事務連絡

令 和 元 年 ６ 月 19 日

各都道府県衛生主管部（局）

御中

厚生労働省医薬・生活衛生局医薬品審査管理課「母集団薬物動態／薬力学解析ガイドライン」の英文版について「母集団薬物動態／薬力学解析ガイドライン」については、「「母集団薬物動態／薬力学解析ガイドライン」について」（令和元年５月 15 日付け薬生薬審発 0515
第１号厚生労働省医薬・生活衛生局医薬品審査管理課長通知。以下「課長通知」という。）により貴管下関係業者等に対する周知をお願いしたところですが、今般、標記について、別添のとおり取りまとめましたので、貴管下関係業者等に対し周知方願います。
なお、本ガイドラインの正文は課長通知別添の邦文版であり、本英文版は参考資料として作成した仮訳であることに御留意ください。

PSEHB/PED Notification No. 0515-1
May 15, 2019

Guideline on Population Pharmacokinetic and Pharmacodynamic Analysis
Contents
1. Introduction
1-1. Background and objectives
1-2. Scope
2. Study procedures
2-1. Clinical trial plan and execution
2-1-1. Points to consider prior to clinical trial planning
2-1-2. Points to be described in protocol
2-1-3. Points to be described in the analysis plan on population analysis
2-1-4. Points to consider related to analytical method of drug concentration
2-2. Data handling
2-2-1. Data management
2-2-2. Missing values
2-2-3. Concentration values below the lower limit of quantification
2-2-4. Outliers
2-3. Model building and diagnosing
2-3-1. Building population pharmacokinetic and pharmacodynamic models
2-3-2. Diagnosis of the validity of a model
2-4. Model qualification
3. Model application
3-1. Predicting pharmacokinetic or pharmacodynamic characteristics of certain target
population
3-2. Clinical trial design
4. Reporting and providing information
4-1. Population analysis report
4-2. Regulatory submission
4-3. Providing information in package insert
5. Relevant guidelines and documents
6. Glossary

11. Introduction
1-1. Background and objectives
In clinical drug development, phase I clinical trials are generally conducted to evaluate
the tolerability, safety and the basic pharmacokinetic profile of the drug, and to investigate
the exploratory pharmacokinetics and pharmacodynamics of the drug in healthy volunteers.
This is done to obtain the information needed for planning and conducting subsequentclinical trials. A patient population, however, sometimes tends to exhibit great diversity interms of age, body weight, type and severity of disease, concomitant drugs, genetic factors,
and lifestyle habits. This results in pharmacokinetic or pharmacodynamic differences whena patient population is compared to a healthy volunteer population. Therefore, anunderstanding

of

the

relationshipbetween

the

dose,

pharmacokinetics,

andpharmacodynamics through phase II and III clinical trials involving patients in whom a drug
under development is indicated, clinical trials in children, elderly persons, or populations
with organ disorders related to drug elimination, drug interaction studies, etc., can be usefulto predict the efficacy and safety of the drug and to obtain the appropriate dosage andadministration. This approach also offers useful information when considering dose
adjustment in patients with a certain characteristic or using a certain concomitant drug.
Two approaches are used for the analysis of drug concentration etc. One is the standard
two-stage analysis in which relevant parameters are estimated for individuals and respective
statistics are performed in a second step. The other approach utilizes a population analysis
where relevant parameters are estimated for the population to which the individuals belong.
Population analysis makes it possible to simultaneously derive population mean values ofpharmacokinetic and pharmacodynamic parameters and their variability. Furthermore, theinfluence of multiple relevant intrinsic and extrinsic factors (covariates) on the drugconcentration and other clinical endpoints obtained from a large number of subjects with awide range of backgrounds can be evaluated, while putting less strain on subjects by
reducing the frequency of sampling in individual subjects (optimal sampling). These
attributes allow population analysis to be applied in late-phase clinical trials (in the target
patient population) and the pediatric drug development. Use of population analysis has
recently been increasing in the investigation of pharmacokinetics and pharmacodynamics,
exposure and response relationships in large-scale clinical trials that are conducted in
multiple regions. With the rapid progression of globalization in drug development,

2

population analysis has become a valuable analytical approach in the evaluation and ethnic
comparison of patient pharmacokinetic profiles and the consideration of appropriate dosage
and administration.
Utilizing simulations using population analysis models helps to obtain useful informationon the efficient conduct of clinical trials, taking the characteristics and development stage ofthe drug into consideration and with a recognition of the limitations due to the limited
amounts of data and information used for the analysis, and to appropriately provide
information to those involved in clinical practice.
This guideline is intended to provide general guidance that is scientifically valid at thepresent time so that evaluations based on population analysis are appropriately conductedduring drug development, and to summarize the points to consider and the basic concept of
the analysis of population pharmacokinetics and pharmacodynamics. Each contents in thisdocument has been reviewed based on scientific knowledge at the present time, however,when new findings are obtained with the progress of research in the future both in theory
and its practical application, it is necessary to act flexibly based on scientific judgments.

