写
○
事

務

連

絡

令和２年６月８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「医薬品の曝露-反応解析ガイドライン」の英文版について「医薬品の曝露-反応解析ガイドライン」については、「「医薬品の曝露-反応解
析ガイドライン」について」（令和２年６月８日付け薬生薬審発 0608 第４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願います。

The following English translation of Japanese Guideline is intended to be a referencematerial to provide convenience for users. In the event of inconsistency between theJapanese original and the translation, the former shall prevail.

Provisional Translation
PSEHB/PED Notification No. 0608-４
June ８, 2020

Guideline for Exposure-Response Analysis of Drugs
Contents
1

Introduction
1.1 Background and objectives

1.2 Scope
2 Exposure-response relationship in drug development
2.1 Exposure-response evaluation
2.1.1
Dose-exposure-response relationship2.1.2
Study design and exposure-response relationship
2.1.3
Data for analysis
2.1.4
Exposure measures
2.1.5
Response measures
2.1.6
Factors of influences on the exposure-response relationship2.2 Methods of exposure-response evaluation
2.2.1
Initial analysis by visualization
2.2.2
Analysis of the exposure-response relationship2.3 Utilization of exposure-response evaluation
2.3.1
Utilization at the nonclinical stage
2.3.2
Utilization in early phase clinical development stage
2.3.3
Utilization in late phase clinical development stage
2.3.4
Utilization in post-marketing stage and for extending indications
3 Reporting and providing information
3.1 Analysis report
3.2 Regulatory submission
3.3 Providing information in package insert
4
5
6

Relevant guidelines and documents
Glossary
Appendix
6.1 Example of a figure generated by exposure-response analysis
6.2 Examples of models used in exposure-response analysis
11Introduction

1.1

Background and objectives

In drug development, investigation of the relationship between exposure and response,
in addition to investigation of the relationship between dose and response (mainly
efficacy or safety), is important to obtain useful information to determine the dosage andadministration more efficiently in clinical studies at each stage of development, to build
the study design, and to provide information to those involved in clinical practice.
Developing models based on the relationship between exposure and response from anearly stage development, then the results of exposure-response analyses using an updated
model based on new data and scientific findings obtained from each development stage
are expected to be used for quantitative decision-making that contributes to strategy
planning of the next phase of development, and exposure-response analysis is therefore
widely used in drug development. In recent years, it is expected that analyses of the
exposure-response relationship and subsequent simulations of clinical responses based on
results of the analyses will contribute to improve the likelihood of success in confirmatory
studies in various disease areas. In addition, it is expected that exposure-response analyses
will be further utilized as one of the methods for estimating the proper dosage andadministration from limited clinical study results, as well as avoiding the conduct of
unnecessary clinical studies for the development of medicinal products targeted for
populations and diseases for which clinical studies are not feasible, as represented inchildren and orphan diseases.
The purpose of this guideline is to show a basic concept and points to consider wheninvestigating the relationship between exposure and response as general guidance that isscientifically valid at present, so that exposure-response analyses and their utilization canbe implemented properly for drug development. Points to consider concerning thedescriptions related to exposure-response analyses in application materials to besubmitted at the time of a market approval submission are also mentioned. Each item
listed in this document has been examined based on the current scientific knowledge;
however, if new knowledge is obtained with the future progress of research in theories
and applications, it is necessary to act flexibly based on scientific judgment.
In addition, this guideline is intended to promote common understanding among drug
developers, healthcare professionals involved in clinical trials, and regulatory authority
personnel, etc. who are involved in consideration using exposure-response analyses andthe interpretation of the results thus obtained, and to recommend the appropriate
implementation of exposure-response analyses during drug development.

