy of the data used for population PK analysis.    
 
4.  Pediatric Trial Designs  
 
The use of modeling and simulation can help evaluate potential trial designs and optimize dose 
selection for pediatric patients.6  Additional sample size considerations for pediatric studies have 
been presented in the literature (Wang et al. 2012).  Population PK analysis is especially 
appropriate in children because it allows the use of infrequent (i.e., sparse) sampling compared to 
the rich sampling associated with traditional PK analyses, thus minimizing the total volume of 
blood sampled.   
 
Selecting a dosing regimen for pediatric studies can be based on simulations using a population 
PK model developed with adult PK data and incorporating the following when scientifically 
justified:  (1) principles of allometry; (2) knowledge of developmental changes that can influence 
the pharmacokinetics of a drug (ontogeny); and (3) data on the bioavailability of the pediatric 
formulation (Holford, Heo, and Anderson 2013; Barbour, Fossler, and Barrett 2014; Zhang et al. 
2015; Mahmood I 2014).  Incorporating the latest understanding in the population PK model on 
the effect of maturation of physiology in pediatric patients across various ages, especially in 
those less than 2 years old, can further improve the ability to identify appropriate pediatric 
dosing.  Dose selection should also consider information on disease similarity and dose- and 
exposure-response relationships in adults and pediatrics.    
 
B.  Application of Population PK Analysis to Describe Drug Use in Labeling  
 
Population PK analysis using data from early- and late-stage clinical trials, together with results 
from stand-alone clinical pharmacology studies, can be used to support the absorption, 
distribution, and elimination (metabolism and excretion) (ADME) information in the labeling of 
the drug.   
  
In addition, population PK analysis, together with relevant information from stand-alone studies, 
nested studies, or other sources, is routinely used to evaluate the effects of covariates on the PK 
parameters of a drug and its relevant metabolites to support dosing recommendations.  The need 
for dose adjustments due to the effect of a covariate on the exposure of the drug should be 
evaluated in the context of all available data, including the known E-R relationships for efficacy 
and safety.  The E-R relationships should be used to establish concentration boundaries within 
which dose adjustments are not clinically relevant (i.e., covariate effects within these boundaries 
might not warrant a dose alteration; however, covariate effects outside these boundaries could 
require a dose modification to optimize the benefit-risk profile of the drug). 
 
 
6 For more information, see the FDA guidance entitled General Clinical Pharmacology Considerations for 
Pediatric Studies for Drugs and Biological Products (December 2014).  When final, this guidance will represent the 
FDAs current thinking on this topic.   
 
  5  Contains Nonbinding Recommendations 
1.  Specific Populations 
 
Results from population PK analyses can be incorporated into drug product labeling to describe 
the PK properties in general patient populations or specific populations, as appropriate.  Whether 
a covariate analysis supports labeling statements depends on multiple factors, such as the number 
of subjects with the covariate included in the analysis, the distribution of the covariate (for 
continuous covariates), and the amount of available PK data (see section IV.A).   
 
Some examples of these situations are listed below: 
 
  Stand-alone renal or hepatic impairment studies in the general population might not be 
ethical with some drugs (e.g., highly toxic agents).  In such cases, adequate representation 
of patients in clinical trials with the intrinsic factor of interest, as well as adequate PK 
sampling to reliably characterize the effects of the intrinsic factor, can allow for the use 
of population PK analysis to characterize the exposure of the drug and its relationship to 
response and thus support dosing recommendations in that population. 
 
  Some defined specific populations are traditionally not studied in stand-alone, clinical 
pharmacology studies because of the lack of an expected clinically meaningful effect on 
the drugs pharmacokinetics due to prior knowledge of the drugs ADME characteristics.  
Instead, factors such as the influence of sex, age, body weight, or race on the 
pharmacokinetics of the investigational drug are often studied without stand-alone trials.  
Population PK analysis can be used to describe the pharmacokinetics of a drug in these 
subgroups.  
 
  Simulations from population PK models established from pediatric and adult data can be 
used to compare the exposure of the drug in pediatric patients and adults, and then derive 
the recommended pediatric doses. 
 