1-2. Scope
This guideline should be applied to the evaluation of population pharmacokinetics andpharmacodynamics for data obtained from phase I, II, or III clinical trials, post-marketingclinical trials, or clinical pharmacology studies, and/or a combination of clinical trials.
2. Study procedures
2-1. Clinical trial plan and execution
It is preferable that the sponsor of a clinical trial should take the following points into
consideration in planning and conducting the clinical trial. Over the course of the processfrom planning of the clinical trial to evaluation of analysis results, it is important tocollaborate with domain experts, such as those working in the areas of clinical pharmacology,
pharmacokinetics, biostatistics and pharmacometrics.
For post-marketing clinical trials, as defined according to Article 56 (Standards forDocuments Submitted in Reexamination etc.) of the Standards for the Implementation ofClinical Trials or Pharmaceutical Products (MHW Ordinance No. 28, 1997), "clinical trial"

3

should be replaced by "post-marketing clinical trial".
2-1-1. Points to consider prior to clinical trial planning(1) Points to consider regarding study design
There are certain points to consider in terms of clinical pharmacology, pharmacokinetics,
pharmacodynamics, biostatistics, and pharmacometrics when planning a clinical trial toobtain data for population analysis.
During population analysis, the model is built based on consideration of such factors as
the objectives of the clinical trial, dosage and administration, target population,
pharmacokinetics and mechanism of action, objective of the analysis, type of data collected(e.g. continuous or discrete values), and any restrictions on the collection of samples or data.
For this reason, the trial is designed by taking into consideration information and study
results that are already available, in addition to factors such as variable factors that may
affect pharmacokinetics and pharmacodynamics, efficacy outcome measures (e.g. true orsurrogate endpoints, or pharmacological endpoints that were validated for their relationshipto clinical efficacy or adverse reactions, and exploratory biomarkers), safety outcome
measures, timing of sampling, and sample size.
Where there are specific pharmacokinetic parameters to be estimated in population
pharmacokinetic analysis, the appropriateness of sampling timing has to be considered,taking into account the accuracy of estimation of such parameters. In the case of a chemicalcompound with a high plasma protein binding ratio, it is also useful to ensure presence orabsence of changes in the unbound fraction or unbound drug concentration can be
determined.
Timing of efficacy and safety endpoint evaluations and acceptable time windows, if
necessary, should be clearly described by taking the onset time of efficacy and adverse events
into account. It is also important to consider the relationship between the times at which dataon the drug efficacy and safety endpoints are obtained and the times at which sampling is
conducted for the determination of drug concentrations so that the exposure and response
relationship can be efficiently evaluated.
In case the data for population analysis are collected over a long period of time from thesame subject who is continuously receiving an investigational drug, parameters such as bodyweight, age, renal or hepatic functions, and drug metabolizing enzyme activities, which are

4inhibited or induced, may change over time. If it is necessary to evaluate changes over timein such parameters, the data of the item to be examined should be collected multiple times
over time. If the investigational drug’s pharmacokinetics and efficacy endpoints are expected
to change significantly in a short period of time, repeated measurements of the drugconcentrations and efficacy endpoints maybe useful within the same subject.
Simulations based on previous study results are useful in considering the study design,
such as the timing of sampling for drug concentrations and efficacy endpoints and sample
size, which are relevant to the planning of population analysis. For instance, performing
simulations with various distributions and data quantity of drug concentrations andcovariates makes it possible to investigate how results would be affected in advance and toreflect the results of the investigation in the future study design.
Hypothetical conditions set for the purpose of building a population analysis modelstrongly affect the analysis results, and it is therefore important to consider the study design
thoroughly to obtain reliable estimates for parameters of interest.
(2) Points to consider in developing a data collection plan
The plan for collecting samples for the measurement of drug concentration should beclearly described. In the sample collection plan assuming population analysis, it is permitted
to specify a wider range than for sample collection points of a standard pharmacokinetic
study. To obtain the necessary drug concentration data for the estimation of parameters ofinterest, however, it is preferable to set multiple time points for sample collection so thatsampling is not concentrated on a specific time point. A practical approach that can beaccommodated in clinical trial environments is to allow a specific time window for sampling
and to collect samples within the acceptable time windows. The actual time of samplingshould be accurately recorded. In relation to time of sampling, it is fundamental to obtainaccurate information on the time of dosing when performing pharmacokinetic analysis.
Patient medication history before sampling, including the time of dosing, should be collected
while taking into account the elimination half-life of the investigational drug being evaluated.
In cases of drip infusion, the start and end time of drip infusion need to be accurately
recorded.
When the effects of concomitant drug(s) are evaluated, dosing information of theconcomitant drug(s) should also be obtained. It is necessary to take appropriate measures to5

prevent noncompliance with treatment, as this may cause errors in the interpretation of theestimated parameters, and to also establish a system to obtain objective records on treatmentcompliance in reference to case report forms and patient diaries, in principle.
2-1-2. Points to be described in protocol
Regarding population analysis, points to be described in the protocol include the primaryobjectives of the population analysis. If it is clear that data from the clinical trial is combinedwith data from other clinical trials, this fact should be detailed in the protocol. If it is clearthat a population analysis is to be conducted during the clinical trial and the plan for the
population analysis is prepared separately from the other analysis plan, again this fact should
be detailed in the study protocol.
2-1-3. Points to be described in the analysis plan on population analysis
The analysis plan should describe the objective, overall strategy, and technical aspects
such as the procedures of the population analysis. Basic items to be included in the analysis
plan are the study and subjects to be included in the population analysis and the rationale forselection, the method for selection of the model, the method for estimation of parameters,
the software to be used in the analysis, the handling of predicted analytical issues (e.g. thehandling of missing values, concentrations below the lower limit of quantification, outliers),
and others. If the population analysis has more than one objective, the primary and secondary
objectives should be separately presented if necessary. Whether the subjects used in theanalysis include healthy volunteers or target patients, or both, or a limited population ofsubjects is adopted based on the ethnicity, age, or other background factors, should bedetermined with respect to the objective of the population analysis. In the case where theanalysis starts with an existing model e.g. built for another indication of the investigationaldrug, or established for adults but applied to pediatrics, the facts that an existing model isused, as well as the rationale why such a model could be used should be presented. Regarding
model building, the planned model building method, criteria for the inclusion of covariates
in the model, methods of model diagnosis and model evaluation should be described.
Although timing of preparing the analysis plan depends on whether or not data from theclinical trial are to be combined with data from other clinical trials, the analysis plan should
be developed at appropriate timing such as before the database is locked or before the start6