2

Scope1.2This guideline should be applied to exposure-response analyses based on the results ofindividual clinical studies or those generated from the integration of results from multiple
clinical studies for drug development. Exposure-response analyses are performed forvarious reasons from an early stage of development, and discussion concerning theclinical study design, etc. in clinical trial consultations, etc. is anticipated between drug
developers and regulatory authority personnel. This guideline describes the usability of
exposure-response analyses in each clinical development phase, as well as examples of
exposure-response analysis utilization throughout drug development. However, thisguideline does not define novel requirements for drug development or market approvalsubmissions.
In this guideline, “exposure” is used as a broad term encompassing drug concentrationin plasma and other biological fluids or various pharmacokinetic parameters (AUC, Cmax,
Ctrough, etc.).
2

Exposure-response relationship in drug development

2.1

Exposure-response evaluation

2.1.1

Dose-exposure-response relationship (see also Appendix 6-1)

The relationship between dosage and response after administration of a drug is
described as the relationship between the dose, exposure observed as systemic drugconcentration or drug concentration at the site of action, and the pharmacodynamic effectcaused by exposure to the drug and the resulting efficacy or safety (Figure 1).

Exposure
Dose

Blood drugconcentration

Response
Response
Drug
concentration
at the site ofactionPharmacodynamic
effectEfficacy and safety

Pharmacokinetics
Pharmacodynamics

Figure 1

Relationship between dose, exposure, and response

Building a model that reveals these dose-exposure-response relationships is useful forthe quantitative assessment and prediction of responses based on exposure. That is,understanding and illustration of the dose-exposure-response relationship using models
are useful to explain the cause of differences in responses due to differences in exposure
by inter-individual variability during the course of absorption, distribution, metabolism,3and excretion, or other differences in responses noted despite similar exposure. However,since models tend to depend on the characteristics of a drug and the data and assumptions
used to construct the model, it is important to use data in line with the scope of use andpurposes of the model when examining exposure-response relationships. Regarding
prediction using models, it should bear in mind that prediction reliability differs greatly
between a prediction within the range of existing data and prior information and a
prediction that is out of the range of existing data and prior information. In addition, if
the dose-exposure-response relationship is already clarified by existing clinical data,
useful information may be provided to facilitate a more rational development strategy
when estimating the dosage and administration for new indications or response in other
patient groups.
Furthermore, consideration of the dose-exposure-response relationship lead to grasp
and understand the efficacy and safety of a drug more precisely than if only the doseresponse relationship is considered. When analyzing data on exposure-response
relationships, it is important to use response-related data in an appropriate range of
exposure according to the purpose.

2.1.2

Study design and exposure-response relationship

When performing a model analysis to clarify the exposure-response relationship, theanalysis set for the analysis of the endpoint defined in line with the study objective thatwas prespecified in the protocol, measures to be evaluated and the summarization method,
handling of interim events such as dropouts and missing data on observed values, and themethod of missing data imputation should be considered based on the design of theclinical study subject to analysis. Therefore, when planning a clinical study, it is importantthat persons in charge of the exposure-response relationship (clinical pharmacologist,
pharmacometrician, etc.) have discussion with clinician and biostatistician and otherrelevant people who are involved in the study, and clarify the rules on the handling ofcovariates, prognostic factors, confounding factors, missing values, etc. related to the
exposure-response relationship before developing the analysis plan.
An analysis plan for exposure-response analysis should be prepared at an appropriate
time before the start of analysis. Regarding points to require attention when preparing an
exposure-response analysis plan, please refer to the relevant section of the Guideline on
Population Pharmacokinetic and Pharmacodynamic Analysis.
2.1.3
Data for analysis
The reliability of the data obtained from the analysis set to be analyzed that is used for4the evaluation of exposure-response relationship has an influence on its predicting
performance. It is therefore important that the data representing the group to be analyzed
should be data with guaranteed reliability that were obtained from a study appropriately
and adequately managed in compliance with various laws or guidelines.
Criteria on inclusion/exclusion of data used for analysis should be determined prior to
analysis and specified in appropriate documents. Regarding points to consider whencollecting data on drug concentration as a measure of exposure, as well as data on
efficacy measures and safety evaluation as a measure of response, please refer to therelevant section of the Guideline on Population Pharmacokinetic and Pharmacodynamic
Analysis.
In interpreting the results of exposure-response analysis, it is preferable that theanalysis set which is used for evaluation of the exposure-response relationship is the sameas the full analysis set in the protocol. Data to be analyzed that are used for model-based
exposure-response analysis may not exactly match the data used for statistical analysis
due to the fact that exposure-related data are missing because no blood sampling was
performed, etc. If these analyses yield different interpretations, consideration of thedifferences in the composition of each dataset and their influence is useful to understandthe results.
Occasionally, the exposure-response relationship can be detected more accurately by
selecting a specific subject groups and analyzing, but it should be carefully interpreted as
there is a possibility that a different exposure-response relationship than that from theentire population is obtained. Data from subjects who received a placebo and baseline
data are useful to understand changes over time in the response measures when the drug
is not administered, to grasp the distribution of the response measures in the analysis set,or to analyze the placebo effect, etc.
In general, when the exposure-response relationship is modeled by integrating resultsfrom multiple clinical studies conducted through clinical development, it is necessary to
explain the validity of such integration by reviewing the comparability of the studies, thepossibility of their integration, and the differences in populations and assessment
measures among the studies in the analysis reports.
2.1.4
Exposure measures
The measures of exposure are selected in consideration of the relevance to responsesuch as efficacy and adverse events, pharmacokinetic features, action mechanism, etc.
When evaluating the relationship between average exposure and response over a certain
period, the area under the curve (AUCss), average concentration (Cavg, ss), maximum