2.  DDIs 
 
Clinical DDIs (e.g., nested studies as part of a phase 3 trial) can be evaluated using population 
PK analysis.  General design considerations for nested DDI studies are found in the FDA 
guidance entitled Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and 
Transporter-Mediated Drug Interactions (January 2020).  Using population PK approaches to 
characterize the DDI potential of a drug is not without limitations, and methodological 
considerations for using population PK analysis to evaluate DDIs have been described elsewhere 
(Bonate et al. 2016; Wang et al. 2017).  Specific considerations for the use of a population PK 
approach to evaluate DDIs include the following: 
 
  DDIs should be characterized for individual drugs and not for therapeutic classes of 
drugs, although exceptions are possible (e.g., multiple proton pump inhibitors can be 
pooled if the interaction mechanism is pH-dependent). 
 
  Multiple perpetrators can be pooled to form one covariate category provided that the 
perpetrators belong to the same class of index inhibitors/inducers (i.e., weak, moderate, 
  6  Contains Nonbinding Recommendations 
or strong) as identified by the Agency.  The pooled perpetrators should all have the same 
mechanism of interaction with similar specificity for their respective metabolizing 
enzymes.  In cases where a clear classification is not recognized by the Agency (i.e., 
transporter inhibitors), pooling should be prespecified whenever possible and is 
considered on a case-by-case basis. 
 
  An adequate number of subjects with the concomitant medication should be included in 
the trial.  The PK sampling schedule should allow for appropriate characterization of the 
PK parameters of interest.  Adequate information on the dose, timing of administration, 
and duration of treatment for the interacting drugs should be available.  Simulations can 
determine the number of subjects needed to detect an interaction of a defined magnitude 
within a given trial design.  
 
  Interactions should be investigated on all physiologically plausible structural elements of 
the PK model (e.g., apparent clearance (CL/F), relative bioavailability (F ), rate of 
rel
absorption) based on the mechanism of the interaction. 
 
 
IV.  DATA USED FOR POPULATION PK ANALYSIS 
 
Sponsors are encouraged to use all relevant data to support the population PK model.  The 
sponsor should justify the omission of data from certain studies, certain individuals, or certain 
time points and prespecify such omissions in the population PK data analysis plan or study 
protocol whenever possible.  The sponsor should demonstrate that the data are adequate for the 
purpose of the analysis in their report.  For example, sponsors can conduct model-based power 
analyses showing that the given study design has sufficient power to detect clinically meaningful 
covariate relationships under the proposed population PK model (see section III.B).   
 
A.  Study Subjects and Covariates 
 
Determining if the data are adequate to address the intended study question is a vital step in any 
population PK analysis (see section V.C).  The dataset should include a sufficient number of 
subjects with an adequate number of PK samples at informative time points.  Covariates cannot 
be claimed to be influential or not influential on the exposure of a drug if the covariate 
distribution is narrow (for continuous covariates) or if the number of subjects in the category is 
inadequate (for categorical covariates).  Many continuous covariates have established cutoff 
values that define category levels.  If dosing recommendations are proposed for these categories, 
the range of the continuous covariates should preferably span the entire category and not just the 
upper or lower ends.  An increase in the range and frequency of the covariates tends to increase 
the likelihood of finding a true clinically significant covariate relationship and decreases the 
likelihood of finding false-positive relationships (Wahlby et al. 2002; Wahlby, Jonsson, and 
Karlsson 2001). 
 
B.  PK Sampling Schedule  
 
  7  Contains Nonbinding Recommendations 
The precision and bias of model-derived PK parameters are dependent on multiple factors, 
including the complexity of the model, the number of subjects, the number of samples per 
subject, and the sampling schedule.  As the number of samples per subject decreases, the 
importance of the timing of PK samples increases.  For example, if the purpose of an analysis is 
to match the maximum concentration (C ) observations across populations or dosage forms, 
max
then a sufficient number of PK samples should be collected in the absorption phase.  Sponsors 
are encouraged to prospectively plan the PK sampling schedule so that the population PK model 
is maximally informative.  
 
The methodology and available software for the optimal design of the population PK model are 
covered in multiple reviews in the literature (Dodds, Hooker, and Vicini 2005; Nyberg et al. 
2015; Ogungbenro and Aarons 2007; Ogungbenro and Aarons 2008).  The list below shows 
some examples of sampling schedules.  Depending on the purpose of the analysis, one or a 
combination of the listed strategies can be employed: 
 
  Patients are randomly assigned sampling windows that are derived based on optimal 
design methods.  The number of samples per patient and the number of sampling 
windows are also determined based on optimal design methods. 
 