of the analysis, considering the objective of the analysis. If it becomes necessary to perform
additional analyses that are not described in the analysis plan, an analysis plan should be
prepared before performing the additional analyses or the details and background of theadditional analyses should be described in the population analysis report after performing
the additional analyses, depending on the analysis content.
2-1-4. Points to consider related to analytical method of drug concentration
Analysis of drug concentrations should be performed using a method that has beenvalidated in an appropriate manner according to the applicable guidelines on analyticalmethod validation. It is preferable to use a central laboratory measurement system whereby
drug concentrations are analyzed at a single facility. If drug concentrations are measured at
multiple facilities, it is preferable to ensure consistency, etc. of the measured concentrations
at the different facilities.
Collecting data on drug concentrations, dosing information, covariates, etc. on an asneeded basis and being aware of the presence/absence of uneven data distribution, etc. help
to ensure the efficient conduct of analyses rather than waiting until the time of data lock forthe clinical trial. If, however, the clinical trial is being conducted in a double-blind manner,data collection on an as needed basis during the clinical trial requires appropriate measure
to maintain the study blinding.
Regarding blood and other biological samples, the entire processes from the sample
preparation, storage at the clinical trial site, transfer of the samples from the clinical trial site
to the analytical laboratory and the analysis at the laboratory should take place in an efficientmanner, by securing human resource, places, equipment, etc. For long duration clinical trials,
analysis should be planned to ensure that the analysis will be completed before the expirationof the stability period of samples.
2-2. Data handling
2-2-1. Data management
To ensure the validity and reliability of the results of population analysis, it is important
to manage the data for analysis, in addition to the statistical processing method which serve
as a precondition, and the quality and validity of the software used. The data for analysis
should be managed according to an appropriate document such as a standard operating7

procedure (SOP) that stipulates operating procedures beforehand. Examples of data
management flows are illustrated in Figures 1-1 and 1-2. Population analysis can be
performed by combining data from multiple clinical trials. Analytical data may be developed
by selecting, combining, and processing data on necessary items from individual clinical
trial databases (Figure 1-1), or by selecting and processing data on necessary items from aclinical trial database in which data from multiple clinical trials are stored in the same format(Figure 1-2). In either case, specifications for analytical data should be documented inadvance. It is important to ensure traceability in a series of processes of creating analytical
data from clinical trial data. The adequacy of the developed analytical data should be judged
through visual checking of the figures generated from the data, review of the summary
statistics, etc.

Clinicaltrial 1

Clinical trial 1
Database

Clinicaltrial 2Clinical trial 2
Database

Clinicaltrial 3Clinical trial 3
Database

Combined andprocessed
according to
specifications

Data forpopulation
analysis

Data analysis

Ensuring traceability

Figure 1-1 Example of analytical data management flow: where data are prepared by
selecting, combining, and processing data on necessary items from individual clinical trial
databases

8

Clinical
trial 1
Clinicaltrial 2Clinical trial
database

Processed
according to
specifications

Clinicaltrial 3

Data forpopulation
analysis

Data analysis

Ensuring traceability

Figure 1-2 Example of analytical data management flow: where data are prepared by
selecting and processing data on necessary items from an integrated database (clinical trial
database)
2-2-2. Missing values
Missing values may cause bias in the estimations, which in turn may lead to false
conclusions, therefore every effort should be made to minimize missing values. Missing
values occur when some of the covariate data are missing or the measurement values of drugconcentrations and clinical responses are missing. In either case, this may complicate
interpretation of the analysis results. Any issues that may occur as a result of missing drugconcentration values, clinical response values and covariate data values and how to handle
them, should be described in advance in the analysis plan. In addition to such a policy
considered in advance, sensitivity analysis, etc. may be performed as necessary to investigate
consequences of missing values.
2-2-3. Concentration values below the lower limit of quantification
The simple omission of values for concentrations below the lower limit of quantification(LLOQ) from the analysis data may result in biased parameter estimates. An appropriate
method of handling of values for concentrations below the LLOQ in population analysis

9should be selected based on considerations of the characteristics of the analysis data or theobjective of analysis.
2-2-4. Outliers
The definition of outliers and the strategy for handling of outliers should be clearly definedin the analysis plan as far as possible. It should be discussed how outliers can affect theresults, based on comparison of results including/excluding outliers, and the discussion
should be described in the population analysis report. If analysis is performed by excluding
outliers, the rationale for the decision should be explained from the standpoints ofphysiological and clinical trial-related events, etc.
2-3. Model building and diagnosing
Population analysis is an iterative process of repeated model building and model diagnosis.
The type and level of the diagnostic method to be applied are selected appropriately basedon the stage of analysis. New techniques to analyze, build and diagnose population models
are being studied and developed on a regular basis. Those who conduct analysis are required
to have correct understanding of the algorithms used and to consider thoroughly theappropriate techniques to be used at different stages of the population analysis.
A typical method of model building and diagnosing is explained, as well as an example of
population analysis flow in Figure 2.