5concentration (Cmax, ss) or trough concentration (Ctrough, ss) at the steady state is often used
as a measure of exposure. On the other hand, as a measure of exposure related to responsethat changes over time within dosage intervals, drug concentration-time profile that ismatched with response measures in terms of measurement time is useful. Active
metabolites are also useful for exposure measures in some instances. Moreover, as ameasure of exposure, not only observed values but also values estimated using the
population analysis method may be used in some instances.
2.1.5

Response measures

In addition to quantitative data such as measurement values, qualitative data such as
binary data (effective or ineffective, etc.), ordered categorical data, etc. that are used inclinical studies can also be used as response measures. For response measures,
observation values or endpoints characterizing efficacy or safety should be selected bytaking account of the purpose of the analysis. For evaluation of the efficacy or safety of adrug, analysis using clinical endpoints or surrogate endpoints as response measures isuseful. However, in early stages of the clinical development, exploratory analyses using
biomarkers as response measures may also be useful for decision-making in clinicaldevelopment. Furthermore, as a measure of the response for safety, the presence orabsence of adverse events is occasionally used as binary data. The occurrence of adverse
events can provide even more useful information if it is analyzed by classificationaccording to seriousness, severity, whether or not they are adverse events of special
interest, etc., or analyzed with use of quantitative data such as clinical laboratory test data,
depending on the purpose of the analysis. In principle, missing data on response measures
should be handled in the same way as efficacy analysis and safety analysis for eachclinical study.
2.1.6
Factors of influences on the exposure-response relationship
Exploration of covariates as factors influencing exposure and response measures isimportant to better understand the exposure-response relationship and predict
response more precisely based on exposure information. Investigation into covariates
influencing exposure measures is often undertaken when a population
pharmacokinetic model is built before exposure-response analysis. However, if there arecovariates influencing both exposure measures and response measures, it is necessary toconsider these covariates during exposure-response analysis, taking potential
confounding into account. In general, covariates to be incorporated in the model should
be determined by allowing for the magnitude of their influence, observability in the6clinical setting, etc., and it is desirable to avoid incorporating excessive covariates.
Concerning points to consider when incorporating covariates into models, please refer tothe relevant section of the Guideline on Population Pharmacokinetic andPharmacodynamic Analysis.
2.2 Methods of exposure-response evaluation
2.2.1
Initial analysis by visualization
Understanding the characteristics of data accurately before performing exposureresponse analysis is a starting point for conducting the analysis adequately. Concerning
evaluation of the exposure-response relationship, prior to the analysis, an exploratory
graphical analysis is performed, and an overview of observed data is obtained. This
provides the information for the hypothesis to be considered in the analysis. It is importantto calculate summary statistics for each factor such as covariate candidates, to visualize
the data by graph, and understand the shape and characteristics of distribution. Dependingon the objective of the analysis, it may be sufficient to evaluate the exposure-response
relationship only by graphical analysis.
2.2.2
Analysis of the exposure-response relationship (see also Appendix 6-2)
There are many different models to analyze exposure-response relationship, dependingon the nature of response measures, the type of data, and the purpose of analysis. In
addition to the empirical models, which are developed based on existing data,mechanism-based models are used. Often, models are linear or nonlinear where exposure
measures are parameters such as AUCss, Cavg, ss, Cmax, ss, and Ctrough, ss. Response measures
are clinical responses related to efficacy or safety, but if there is a time lag between
exposure
andclinicalresponse,time-course
modelsofpharmacokinetics/pharmacodynamics, such as indirect response models, are also
included. If it is necessary to study the long-term effect of a drug on disease that
progresses over a long period of time, it may also be necessary to develop a model thattakes the natural progression of the disease into consideration. In particular, when theempirical model is applied, the model should be selected based on the purpose of
exposure-response analysis, and it is generally desirable to select a simpler model among
those that can achieve the purpose of analysis.
In recent years, as mechanisms of pathogenesis have been clarified in various disease
areas, modeling including the disposition of substances in body, such as the productionand elimination of endogenous substances related to pathological conditions, has been
performed. In some cases, clinical responses may be simulated more precisely by7