  Patients randomly contribute two or more samples that, when combined, cover the entire 
dosing interval. 
 
  Most patients contribute one sample at a specified time point, often immediately before 
the next dose when there is sufficient prior PK knowledge about the study drug. 
 
Sponsors are encouraged to use all relevant PK data for the intended use.  However, the extent of 
sampling per individual patient and the percentage of sampled patients should ultimately depend 
on the intended use of the data.  For example, if the C  will be used in subsequent E-R 
max
analyses, then there should be adequate sampling around the time to the maximum concentration 
(T ) (see section III.A.3).  In any situation, it is important to verify that patients with missing 
max
PK data do not differ from other patients.  For example, patients with missing PK data should not 
have higher dropout rates because of a lack of effect or adverse events.  If between-occasion 
variability will be estimated, the design should be sufficient to assess the presence and 
magnitude of effect, requiring at least multiple samples per individual at more than one occasion.  
Ignoring large between-occasion variability can lead to biased population parameter estimates 
(Karlsson and Sheiner 1993).  Similarly, multiple samples per individual should be collected if 
time-varying covariates, such as the influence of anti-drug antibodies on drug exposure, are 
expected. 
 
 
V.  DATA ANALYSIS 
 
This section of the document provides some guiding principles on the methodological aspects of 
population PK analysis.  Further methodological considerations and good practices, including 
topics not covered here, have been described by others (Ette and Williams 2007; Bonate PL 
  8  Contains Nonbinding Recommendations 
2011; Mould and Upton 2013; Byon et al. 2013; Tatarinova et al. 2013; Lunn et al. 2002; 
Schmidt and Radivojevic 2014). 
 
A.  Preliminary Examination of the Data 
 
Population PK analysis often begins with a visual examination of the observed data.  Preliminary 
examination of the data reveals patterns and features in the population dataset using graphical 
and statistical techniques and can provide powerful diagnostic tools for confirming assumptions 
or, when the assumptions are not met, for suggesting corrective actions (Tukey 1977; Ette and 
Ludden 1995).  For example, strong correlations between covariates can preclude the ability to 
make inferences about individual covariate effects and have implications for the choice of 
covariate modeling approach.  The scientific (i.e., pharmacologic, biologic, pathophysiologic, or 
clinical) merits of including highly correlated covariates should be assessed.  This scenario is 
often the case with, for example, body weight and creatinine clearance as calculated by the 
Cockcroft-Gault Equation.  Relevant preliminary examination of the data should be concisely 
described in the population PK analysis report.  
 
B.  Model Development 
 
Model development methods and best practice recommendations are constantly evolving.  
Specific advice on how to develop population PK models is beyond the scope of this guidance.  
However, to facilitate the regulatory review of population PK models, sponsors should explicitly 
describe their model development procedures (see section VII for more discussion on population 
PK reporting).  Some aspects of model development that are important for regulatory review are 
provided below:    
 
  Model development issues can be addressed through several valid approaches, each with 
its own benefits and drawbacks.  For example, covariate analysis can be performed based 
on several approaches or their possible combinations (e.g., stepwise covariate analysis, 
full covariate model approach, the Lasso) (Wahlby, Jonsson, and Karlsson 2002; 
Gastonguay 2004; Ribbing et al. 2007).  In such cases, sponsors should justify why a 
particular approach was used.  
 
  The choice of the structural model should be informed from exploration of the observed 
data, previous modeling results (when applicable), and a common scientific rationale. 
Otherwise, an inappropriate structural model (e.g., missing the covariance between 
clearance and volume of distribution in the structural model) could result in incorrect 
covariate identification.  
 
  Covariate-parameter relationships can be formulated based on the current knowledge of 
biology, physiology, or allometric principles, and should take into account any data-
driven information on the covariate-parameter relationship (e.g., time-dependent 
covariates).  Correct identification of the true covariate-parameter relationship is essential 
when applying a population PK model to describe drug exposure in patients based on 
covariates included in the population PK analysis or when applying the population PK 
model to untested scenarios.   
  9  Contains Nonbinding Recommendations 
 
  Issues regarding missing data, including missing covariates and data below the limit of 
quantification (LOQ), should be addressed with appropriate analysis methods (Beal 2001; 
Bergstrand and Karlsson 2009; Johansson and Karlsson 2013; Keizer et al. 2015).  The 
sponsor should justify their methodological approach with regard to missing data using 
approaches such as sensitivity analysis. 
  