10• 母集団基本モデルの構築
Building base model
不採択
Not
adopted
Structural model
構造モデル• Building model thatpharmacokinetics•describes
薬物動態及び薬力学を記
andpharmacodynamics
述するモデルの構築
1-compartment model
1-コンパートメントモデル
Emax model, etc.
Emaxモデル 等

Error model
誤差モデル• Incorporating intervariation•individual
個体間変動誤差
errorの組み入れ• Incorporating intra• 個体内変動誤差
individual variation
の組み入れ
error

Covariate model
共変量モデル• Incorporating
covariates•known既知の共変量の組み入れ

Error model
誤差モデル• Incorporating• inter-occasion
時期間変動の
model
(if
組み入れ
necessary)（必要な場合）Modelモデル
diagnosis
診断

Adopted
採択
Buildingbase model by mutual consideration
相互に検討して母集団基本モデルを構築• 共変量の検討による母集団最終モデルの構築
Building final model based on consideration of covariatesNot
adopted
不採択

Base model
採択した母集団
adopted
基本モデル

Covariate
model
共変量モデル• Incorporating covariates intomodel
• base
母集団基本モデルへの共変量
Forward selectionの組み入れ
Backward
elimination
変数増加法
Stepwisecovariate modeling
変数減少法
Full
model estimation with
ステップワイズ法
all-inclusive
covariates,
etc.
フルモデル法
等

Model
モデル
diagnosis
診断

Adopted採択

qualification• Modelモデルの適格性評価

Not不適切appropriate(re-building
model from
（必要に応じて、母集団基
basemodel if necessary)
本モデルから再構築）

Model
モデル
qualification
適格性評価

Appropriate
適切Completion of
母集団最終モデ
final
model
ルの完成

Figure 2 Example of population analysis flow
2-3-1. Building population pharmacokinetic and pharmacodynamic models
A population pharmacokinetic or population pharmacodynamic model may be referred toas a base model or a final model, depending on the stage of model building. A base model
consists of a structural model, inter- and intra-individual variability (error model), andcovariates (covariate model) that clearly affect pharmacokinetics / pharmacodynamics. The
final model is a model that incorporates important covariates into the base model, and is
obtained after evaluating its validity.

11

In the following, structural, error, and covariate models are explained, although there is noneed for the process of building a model to follow this order. It is important to present theprocess of analysis, the path leading to the conclusion, and the rationale for selecting acertain model from among multiple different choices, to ensure that retrospective
investigation is possible and the analysis results can be reproduced in an objective manner.
(1) Structural model
A structural model is a model to describe pharmacokinetics and pharmacodynamics,
including models such as a compartment model and Emax model. The first point to consider
in selecting a structural model is to select one that can appropriately represent thecharacteristics of observations. The characteristics include linear or non-linear
pharmacokinetics, and monophasic or polyphasic behaviors. Furthermore, when selecting a
structural model, the amount of information in the obtained data needs to be considered. For
instance, pharmacokinetic parameters for the absorption phase cannot be estimated from thedrug concentration data obtained from samples collected only during the elimination phase.
Where distinction between linear and non-linear models and the optimal number ofcompartments is concerned, results of clinical pharmacokinetic studies conducted at an earlystage, such as phase I clinical trials, often serve as reference. However, model selection, thenumber of compartments in particular, depends on the sampling frequency and schedule, andit does not necessarily have to match the results obtained in previous studies. Setting too
many parameters as compared to the characteristics of the obtained data should be avoided,
a simplified model may often be more appropriate where the analysis is to be performed
based solely on a small number of drug concentrations collected from individual patients.
On the other hand, simplifying a structural model may not be required if the analysis is to be
performed based on drug concentrations obtained from intensive sampling in a phase I
clinical trial, combined with the drug concentrations obtained from sparse sampling in aphase II or later clinical trial.
(2) Error model
In general, population analysis involves building of a model by setting two error terms,
inter- and intra-individual variability.
If inter-individual variability is described parametrically, the generally used types of error

12distribution are normal and lognormal distributions. The former is also called an absolute
error model, and the latter corresponds to a proportional error model. As for intra-individual
variability, in addition to absolute and proportional error models, a mixed type, or a
combination of the two, is also used. In other words, it is assumed that data for lowconcentration areas have a certain variance, while high-concentration areas have errors
proportional to the concentration included in the measurement data. The appropriate error
model is decided based on model diagnosis such as an examination of the distribution ofparameter estimates, consideration of the analytical methods used in measurements, various
residual plots, and so on (model diagnosis is described in detail in Section 2-3-2).
If there are correlations observed between multiple inter-individual variability parameters,such correlations may be incorporated and analyzed in an appropriate manner. However,there are cases where an apparent correlation is observed as a result of influence factors thatare important as covariates being missed, and it is necessary to examine error and covariate
models in a mutual manner. Inter-individual variability parameters should be characterized
for structural parameters that allow for the estimation of inter-individual variability based ondata.
In addition to the inter-individual variability and intra-individual variability, inter-occasion
variability for individual parameters may also be taken into account in some cases. In sucha case, three error terms, inter-individual, intra-individual, and inter-occasion variability
should be set in a model.
(3) Covariate model
In population analysis, building of an appropriate covariate model leads to theidentification of contributory causes of inter-individual variability. Selection of covariates
should take account of factors considered important from pharmacokinetic and clinicalstandpoints.
Covariates to be incorporated into a model depend on the distribution of observed
covariates. For instance, if all subjects have normal renal function, a relationship between
renal function and clearance cannot be obtained, even for drugs that affect renal excretion.
In an analysis of correlation with pediatric development, the reliability of the analysis result
is low unless data are obtained from subjects that are evenly distributed in each age group.
An analysis of a special factor generally requires the formulation of a study protocol that13