developing a system pharmacology model. On the other hand, when mechanism-based
analysis is performed, the model could become complicated and parameters that cannot
be determined directly from clinical studies are sometimes required; therefore, it isrecommended to use a model fit for the purpose of analysis.
2.2.2.1

Incorporation of covariates

Candidates of covariates to investigate should be chosen by taking trends of dataobtained from the exploratory graphical analysis and clinical/physiological significanceinto account.
When the covariate is a continuous variable, linear, power, exponential, etc. models areused as regression models. In such a case, values are usually standardized with therepresentative values of the covariates, and used as explanatory variables.
When the covariates are discrete values, the effects of the covariates are described asadditional changes or proportional changes compared to a control group in each covariate
stratum.
If changes over time in covariates are important, incorporation of time-varying
covariate values should be considered.
For details, please refer to the Guideline on Population Pharmacokinetic andPharmacodynamic Analysis.
2.2.2.2

Model diagnosis and qualification

When using a model for exposure-response analysis, it is necessary to perform model
diagnosis and qualification according to the intended use. The stability and robustness of
the analysis results, the validity of the parameter estimates obtained, predicting
performance, etc. are evaluated referring the intended use of the model.
For details, please refer to the Guideline on Population Pharmacokinetic andPharmacodynamic Analysis.
2.3 Utilization of exposure-response evaluation
A correct understanding of the exposure-response relationship provides useful
information for planning of an appropriate clinical study design, scientific interpretationof clinical study results, and finding optimal dosage and administration in various patient
populations, thereby facilitating strategic and efficient drug development. Exposureresponse models are continuously refined and updated at each phase of clinicaldevelopment from the nonclinical to the post-marketing stage, so that they can contributeto efficient development planning at a subsequent stage, as well as the appropriate use of8