  The sponsor should distinguish between outlying individuals and outlier data points.  
Individual data points that are suspected outliers could be omitted during the model 
development process.  However, the sponsor should investigate the influence of the 
outliers on the final parameter estimates by refitting the final model to the complete 
dataset.  Removal of suspected outlying individuals is generally discouraged unless the 
reason for the outlier is a protocol violation or other human error.  Sponsors should 
specify how outliers are identified and handled in the analysis.  The reasons for declaring 
a data point to be an outlier should be prespecified in the data analysis plan.  For 
example, data points with a normalized weighted residual greater than five could be 
considered outliers in some cases.  The number of excluded outliers should be kept to a 
minimum and clearly documented in reports and datasets.    
 
C.  Model Validation 
 
Model validation is a critical step for any population PK analysis and should be conducted to 
examine whether the developed model can sufficiently characterize the observed data and 
generate reliable modeling and simulation results to meet the needs of the analysis.  No single 
model validation method is generally sufficient to evaluate all components of a model.  Several 
methods are generally used so that the relative strengths and weaknesses of each method can 
complement each other.  In general, models should describe the data with an acceptable level of 
bias and an acceptable degree of precision.   
 
Model validation depends on the objective of the analysis and should follow a fit-for-purpose 
approach.  In some cases, a model can be valid for one purpose but not for another.  When 
parameter shrinkage is high (standard deviation-based shrinkage: usually greater than 20-30 
percent), EBEs might be less reliable (Savic and Karlsson 2009).  The informativeness of 
individual data should be assessed using individual shrinkage estimates, and the reliability of 
individual drug exposure levels to be used in a sequential E-R analysis (see section III.A.3) 
needs to be carefully evaluated.  However, such a model can still be useful for a covariate 
analysis provided that the method for covariate modeling is insensitive to shrinkage (Mould and 
Upton 2013).        
 
Submissions to the Agency should contain a detailed description of the model validation 
methods used, an explanation of why those methods were selected, model diagnostics supporting 
use of the model, and sensitivity analyses, as needed, to demonstrate the impact of assumptions 
on the conclusions (see section VII.A).  Sponsors are encouraged to discuss model validation 
approaches with the Agency, particularly if the population PK analyses are critical for 
understanding and supporting proposed dosing.  
 
  10  Contains Nonbinding Recommendations 
1.  Common Approaches for Model Validation  
 
Procedures for conducting a thorough model validation are continuously evolving, and the 
Agency welcomes innovations in this field.  Several common methods of model validation are 
reported in the literature (Karlsson and Savic 2007; Byon et al. 2013, Nguyen et al. 2017) and are 
discussed in the sections below.   
 
Basic goodness-of-fit (GOF) plots illustrate how well the model describes the observed data.  
GOF plots are also used to evaluate model assumptions (e.g., normality of the random effects) 
and to guide model development.  Although GOF plots can show that the overall fitting is 
acceptable, additional evaluation of the model in subgroups of patients is often necessary 
depending on the context of use.  For example, if the model will be used to predict drug 
exposures in pediatric patients, model validation should be conducted for all relevant age groups.  
GOF plots that are stratified for important patient characteristics (e.g., stratified by age group or 
cytochrome (CYP) polymorphisms), trial design (e.g., dose or formulation), sampling design 
(e.g., sparse versus full), or other important variables are often more convincing of the adequate 
performance of a model than GOF plots of the full dataset.  Because few patients could be 
included in some analysis subgroups, the sponsor should include justifications, as needed, 
regarding the sufficiency of the available data to support its intended use.     
 
The following is a list of some of the GOF plots that are considered informative:  
 
  The dependent variable (DV) versus the individual predictions (IPRED) 
 
  The DV versus population predictions (xPRED), where x could be nothing, C 
(conditional), or P (simulation-based) 
 
  The absolute individual weighted residuals (|IWRES|) versus IPRED or time7 
 
  The appropriate form of weighted residuals (xWRES) versus xPRED or time, where x 
is defined as above 
 
  A representative sample of IPRED, xPRED, and observations versus time (one plot per 
subject)7 
 