ensures that data suited to the objective of the analysis are collected.
When building a base model, it may sometimes be appropriate to incorporate apparent
covariates that affect the pharmacokinetics and pharmacodynamics into the structural modelin advance. For instance, in a case of a drug for which the dose is determined based on the
body surface area, incorporating the body surface area into clearance helps obtain results
more suitable for clinical application. The methods of selecting covariates utilized in the
final model building process in a nonlinear mixed-effects model include forward selection,
backward elimination, stepwise covariate modeling, full model estimation with all-inclusive
covariates, and so on. Other methods may also be used as long as an appropriate covariate
model may be built in a reasonable manner according to the algorithms used.
When selecting covariates, correlations between different covariates such as “advancedage” and “reduced renal function” require attention. Basically, the effect of covariates on
independent factors should be investigated through analyses. When multiple factors to beconsidered as covariates have a strong correlation, it is preferable to build a model by
selecting factors that are more physiologically and clinically appropriate from the correlating
factors. If two covariates with a strong correlation are included in the dataset, investigation
should be undertaken to see if an analysis that incorporates the influence of a covariate at
the same time does not lead to unstable results, such as significant differences in analysis
results due to differences in the initial values and analysis datasets, or no convergence.
Covariates that are more physiologically and clinically appropriate and are easy to use inhealthcare settings should be selected. It is therefore necessary that the selection ofcovariates should be based on clinical, pharmacodynamic, and pharmacokinetic knowledge
and discussions, coupled with a valid statistical evaluation, rather than being based solely on
statistical significance.
2-3-2. Diagnosis of the validity of a model
After being built, a model should be diagnosed for its validity from the standpoints of thestability and robustness of the analysis results, the plausibility of the parameter estimatesobtained, and predictive performance for reproduction of analyzed data. As the “valid model”
is necessarily determined by the purpose of the analysis, criteria for diagnosing the validityof a model cannot be unambiguously defined. To objectively clarify the validity of theconstructed model, it is necessary to perform appropriate diagnoses according to the14objective.
The stability and robustness of the analysis results may be diagnosed by investigating theeffects of the initial parameter values, the effects of the hypothesis or conditions used formodel building, the effects of excluding individuals with observations or covariate data
deviating from average trends, and so on. Observations and individuals that may have asignificant impact on the results may be identified by their effects on the objective function
value, for instance. Acceptably small estimation error of each parameter estimate may also
be another piece of information to judge the stability and robustness of the analysis result.
In addition, confidence intervals estimated by such methods as bootstrap, jackknifing, andlikelihood profiling, etc. are useful to judge the stability and robustness of analysis results.
Concerning diagnosis methods from which confidence intervals are derived, the bootstrap
method generates a dataset by randomly resampling individual data from the original dataset
used for the analysis, obtains summary statistics of estimates by repeating the process for
obtaining parameter estimates, and compares these with the original parameter estimates toevaluate bias and precision. In general, bootstrap statistics are obtained based on a summaryfrom the runs with successful convergence. When there is a considerable number ofbootstrap runs with abnormal termination, the cause of abnormal termination and its
consequences should be examined.
Plausibility of parameter estimates, in general, can be comprehensively diagnosed byvisual inspections of agreement between model estimates and observations, dependency ofresidual errors on elapsed time from administration or estimates, and, model dependency of
post hoc estimates of inter-individual parameters on time, dose amounts, or primarycovariates, dispersion or skewness of inter- or intra-individual variability, and outliers, aswell as the clinical, pharmacodynamic, and pharmacokinetic validity. It is necessary to notethat random effects which are excessively small and close to zero may suggest a lack ofsufficient information with regard to the inter-individual variability. It is also necessary tocheck the presence of shrinkage, where individual post hoc parameter estimates converge on
the population mean due to insufficient information. Care needs to be taken for evaluating
any parameter-covariate relationships with large shrinkage found in the random effect on theparameter.
For visual evaluation of predictive performance of the model, visual predictive check(VPC), a simulation-based evaluation model taking inter-individual variability and intra-

15individual variability into account, is used. The model simulates model predictive values
corresponding to the analysis dataset based on the parameter estimates. By superimposing
the median and appropriate percentile intervals on the plot of actual observations, visualevaluation is performed. The validity of the structural model or random effect model forinter- and intra-variability is evaluated by comprehensively taking evenly distributed
observations relative to the predicted median and the ratio of observations contained in theprediction intervals into account. It is necessary for appropriate visual comparison to suitably
set the percentiles and bins for constructing prediction intervals. When groups with different
doses or primary covariates are evaluated simultaneously, prediction corrected VPC, etc.
should be considered, although evaluation of predictive performance by each factor bycreating individual VPC plots by group stratified for dose or primary covariates may derivemore useful information in some cases.
Predictive performance may be evaluated on a dataset that has not been used in the modelbuilding (i.e. validation dataset) to rigorously ensure predictive performance on dataobtained from the same population (external validation method). It is, however, associatedwith certain problems including a reduced amount of data available for model building as aresult of data-splitting and the potential uncertainty of the evaluation due to a single roundof data-splitting. To compensate for these shortcomings, cross validation techniques can beused, whereby rounds of data-splitting, model building and predictive performance areconducted repeatedly. With this method, repeating of a series of processes avoids some of
the risk of making an erroneous decision where an extremely biased distribution of validation
data is used for predictive performance, and it also increases the size of data to be used formodel building.
2-4. Model qualification
Model qualification is conducted to ensure that the established population final model has
sufficient performance characteristics to satisfy the purpose of the analysis from an objective
viewpoint based on the results of model diagnosis. As is true of model diagnosis, there is nota general evaluation method that is applicable to all analyses, and it is therefore necessary to
determine an appropriate form of multiple model diagnosis and standards that are requiredfor practical purposes for each analysis based on the characteristics of the drug, from clinical,
pharmacodynamic, and pharmacokinetic viewpoints. It is also necessary to report the results