drugs in clinical settings.
2.3.1

Utilization at the nonclinical stage

Appropriate exposure-response analysis based on results of toxicokinetics ofnonclinicalsafetystudies,
nonclinicalpharmacokinetic
studies,andpharmacokinetic/pharmacodynamic studies in animal models of the disease to predict theclinical response can help to confirm the dependence of safety and efficacy responses on
drug exposure, and may contribute to identify the most predictable measures of exposure.
The selection of biomarkers to be measured in clinical studies is enabled by examining
the relationship between exposure and pharmacodynamic actions, including promotion or
inhibition of the production of bioactive substances, or receptor occupancy of the target,
etc. that are expected from the mechanism of action. When the clinically relevant
exposure is estimated, species differences such as between human and animal species inrelative pharmacodynamic intensity, etc. should be considered. After the clinically
relevant exposure is estimated, an informative suggestions may be obtained for dosage
and administration to be studied in subsequent clinical studies when combined withmethods for predicting human exposure such as physiologically based pharmacokinetic(PBPK) model or an allometric scaling.
Clinical and nonclinical information regarding preceding drugs with the samemechanism of action may be useful for exposure-response analyses. An application ofquantitative systems pharmacology (QSP) modeling could also help to predict clinicalresponses in patients, when it is difficult to predict from responses in animal models or
healthy volunteers.
2.3.2
Utilization in early phase clinical development stage
When population pharmacokinetic/pharmacodynamic analysis is performed based ondata obtained early phase clinical studies, including first-in-human (FIH) studies inhealthy volunteers or clinicalstudiesinpatients, the establishedpharmacokinetic/pharmacodynamic model could be utilized to confirm the expectedmechanism of action and necessary level of exposure as well as the dosage need toachieve the targeted pharmacodynamic response. In addition, simulation ofpharmacodynamic and clinical responses in the target patient population may serve as a
guide to consider appropriate inclusion criteria, dosage and administration to be studied,and the necessity of dose adjustment in subsequent clinical studies. Based on the dataobtained from the proof-of-concept (POC) study, in depth understanding about therelationship between exposure, pharmacodynamic, and clinical response in the target9patient population could be achieved. A highly predictable model for clinical response
can be constructed by incorporations of significant covariates, the natural progression of
the disease, time-course in the clinical response, and placebo effect on clinical response,
etc. into the exposure-response model. Exposure-response models that are appropriately
constructed based on data collected before the conduct of late phase clinical studies areuseful to determine the appropriate dosage and administration, target patient population,
number of patients, duration of the study, evaluation time points, etc. in the late-stage
clinical study. For example, setting enrolment criteria or stratification factors for clinicalstudies by taking covariates that have been found to influence efficacy or safety into
account should be useful for efficient drug development through providing appropriate
design of late phase clinical studies.
Aside from conducting a head-to-head comparison against competing drugs with thesame mechanism of action in a POC study for differentiation purpose, comparison can be
possible by incorporating differences in relative intensity of pharmacodynamic response
obtained from public-domain literature into the model. It is also possible to integrate
literature information about the responses of competing drugs by model-based metaanalysis in order to confirm the relative position.
Analyses aimed at the evaluation of the relationship between drug concentration andchanges in QT/QTc interval are mentioned in “The Clinical Evaluation of QT/QTc
Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs
Questions & Answers (R3)” (ICH-E14 Guideline Q&A) (Administrative Notice dated
May 23, 2017). For details, please refer to the ICH-E14 Guideline Q&A.
2.3.3
Utilization in late phase clinical development stage
In general, it becomes possible to establish and update a more reliable exposureresponse model based on data from late phase clinical studies including dose-response
and confirmatory studies, which can be applied to predict clinical responses in morediverse patient populations. Establishment of exposure-response models for efficacy andsafety also make it possible to select scientifically the best dosage and administration inview of balancing the both aspects. In case of reliable prediction of clinical response canbe simulated by the dose-exposure-response relationships revealed by an appropriately
validated model, the simulated response derived from the model can be used as therationale for setting the dosage and administration to be studied in confirmatory studies.
For example, dosage and administration that was not directly investigated in a doseresponse study can be selected as dosage and administration studied in confirmatory
studies based on the model-derived prediction of responses within the range of the10

investigated doses in the dose-response study. It should be noted, however, the reliabilityof the model-derived prediction at a dose outside the dose range of investigated in thedose-response study is generally low. Moreover, when model-derived prediction ofclinical response are used for selecting the dosage in a confirmatory study, it is also
desirable to model the exposure response relationship from data obtained in a wellmanaged controlled study involving randomization and blinding.
Furthermore, an appropriately validated exposure-response model quantitatively
predicts clinical response in subpopulations stratified by significant covariates, so that it
can provides a scientific rationale for the necessity to adjust the dosage and administrationin the subpopulation. Similarities in dose-exposure-response relationships obtained indifferent ethnics or regions can suggest similarities in clinical response between theethnics or regions. The similarity in dose-exposure-response relationships demonstrated
by the model may be useful to justify study design, including the selection of dosage andadministration in multi-regional clinical trials.
2.3.4
Utilization in post-marketing stage and for extending indications
In the post-marketing stage, sharing the reliable exposure-response model that wasestablished during the course of drug development with healthcare professionals
appropriately is expected to greatly contribute to the promotion of proper use of drug andpersonalized medicine in clinical settings. In drug development for a new formulation or
an extension of indications in the post-marketing stage, the exposure-response model may
presumably be applied (but are not limited) to the following purposes.
 To make it possible reliably to predict clinical response at any doses or exposure
within the range of dose or exposure to be studied and thus to set the dose or exposure
range to be expected no clinical relevant difference or requiring adjustment of dosage
and administration.