  A histogram or Quantile-Quantile (Q-Q) plot of random effects 
 
  The correlations between random effects 
 
  The random effects versus covariates8 
 
 
7 Time should be evaluated as continuous time and as time after dose. 
 
8 This plot is most informative when it is compared before and after the inclusion of covariates. 
  11  Contains Nonbinding Recommendations 
The individual parameters, IPRED and IWRES, tend to shrink towards the population estimate in 
individuals with few observations.  When shrinkage is high (the standard deviation-based 
shrinkage is usually greater than 20-30 percent), diagnostic plots that rely on EBEs, IPRED, or 
IWRES can become uninformative, and correlations between random effects and covariates can 
be obscured (Savic and Karlsson 2009, Xu et al. 2012, Lavielle and Riba 2016).  Furthermore, 
high shrinkage can limit the value of using individual post hoc estimates for E-R analysis (see 
section III.A.3).  However, simulation-based, diagnostic plots are not affected by shrinkage in a 
similar manner and can be more informative for diagnostic purposes when shrinkage is high.  
Model selection based on parameter shrinkage is not necessary.  There are several available 
simulation-based diagnostics, including but not limited to, Visual Predictive Check (VPC), the 
prediction corrected VPC (pcVPC), the Numerical Predictive Check (NPC), and Normalized 
Prediction Distribution Errors (NPDE) (Bergstrand et al. 2011, Brendel et al. 2006).   
 
GOF criteria can also be reflected by some numerical metrics, such as the estimate of the 
precision of a parameter.  Estimates of parameter precision can provide valuable information 
regarding the adequacy of the data to support that parameter.  Parameter uncertainty can be 
estimated through several methods, such as bootstrap procedures, sampling importance 
resampling, log-likelihood profiling, or using the asymptotic standard errors of parameter 
estimates.  In addition to parameter uncertainty, it is important to compare parameter point 
estimates with previous analyses and to evaluate the physiological plausibility of the point 
estimates.  Another useful numerical metric to evaluate models is the condition value.  A large 
condition number (the ratio of the largest and the smallest eigenvalue), typically interpreted as 
values greater than 1000) indicates that the observed data cannot support the estimation of one or 
several parameters (Montgomery, Vining, and Peck 2012).  Over-parameterized models can be 
simplified and updated as additional data from well-designed clinical trials become available.   
 
An appropriate model should be biologically plausible, consistent with current knowledge, and 
have mathematically identifiable parameters.  Performance of the model can be checked against 
a set of test data, either internal or external.  A validation approach relying on the data used for 
model building is considered as internal validation.  External validation, on the other hand, relies 
on the data not used for model building.  Data collected in a drug development program can be 
split into a model building dataset and a testing dataset.  The testing dataset is commonly used 
for external validation.  Data splitting is a powerful method for model checking.  However, 
before deciding on a data splitting approach, sponsors should consider the potential impact of the 
loss of data on the models power to detect covariate relationships and estimate parameters with 
an acceptable degree of precision.  
 
There is an additional level of uncertainty when the purpose of the model is to simulate PK 
profiles for scenarios that are beyond those that have been clinically studied.  Such uncertainty 
can be addressed by a sensitivity analysis of the parameter estimates and their impact on the 
metric used to inform a decision (Kimko and Peck 2010). 
 
D.  Simulations Based on Population PK Models 
 
Simulations should be based on protocols that outline the simulation study to be performed.  The 
level of detail in the protocol should correspond to the complexity and impact of the question the 
  12  Contains Nonbinding Recommendations 
simulation addresses.  Models used for simulations should be validated to address the specific 
question the simulation study is trying to answer.  Although a model could have been previously 
used and validated, it needs to be revalidated if the new purpose is different from the original 
purpose.  Depending on the purpose of the model, various levels of uncertainty and variability 
can be added to the simulations.  Some examples are discussed below.   
 
1.  Simulations Based on Fixed-Effect Estimates 
 
In their simplest form, simulations are used to illustrate the drug-concentration profile for a 
typical individual.  Such simulations are based on the typical estimates of the fixed-effect 
parameters.  Note that predictions based on typical parameter estimates do not correspond to 
mean predictions.  Mean predictions are obtained by calculating the mean concentration-time 
profiles based on simulations with between-subject variability in population PK parameters and 
residual error. 
 