16

of the chosen model diagnosis method to verify that the model eventually adopted is anappropriate population final model.
3. Model application
The final model built by population analysis and parameter estimates imparts useful
information for setting an appropriate dosage and administration and may also become
useful information for the proper use of drugs. In addition, they may be utilized to estimate
the pharmacokinetic or pharmacodynamic behavior in a certain target population and to planclinical trials. Points to consider for model application are outlined below.
3-1. Predicting pharmacokinetic or pharmacodynamic characteristics of a certain target
population
If a covariate included in a population model is a factor that describes characteristics of acertain target population, it may be useful to investigate the pharmacokinetic or
pharmacodynamic profile in the target population by simulation incorporating error models.
It is necessary to consider that, extrapolation by simulation beyond the distribution ofcovariate in the target population can reduce the reliability of the predicted values as they
are obtained from the tails of the covariate distribution.
3-2. Clinical trial design
Prediction of the clinical trial results through stochastic simulation that takes into accountinformation such as inter- and intra-individual variability and expected distribution ofcovariates in the patient populations in the target clinical trial may be useful in developing a
study plan. For instance, planning and decision-making for a phase III clinical trial may havebenefit of higher levels of certainty by adding model analysis and simulation based on thepharmacokinetic and pharmacodynamic data up to the phase II clinical trial combined withthe results of analysis of the observations obtained from the phase II clinical trial. As
simulation involves variety of approximation and assumptions, it is necessary to investigate
the assumptions used as the premise of the model building, background of the target
population, and prognostic factors for the target disease in advance, and that there are
inherent limitations due to the small amounts of data and information used in the analysis.
In creating simulation data that contain covariates (simulation target population), attention

17

should be made to correlation between covariates. In the case of parametric sampling inwhich the mean and estimates of variance of the covariates adopted are used for sampling ofrandom numbers based on probability distributions such as normal and uniform distribution
to generate new data, sampling based on estimated distribution of limited amounts of data
can cause prediction bias. There are also possibilities that covariance between parameters
and interaction between parameters and covariates may not be reproduced appropriately.
4. Reporting and providing information
4-1. Population analysis report
The population analysis report should be prepared in accordance with the descriptions in
the analysis plan for population analysis. It is desirable that the population analysis reportcontains the particulars outlined below. However, simplified population analysis report may
be appropriate in some cases, such as where the results of population analysis are includedin the clinical study report, or depending on the objective of the population analysis. Whenthe analysis results are to be used for regulatory submission, it is necessary that quality
control and assurance for the data management, analysis and the report should be conductedin an appropriate manner.
1) Summary
A concise overview that summarizes the population analysis should be presented. The
objective of analysis, methods, results, and sufficient information to explain the key
conclusions should be included in the overview.
2) Introduction
In the introduction section, concise descriptions of background information of theinvestigational drugs, and the positioning of the present analysis in the development of theinvestigational drugs should be provided.
3) Objective of population analysis
The objective of the population analysis should be stated. If there is more than one
objective, it is desirable that primary and secondary objectives be explicitly stated.
4) Method of population analysis
The overall methods such as clinical trials covered by population analysis, data used inanalysis, and methods of data analysis should be described as described below. Any change

18

from the analysis plan should be indicated.
Clinical trials covered by population analysis
The clinical trials covered by population analysis should be identified, and then concise
descriptions of each of the clinical trials should be provided, including the study designs andcontents, target subjects and number of subjects, characteristics of subjects, investigational
drugs, and their dosage and administration.
Data used in analysis
Descriptions of the drug concentration and other clinical endpoints investigated in the
population analysis, information on the data collection for drug concentrations and efficacy
endpoints, including number of sampling points, sampling times, and endpoint measurement
times, and variables investigated as covariates in the population analysis should be provided.
For derived covariates such as creatinine clearance, the calculation method used should bepresented. Handling of missing data, values for concentrations below the LLOQ, and outliers
should be described.
Methods of data analysis
Particulars about the population analysis should be provided, including the method used,software and its version, compiler, operating system (OS) and other operating environment
information, estimation method, information concerning components of the model andrelevant assumptions, such as parameters and distribution of random effects, overall modelbuilding methods, covariate model building procedures (stepwise covariate modeling, fullmodel estimation with all-inclusive covariates, etc.), selection criteria for covariates (suchas p value), and the methods for model diagnosis and model qualification.
5) Analysis results
As the analysis results, the following, accompanied by a summary using appropriate
figures or tables should be described.
Characteristics of population and data as target of analysis
The number of subjects, number of data measured, time-drug concentration plots, profile
of data measured, summary statistic of demographic variables and other covariates, resultsof handling of outliers and missing values, and so on.
Results of model building
The process of reaching a decision on the final model, showing the rationale for thedecision clearly, the parameter estimates with their standard error and diagnosis plots for the19base and final models. As for covariate selection, it is desirable to illustrate them bypresenting plots that show the correlation between covariates and plots of the empirical
Bayes estimates of the parameters versus covariates. The results of model qualification for
the final model should be presented.
6) Discussions and clinical applications
The validity and clinical significance of the model building and estimates should be
discussed. If the final model that has been built is applied to administration planning or otherpurposes, it is recommended that the results of simulation based on the final model as wellas its reliability be illustrated in figures.
7) Appendix
A report including model files describing the structures of the base and final models andtheir outputs, and the datasets used in the analysis (the representative portion of the dataset
for a few selected subjects may be used) should be attached as an appendix. Figures andtables not used in the main part of the report may be included in the appendix.
4-2. Regulatory submission
When the results of population analysis are used for regulatory submission, the population
analysis report should be provided and Section 2.7, Clinical Summary, of the Common
Technical Document (CTD) should be prepared based on the population analysis report,
according to a series of relevant notifications, etc. In the clinical summary, the following
information should be described; the analysis data, model building procedures, final models
for pharmacokinetics, pharmacodynamics or exposure and response relationships,
population parameter estimates obtained, the results of simulation performed based on themodel, using figures and tables, and the model diagnosis and qualification results.
Information on the population analysis should be appropriately reflected if necessary in thedescription of the pharmacokinetic and pharmacodynamic profiles of the drug in the relevant
sections of the CTD.
For population analysis for which electronic data are subject to submission in filing for
approval, electronic data should be submitted according to a series of relevant notifications.
4-3. Providing information in package insert
When the results of population analysis provide useful information for appropriate use of20

the drug, population analysis information should be included in the "Pharmacokinetics" orother appropriate sections of the package insert. When the simulation results provide
rationale for the important caution, descriptions of caution should be included in an
appropriate part under the "Precautions" section of the package insert, indicating that the"Pharmacokinetics" section should be referred to.
When preparing these descriptions, information should be provided with the prospect thatthe population analysis results will be used in healthcare settings, for example, when
information on pharmacokinetics in the target patient population of the drug is provided,
when information on pharmacokinetic or pharmacodynamic variable factors is provided, orwhen information that serves as the rationale for dose adjustment for a certain target
population is provided.