To predict clinical response based on the simulated exposure under the new dosageof new formulations developed for pediatric or for modified release etc. and the newdosage and administration developed in the post-marketing stage.



To utilize for clinical study design based on the predicted clinical response in a newpatient population, such as pediatrics, or other therapeutic uses by the establishedexposure-response model based on data from adults patients for the initial indications(It is recommended to establish the exposure-response relationship in the preceding
development in adults patients if the application of the model to predict the clinicalresponse in pediatric patients is planned to utilize for study design). The similarities11

in clinical response should be demonstrated by comparing the exposure-response
relationships in adult and pediatric populations.
3 Reporting and providing information
3.1 Analysis report
This section specifies points to be noted for an analysis report etc. when a model-based
exposure-response analysis is performed. The analysis report should be prepared basedon the analysis plan. It is desirable that the analysis report contains the particulars outlined
below. However, a simplified analysis report may be appropriate in some cases, such aswhere the results of exposure-response analysis are included in the clinical study report,
or depending on the objective of the exposure-response analysis. When the analysis
results are to be used for regulatory submission, it is necessary that quality control andassurance for the data management, analysis, and the report should be conducted in an
appropriate manner.
1) Summary
A concise overview that summarizes the exposure-response analysis should bepresented. The objective of analysis, methods, results, and sufficient information to
explain the key conclusions should be included in the summary.
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
the investigational drugs should be provided.
3) Objective of exposure-response analysis
The objective of the exposure-response 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 exposure-response analysis
Clinical studies subject to exposure-response analysis, data used for analysis, and theoverall methodology such as data analysis methods should be described. If there are anychanges to the documented analysis plan, the changes and reasons should be indicated.
 Clinical studies subject to exposure-response analysis
The clinical studies subject to exposure-response analysis should be identified. For
each of these studies, information about administration such as the study design and12details, target subjects and number of subjects, characteristics of subjects, investigational
drugs, their dosage and administration, etc. should be summarized.
 Analytical data
Information about the endpoints related to the clinical response examined in the
exposure-response analysis, time points of sample collection, time points of measurementof exposure or response measures, etc., as well as measures investigated as covariates inthe exposure-response analysis should be described.
 Data analysis methods
Particulars about the analysis should be provided, including the method used, softwareand 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 model building methods, covariate model building procedures (stepwise
covariate modeling, full model estimation with all-inclusive covariates, etc.), selection
criteria for covariates (such as 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, profile of data measured, summary
statistics of demographic variables and other covariates, results of the handling of outliers
and missing values, excluded data and reasons for exclusion, etc. should be presented.
 Results of model building
Descriptions should be made by clearly showing the process of reaching a decision on
the final model and the rationale for the decision. The structure of the constructed
exposure-response model and the parameter estimates with their standard error and, if
feasible, diagnosis plots should be presented. The qualification results for the exposureresponse model should also be presented.
6) Discussions and clinical applications
The validity and clinical significance of the model building and estimates should be
discussed. In particular, in such cases as the dosage and administration that was notevaluated in the dose-response study is set in a confirmatory study or data are used as therationale for dose adjustment in the package insert, it is recommended to clearly indicate

13the applicable dosage range and validity of demographic data etc. based on thecharacteristics and range of the data used. Besides, it is also recommended to present themethod for simulation based on the exposure-response model and to illustrate the resultsof the simulation as well as its reliability in figures.
7) Appendix
The model file describing the structure of the exposure-response model and output
results, as well as the dataset used for analysis (a partial dataset from some of the subjects
may be used), should be accompanied as appendix. Figures and tables not used in themain part of the report may be included in the appendix.
3.2