2.  Simulations Based on Uncertainty of Fixed-Effect Estimates 
 
Uncertainty in parameter estimates can be accounted for if the desire is to illustrate the 
probability of the typical subjects drug exposure to reach or stay above a specific cutoff point or 
if one wishes to illustrate the effect of covariates.  The choice, if uncertainty of fixed effects is 
considered, should be objective driven.  For example, forest plots that illustrate the effect of 
covariates on the area under the concentration time curve (AUC) or other parameters can be 
generated based on simulations with uncertainty in fixed-effect parameters, thereby facilitating 
the interpretation of the relative importance of covariates on exposure.  Alternatively, if the 
effect on exposure estimates is the objective, the uncertainty of all fixed effects should be 
considered.  These types of simulations are also useful to evaluate the performance of new 
dosing regimens for testing in future trials.  Additionally, simulations with parameter uncertainty 
can be used to graphically illustrate the effect of parameter precision on PK profiles. 
 
3.  Simulations Based on Estimates of Between-Subject Variability 
 
Between-subject variability in PK parameters is accounted for in simulations when the purpose is 
to show the range of individual predictions of concentrations in the studied population.  Residual 
error is added to the individual prediction when the range of observed concentrations is the main 
interest.  
 
If the purpose is to predict the observed concentration range of a future population, then 
uncertainty in the between-subject variability should be accounted for in addition to the fixed-
effect parameters and residual error.  
 
The correlation between random effects should be accounted for to avoid unrealistic parameter 
combinations in subjects.  Simulations that account for between-subject variability and include 
covariate effects should be conducted in a population with realistic demographic variables.  
Demographic variables can be obtained from databases or generated by resampling with 
replacement of the individuals in the original study or by sampling the covariate distributions and 
their correlations in the target population. 
  13  Contains Nonbinding Recommendations 
 
 
VI.  LABELING BASED ON THE RESULTS OF POPULATION PK ANALYSIS 
 
Results from population PK analysis can be presented in the CLINICAL PHARMACOLGY 
section and summarized in other sections of labeling, as appropriate.  Other relevant labeling 
sections should not repeat this detailed information but rather provide a succinct description or 
recommendation based upon these results followed by a cross-reference to the CLINICAL 
PHARMACOLGY section, as appropriate.  In general, there is no need to explicitly state in 
labeling that the information is based on population PK analysis.  Recommendations for 
developing the CLINICAL PHARMACOLOGY section are discussed in the FDA guidance for 
industry entitled Clinical Pharmacology Labeling for Human Prescription Drug and Biological 
Products  Content and Format (December 2016).   
 
 
VII.  POPULATION PK STUDY REPORTING 
 
This section outlines the recommended format and content for a sponsor to submit a population 
PK analysis in support of its drugs clinical pharmacology program.  Population PK analyses can 
be appropriate at multiple points in the drug development process, for example in the IND, NDA, 
BLA, ANDA, or postmarketing stages.  The depth and breadth of population PK analyses at each 
stage can vary because of the availability or quality of clinical data.  
 
Population PK study reports that are important for regulatory decisions should be included in the 
electronic common technical document (eCTD) module 5.3.3.5 and the corresponding programs, 
datasets, and define files under the datasets folder within eCTD module 5. 
 
A.  Format and Content of the Population PK Study Report 
 
To enable the efficient and consistent review of population PK analyses, the FDA recommends 
that the results from population PK analyses should be accompanied with a structured population 
PK report.  The report should contain the following sections:  (1) executive summary, (2) 
introduction, (3) materials and methods, (4) results, (5) discussion, (6) conclusions, and (7) 
appendix (if applicable).  Expected information and elements to be included in these sections are 
discussed briefly in Table 1.  Sections of the annotated labeling that contain information based 
on population PK analysis should include hyperlinks to the population PK report.  Additional 
discussion regarding reporting is available in the scientific literature (Dykstra et al. 2015). 
 
 
 
 
  14  Contains Nonbinding Recommendations 
Table 1: Expected Content in Each Section of the Population PK Study Report 
Sections  Expected Content 
Executive    The purpose of the analysis 
Summary   
  The key findings that affect drug use or labeling  
 
  A plain language summary of objectives, data, methodology, and conclusions  
 
  Results presented as their effect on clinically relevant drug exposures, not 
model parameters 
 
  Visual presentations of relevant exposure metrics to illustrate how identified 
subpopulations differ from the typical population 
Introduction    The background to place the population PK study in the context of the overall 
clinical development program 
 
  The objectives for the population PK analysis 
 
  The PK characteristics of the drug  
Materials    A description of the trials and the trial data included in the population PK 
and Methods  analysis with information on: 
 