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5. Relevant guidelines and documents
This guideline shows the general guidance that is scientifically plausible for conducting
population analysis. While guidelines and documents that have already been notified provide
descriptions regarding population analysis, this guideline integrates and organize contentsof such guidelines, and furthermore, incorporates the latest knowledge and concepts as ofpresent.
ICH guideline
1) PAB/PCD Notification No. 227 March 20, 1995 Clinical Safety Data Management:
Definitions and Standards for Expedited Reporting (ICH E2A)
2) PFSB/SD Notification No. 0328007 March 28, 2005 Post-approval Safety DataManagement: Definitions and Standards for Expedited Reporting (ICH E2D)
3) PFSB/ELD Notification No. 0916001, PFSB/SD Notification No. 0916001 September
16, 2005 Pharmacovigilance Planning (ICH E2E)
4) PAB/PCD Notification No. 335 May 1, 1996 Structure and Content of Clinical Study
Reports (ICH E3)
5) PAB/PCD Notification No. 494 July 25, 1994 Dose-Response Information to Support
Drug Registration (ICH E4)
6) PMSB Notification No. 739 August 11, 1998 Handling of Data on Clinical Trials on
Drugs Performed in Foreign Countries, Attachment PMSB/ELD Notification No. 672
Ethnic Factors in the Acceptability of Foreign Clinical Data (ICH E5)
7) MHLW Ordinance March 27, 1997 Guideline for Good Clinical Practice, Attachment
PAB Notification No. 430 Enforcement of the Ordinance Regarding Good Clinical
Practice (ICH E6)
8) PAB/NDD Notification No. 104 December 2, 1993 Studies in Support of Special
Populations: Geriatrics (ICH E7)9) PMSB/ELD Notification No. 380 April 21, 1998 General Considerations for Clinical
Trials (ICH E8)
10) PMBS/ELD Notification No. 1047 November 30, 1998 Statistical Principles for Clinical
Trials (ICH E9)11) PMSB/ELD Notification No. 1334 December 15, 2000 Clinical Investigation on
Medicinal Products in the Pediatric Population (ICH E11)2212) PFSB/ELD Notification No. 0109013 January 9, 2008, PFSB/SD Notification No.
0109002 Definitions for Genomic Biomarkers, Pharmacogenomics, Pharmacogenetics,
Genomic Data and Sample Coding Categories (ICH E15)
13) PFSB/ELD Notification No. 0707-3 July 7, 2009 Partial Revision of the Notification
Concerning Materials to Be Attached to the Approval Application Form upon Approval
Application for Marketing of New Drugs (ICH M4 and M8)14) PSEHB/PED Notification No. 0118-1 January 18, 2018 Genomic Sampling andManagement of Genomic Data (ICH E18)
15) PSEHB/PED Notification No. 1227-5 December 27, 2017 Addendum: Clinical
Investigation of Medicinal Products in the Pediatric Population (ICH E11 [R1])
Regional guidelines and documents
1) PFSB/ELD Notification No. 796 June 1, 2001 Note on Clinical Pharmacokinetic Studies
of Pharmaceuticals
2) PFSB/ELD Notification No. 0928010 September 28, 2007 Basic Principles on Global
Clinical Trials3) PFSB/ELD Notification No. 0711-1 July 11, 2013 Guideline on Bioanalytical Method
Validation in Pharmaceutical Development
4) PFSB/ELD Notification No. 0401-1 April 1, 2014 Guideline on Bioanalytical Method(Ligand Binding Assay) Validation in Pharmaceutical Development
5) PFSB/ELD Notification No. 0620-6 June 20, 2014 Basic Principles on Electronic
Submission of Study Data for New Drug Applications
6) PFSB/ELD Notification No. 0427-1 April 27, 2015 Notification on Practical Operationsof Electronic Study Data Submissions
7) PSEHB/ELD Notification No. 1225-10 December 25, 2015 Guideline for
Pharmacokinetics and Pharmacodynamics of Antimicrobials8) PSEHB/ELD Notification No. 0723-4 July 23, 2018 Guideline on Drug Interaction for
Drug Development and Appropriate Provision of Information
Regional guidelines and documents (package inserts)
1) PSEHB Notification No. 608-1 June 8, 2017 Instructions for Package Inserts of
Prescription Drugs, etc.