Regulatory submission

When the results of exposure-response analysis are used for regulatory submission, theanalysis report should be provided and section 2.7, Clinical Summary, of the Common
Technical Document (CTD) should be prepared based on the 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
exposure and clinical response relationships, parameter estimates obtained, the results ofsimulation performed based on the model, using figures and tables, and the model
diagnosis and qualification results. Also, when describing the relationship between drug
exposure and clinical response, information on the exposure-response analysis should bereflected appropriately in the relevant parts of the CTD, if necessary.
For exposure-response analysis for which electronic data are subject to submission in
filing for approval and etc., electronic data should be submitted according to a series of
relevant notifications.
3.3 Providing information in package insert
When the results of exposure-response analysis provide useful information for
appropriate use of the drug, exposure-response analysis information should be includedin the “Pharmacokinetics” section of the package insert. When the simulation results aredescribed in the package insert as the rationale for an important caution, they should be
described in such a way that they can be distinguished from the actual measurement data.
Information should be provided with the prospect that exposure-response analysis
results are utilized in healthcare settings, when providing information that serves as thebasis for the dose adjustment policy in a specific group or is provided as the rationale for
an important caution etc.

14

4

Relevant guidelines and documents

This guideline shows the general guidance that is scientifically plausible for conducting
exposure-response analysis. While guidelines and documents that have already been
notified provide descriptions regarding exposure-response analysis, this guideline
integrates and organizes contents of such guidelines, and furthermore, incorporates thelatest knowledge and concepts as of present.
ICH guideline
1)

2)

3)

4)

5)

6)

PAB/PCD Notification No. 227 March 20, 1995 Clinical Safety Data Management:
Definitions and Standards for Expedited Reporting (ICH E2A)PFSB/SD Notification No. 0328007 March 28, 2005 Post-Approval Safety DataManagement: Definitions and Standards for Expedited Reporting (ICH E2D)PFSB/ELD Notification No. 0916001, PFSB/SD Notification No. 0916001
September 16, 2005 Pharmacovigilance Planning (ICH E2E)
PAB/PCD Notification No. 335 May 1, 1996 Structure and Content of Clinical Study
Reports (ICH E3)
PAB/PCD Notification No. 494 July 25, 1994 Dose-Response Information to Support
Drug Registration (ICH E4)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) PMSB/ELD Notification No. 1047 November 30, 1998 Statistical Principles forClinical Trials (ICH E9)11) PMSB/ELD Notification No. 1334 December 15, 2000 Clinical Investigation on
Medicinal Products in the Pediatric Population (ICH E11)
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)15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) PFSB/ELD Notification No. 1023-1 October 23, 2009 The Clinical Evaluation of
QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic
Drugs (ICH E14)
15) PSEHB/PED Notification No. 1227-5 December 27, 2017 Addendum: Clinical
Investigation of Medicinal Products in the Pediatric Population (ICH E11 [R1])16) PSEHB/PED Notification No. 0118-1 January 18, 2018 Genomic Sampling andManagement of Genomic Data (ICH E18)
17) PSEHB/PED Notification No. 0612-1 June 12, 2018 General Principles for Planningand Design of Multi-Regional Clinical Trials (ICH E17)
Regional guidelines and documents (pharmacokinetics)
1) PMSB/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
Operations of 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. 0723-6 July 23, 2018 Drug-Drug Interaction
Guideline for Pharmaceutical Development and Appropriate Information Provision
9) PSEHB/PED Notification No. 0515-1 May 15, 2019 Guideline on Population
Pharmacokinetic and Pharmacodynamic analysis
10) PSEHB/PED Notification No. 1225-1 December 25, 2019 Revision of Guidance for
Establishing Safety in First-in-Human Studies during Drug Development
Regional guidelines and documents (package inserts)

161) PSEHB Notification No. 608-1 June 8, 2017 Instructions for Package Inserts of
Prescription Drugs, etc.
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 (2003.4)
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) FDA: Guidance for Industry: Physiologically Based Pharmacokinetic Analyses —Format and Content (2018.8)
7) EMA: Guideline on the clinical investigation of the pharmacokinetics of therapeuticproteins (2007.7)
8) EMA: Guideline on reporting the results of population pharmacokinetic analyses(2008.1)
9) EMA: Guideline on the use of pharmacokinetics and pharmacodynamics in thedevelopment of antimicrobial medicinal products (2017.2)
10) EMA: Reflection paper on the use of extrapolation in the development of medicines
for pediatrics (2018.10)
11) EMA: Guideline on the reporting of physiologically based pharmacokinetic (PBPK)
modelling and simulation (2019.7)
5