  Dose, including the frequency and duration of dosing, as well as the 
o
number of subjects, number of samples, and number of below the level 
of quantification (BLOQ) samples  
 
  Reasons for not including trials with available and potentially 
o
informative data, if applicable  
 
  A distinction between all available relevant data versus final data used 
o
for model building and evaluation 
 
  Hyperlinks to the original trial reports of data used for the analysis and the 
bioanalytical evaluation reports for each trial 
 
  Detailed demographic information and other covariate information 
 
  Planned criteria, table of assumptions, and procedures for model building and 
evaluation, including: 
 
  Handling of outliers and missing data (e.g., methods of imputation) 
o
 
  Model-estimation method 
o
 
  Equations for all model structures and covariate relationships 
o
 
  Equations for parameter transformations, if parameters are presented in 
o
dimensions other than those in the model output file 
 
  Continued 
  15  Contains Nonbinding Recommendations 
Table 1 continued: Expected Content in Each Section of the Population PK Study Report 
Sections  Expected Content 
Materials    Equations for parameter transformations, if parameters are presented in 
o
and Methods  dimensions other than those in the model output file 
 
  The method for incorporating variability or parameter uncertainty or 
o
for deriving confidence intervals or prediction intervals 
 
  Detailed descriptions of planned simulations and sensitivity analyses used to 
support conclusions and recommendations 
 
  Software and version used for the analysis and simulation* 
* Note: The FDA does not recommend or require the use of particular 
software for conducting population PK analysis.  The sponsor should report 
the software used and submit the electronic files supporting the analysis and 
simulations (see section VII.B below).  If necessary, consult the FDA 
regarding the submission of certain file types. 
Results    Summary of the data and data exploration, with adequate graphical and tabular 
displays 
 
  A description of the final model, model building steps, validation, final 
parameter estimates, and deviations from planned analyses, if applicable 
 
  Application of the model to the objectives, including the relevance of 
covariates on PK parameters and simulations of alternative dosing, if 
applicable 
 
  Accompanying tables and figures to place findings into context 
 
  Typically, tables and figures should include the following, with other tables 
and figures included in the Appendix and cross-referenced in the report body, 
as applicable: 
 
  Key modeling building steps, including a description of the structural 
o
and covariate models and objective function changes   
 
  A comparison of parameter estimates from the base to the final model 
o
 
  Parameter estimates and their associated uncertainty, with variability 
o
reported as the coefficient of variation (CV%)  and precision reported 
as the percent relative standard error (RSE%) or the 95 percent 
confidence interval.  Shrinkage should be included in the parameter 
estimates table. 
 
  Key diagnostic plots 
o
 
  GOF plots stratified by the relevant covariates to illustrate model 
o
performance in specific subgroups 
 
  Continued 
  16  Contains Nonbinding Recommendations 
 
Table 1 continued: Expected Content in Each Section of the Population PK Study Report 
Results    Results of model evaluations/validation, such as VPCs, 
o
pcVPCs, NPCs, and NPDEs, etc., should also be included 
 
  A description of simulation results or scenarios, if applicable 
Discussion    An interpretation of the modeling results, including discussions on: 
 
  The adequacy or limitation of the data and model to support 
o
conclusions and recommendations  
 
  The rationale for the modeling approach, assumption 
o
verification, and assessment of uncertainty  
 
  Consistency or inconsistency between the results from 
o
population PK and stand-alone clinical pharmacology studies 
and/or previously completed related population analyses 
 
  An assessment of the clinical relevance of the results, including 
physiological plausibility and clinical significance of the identified 
relationships 
 
  An assessment of any alternative dosing regimens in the context of E-R 
analyses for safety and efficacy 
Conclusions    A short summary of major findings from the analysis written in plain 
language 
Appendix  Additional materials should be included in the Appendix, as needed.  The 
Appendix does not need to contain full line listings from datasets.  Materials 
from the Appendix should be cross-referenced in the report body, as 
appropriate.  Appropriate materials for the Appendix include but are not limited 
to: 
 
  Supplemental tables and figures for summaries and analyses included 
in the report that support model development or conclusions but are not 
critical to the primary purpose of the report 
 
  Methods and codes for generating the key figures and tables 
(preferably, codes are submitted as separate files from the study report) 
 
  Listing of samples removed from the analysis and reasons for removal 
 
  A run record describing the steps during model development  
 
  A diagram showing the connection between input datasets, 
codes/control streams, and output datasets presented in the E-R report 
 