232) PSEHB/SD Notification No. 608-1 June 8, 2017 Points to Consider regarding theInstructions for Package Inserts of Prescription Drugs, etc.
Overseas guidelines and guidance1) FDA: Guidance for Industry: Population Pharmacokinetics (1999.2)
2) FDA: Guidance for Industry: Exposure-Response Relationships – Study Design, Data
Analysis, and Regulatory Applications (2003.5)
3) FDA: Guidance for Industry: End- of-Phase 2A Meetings (2009.9)
4) FDA: White Paper: Challenge and Opportunity on the Critical Path to New Medical
Products (2004.3)
5) FDA: Guidance for Industry: General Clinical Pharmacology Considerations for
Pediatric Studies for Drugs and Biological Products (draft, 2014.12)
6) EMA: Guideline on the clinical investigation of the pharmacokinetics of therapeuticproteins (2007.7)
7) EMA: Guideline on reporting the results of population pharmacokinetic analyses(2008.1)
8) EMA: Guideline on the use of pharmacokinetics and pharmacodynamics in thedevelopment of antimicrobial medicinal products (2017.2)

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6. Glossary
Chapter 1
1) Population analysis: An analysis method to describe the pharmacokinetic and/or
pharmacodynamic properties of a drug for a typical individual (i.e. fixed effects) and, in
addition, the variability in the population (i.e. random effects).
Chapter 22) Structuralmodel: A mathematical

model

to

describe

pharmacokineticorpharmacodynamic responses.
3) Inter-occasion variability: Variability arising from changes in parameters for a subject
during the evaluation period. It is generally modeled as a random effect between periods.
4) Intra-individual variability / Within-subject variability: Variability arising from changes
in parameters within a subject. It may be modeled as a fixed effect dependent on period
or covariates, or as a random effect between periods (i.e. inter-occasion variability).
Where intra-individual variability is not modeled, it will not be distinguished fromresidual variability.
5) Inter-individual variability / Between-subject variability: Variability arising fromchanges in parameters between subjects. It is variability from the population mean
parameter which is usually modeled as a random effect.
6) Outlier: For pharmacokinetic or pharmacodynamic observation: an observation thatappears to be largely outside the expected distribution derived from the model. For acovariate of an individual: a value that appears to be largely outside the expected
sampling distribution of the covariate in the population.
7) Covariate: A factor that has influences over pharmacokinetic and pharmacodynamic
responses. Covariates include a subject’s intrinsic factors (such as body weight, sex, orage) and extrinsic factors (such as concomitant drugs or smoking status) of the subject,conditions of the drug treatment (such as formulations, dosage and administration, anddietary conditions), or other factors that potentially influence the response.
8) Sensitivity analysis: In population analysis, sensitivity analysis primarily refers to an
investigation of the dependence of analysis results on changes in data or parameters forthe purpose of evaluating the stability and robustness of the model.

259) Base model: The structural and statistical model that best describes the data usually inabsence of covariates, which is typically established before covariate exploration.
10) Final model: The model that provides the best overall description of the data and thatappropriately addresses the objectives of the analysis. Typically, it is a model that has
been established as a result of covariate model building based on the base model.
11) Error model: A model that describes variability from a population mean or model
estimate, such as inter- or intra-individual variability.
12) Residual error: Difference between the observation and model estimate forpharmacokinetic or pharmacodynamic responses.
13) Individual parameters: The estimates of each subject’s parameter set given the model
parameters and the data, obtained after the estimation of the fixed effect parameters by
empirical Bayes estimation conditional on a fitted model. They are also called empirical
Bayes estimates or post hoc parameter estimates.
14) Forward selection: A method of covariate exploration where each parameter-covariate
combinations is added in turn as a predictor for a parameter in the base model. The
covariate that gives the best fit of the data according to some criteria is retained andtaken forward to the next step. In the following steps all remaining covariates are tested
until no more covariates meet the pre-defined level of statistical significance for being
included into the model.
15) Backward elimination: A method of covariate exploration where each parametercovariate combination is removed in turn one at a time in ascending order of effect fromthe full model involving all the covariates that have been preliminarily selected. The
process is repeated until the effects of all of the covariates contained in the model meetthe pre-defined level of statistical significance for being retained into the model.
16) Stepwise covariate modeling: A method of covariate exploration where tests are runsequentially by adding and removing covariates based on a pre-defined statisticallysignificance level to obtain the final covariates.
17) Full model estimation with all-inclusive covariates: A method of covariate exploration
where the influence on population parameters of pre-defined covariates of interest isevaluated simultaneously in the base model that involves all the covariates. The degreeof the effect of a covariate is determined based on the confidence interval for theestimated effects by the covariate.

2618) Bootstrap: A method to evaluate estimation errors and biases of parameter estimates formodel diagnosis and model evaluation. Bootstrap datasets are generated by repeatedly
resampling individuals with replacement from the original data. The model is then fit toeach bootstrap dataset to obtain the sampling mean and distribution of the parameter
estimates of the model.
19) Random effect: The stochastic component that describes the deviation from the typicalparameter value or response.
20) Shrinkage: A phenomenon in which post hoc individual parameters (empirical Bayes
estimates) shrink around the population mean ( -shrinkage) or the distribution ofresidual error shrinks toward zero ( -shrinkage), due to excessive random effectparameters.
21) Predictive check: A simulation-based evaluation method used for a comprehensive
evaluation of the predictive performance of a model. It may be used for a comparison
with observations or the verification of consistency in properties between observed andmodel derived data in a visual manner (i.e. visual predictive check; VPC), for a numericalcomparison (i.e. numerical predictive check; NPC), or for a statistical comparison (i.e.
posterior predictive check; PPC), etc.
22) External validation: One of the most rigorous methods for model diagnosis. The
predictive performance of the model obtained by using a dataset that is based on datafrom other studies but not used in the model building is evaluated. On the other hand, amethod of reusing the data used for model building by means of division and resampling
and then evaluating the predicting performance of the model obtained is called internal
validation.
23) Cross validation: An internal validation method used for model diagnosis, in which amodel is built based on an index dataset obtained by data-splitting, while the predictive
performance of the model is evaluated using the remaining validation dataset. The
process is repeatedly performed.
Chapter 324) Parametric sampling: Generates parameters or observations by using random numbers
based on the probability distribution obtained from the estimates of mean and variance.

2725) Stochastic simulation: Simulation that generates random numbers and obtains the
distribution of parameters of many subjects while factoring in errors.

28