B.  Submitting Electronic Files to the Agency 
 
  17  Contains Nonbinding Recommendations 
Sponsors should refer to the FDA web page9 for general advice on submitting data and related 
files (e.g., coding scripts).  It is critical that datasets and model files submitted for the base, final, 
and key intermediate models are the same as those used for generating the model outputs in the 
appendices of the report.  In addition, FDA staff should be able to identify the source data for 
any dataset constructed using output files or postprocessed results from population PK analyses.  
For example, if exposure metrics from population PK analyses are included in datasets for E-R 
analyses, the sponsor should ensure the traceability between the population PK model output and 
any postprocessing steps involved in the construction of the dataset.  This can be accomplished 
by providing definition files, reviewer guides, and/or codes utilized for dataset assembly.    
 
All conclusions drawn from the population PK analysis should be reproducible by the Agency 
with the available codes and data.  It is also important to include the unique subject identifier 
information for each subject in the population PK dataset and ensure that the identifiers are the 
same in the individual clinical trial datasets.  This information is vital if data integration is 
required between the individual-level outputs (e.g., individual, post hoc estimates for clearance 
or volume of distribution generated from the population PK model) and the efficacy or safety 
datasets from the individual clinical study reports.
 
9 The FDA web page on submitting data can be accessed at https://www.fda.gov/about-fda/center-drug-evaluation-
and-research-cder/modeldata-format. 
 
  18  Contains Nonbinding Recommendations 
VIII.  GLOSSARY OF SELECTED TERMS 
 
Covariate  An observed factor that correlates with drug exposure in patients (e.g., 
renal function, body weight, age, sex, genetic polymorphism)  
 
DV  Dependent variable (e.g., drug plasma concentrations) 
 
EBE  Empirical Bayes Estimates, or individual parameter estimates in a 
mixed-effects model 
 
GOF   Goodness of fit, a collection of diagnostic criteria used to evaluate 
model performance  
 
IPRED  Individual Predicted Data, based on Individual Empirical Bayes 
parameter estimates 
 
IWRES  Individual Weighted Residuals, a type of residual 
 
Model validation   The use of various methods to validate model performance for a 
specific purpose 
 
NPC  Numerical Predictive Check, a GOF method related to VPC  
 
pcVPC  Prediction corrected VPC, a type of GOF plot related to VPC  
 
Q-Q  Quantile-quantile, a type of GOF plot 
 
Residual   The difference between the predicted and the observed value 
 
Residual error  An estimate of the remaining unexplained variability 
 
Shrinkage  A measure of the extent to which EBE depends on the population 
parameters versus the individual observed data 
 
VPC  Visual predictive check, a type of GOF plot   
 
xPRED  Predicted data, based on population parameter estimates, where x can 
be nothing, C (conditional), or P (simulation-based) 
 
xWRES  Weighted Residuals, a type of diagnostic, where x can be nothing, C 
(conditional), or P (simulation-based) 
 
  19  Contains Nonbinding Recommendations 
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  23  Contains Nonbinding Recommendations 
FDA guidances for industry:10  
 
 Clinical Pharmacology Labeling for Human Prescription Drug and Biological Products  
Content and Format (December 2016) 
  
Clinical Drug Interaction StudiesCytochrome P450 Enzyme- and Transporter-Mediated Drug 
Interactions  (January 2020) 
 
Exposure-Response Relationships  Study Design, Data Analysis, and Regulatory Applications 
(May 2003)  
 
In Vitro Drug Interaction Studies  Cytochrome P450 Enzyme- and Transporter-Mediated Drug 
Interactions (January 2020) 
 
Pharmacokinetics in Patients With Impaired Hepatic Function  Study Design, Data Analysis, 
and Impact on Dosing and Labeling (May 2003) 
 
 
 
 
 
10 See also the following FDA guidances:  (1) Assessing the Effects of Food on Drugs in INDs and NDAs  
Clinical Pharmacology Considerations (February 2019); (2) General Clinical Pharmacology 
Considerations for Pediatric Studies for Drugs and Biological Products (December 2014); and (3) 
Pharmacokinetics in Patients With Impaired Renal Function  Study Design, Data Analysis, and Impact 
on Dosing and Labeling (September 2020).  When final, these guidances will represent the Agencys 
current thinking on these topics. 
 
  24