Glossary

1) Model based meta-analysis
This is an analysis method of integrating data from different studies/literature andanalyzing them using a model. For data that cannot be compared directly due todifferences in observation time points, patient characteristics, etc., comparisons may still
be possible by adjusting for these effects using an appropriate model. However, since it
is an analysis integrating data from different studies or literature, it is important to selectstudies or literature information that are not biased (systematic review), and theintegration method, etc. is also important.

172) Quantitative system pharmacology model (QSP)
It is possible to predict the efficacy and safety of a drug by modeling the mechanismof disease onset and the mechanism of action of the drug based on detailed knowledge ofthe biological systems.
3) Empirical model
Empirical models are data-driven models in which drug concentration data and efficacy
and safety data obtained from clinical studies, etc. are used. Empirical models are easy todevelop, requiring fewer assumptions than mechanism-based models.
6

Appendix

This appendix shows an example of a figure generated by exposure-response analysis
and examples of models related to exposure-response analysis mainly used in analyses ofthe pharmacometrics field (including points to consider for each model depending on thecharacteristics of the data used in the analysis).
6.1 Example of a figure generated by exposure-response analysis
Even if there are variations in the response at specific doses, investigation of the
exposure-response data at multiple doses in a single figure may make it possible to obtain
more useful information on doses at which the required response can be obtained.

18

The solid line and the gray area are the model-simulated median and 90% prediction interval, respectively.

Figure 2 Example of a dose-exposure-response relationship6.2 Examples of models used in exposure-response analysis
In cases in which the response measure is a continuous value and a plateau is observedin the changes in response measure within the range of the exposure measure, the use ofan Emax model or a sigmoidal Emax model (Equation 1) may be considered. If the plateau
is not observed in the changes in the response measure, the use of a linear model (Equation2) may be considered.
Equation 1.

Example of the (sigmoid) Emax model:
𝐸𝑚𝑎𝑥 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝛾
𝐸 = 𝐸0 +𝛾𝐸𝐶50 + 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝛾

E: Response measure
Exposure: Exposure measure
E0: Response measure with an exposure measure of 0 (e.g., baseline or change
in the placebo group)
Emax: Maximum effect
EC50: Measure of exposure resulting in 50% of the maximum effect
γ: Hill coefficient (When γ equals 1, the model becomes an Emax model)
Equation 2.

Example of a linear model:
𝐸 = 𝐸0 + 𝑆𝑙𝑜𝑝𝑒 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒

E: Response measure
Exposure: Exposure measure1
E0: Response measure with an exposure measure of 0
Slope: Slope of change in response with exposure
When the response measure is binary data, the use of a logistic regression model, etc.
may be considered (Equation 3).
Equation 3.

1

Example of a logistic regression model:

Logarithmically converted exposure measure can be used.
19

𝑝(𝑥)
𝐿𝑜𝑔𝑖𝑡(𝑝(𝑥)) = 𝑙𝑜𝑔 [] = 𝐿𝑜𝑔𝑖𝑡0 + 𝑆𝑙𝑜𝑝𝑒𝐿𝑜𝑔𝑖𝑡 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒
1 − 𝑝(𝑥)x: Specific value of independent variable
p(x): Probability that the response measure is x
Exposure: Exposure measure
Logit0: Logit of the response measure with an exposure measure of 0
SlopeLogit: Slope of change in logit with exposure
In the following cases, analysis of time-course data should be considered.
When the inter-occasion variability in the response measure is largeWhen the exposure-response relationship at the primary evaluation time point
appears inconsistent with other evaluation time pointsIn situation with frequent or informative dropouts on the response are observed.

When the time-courses of the response measure are important, etc.

In these cases, analysis should be performed by using the exposure and response
measures obtained over time, and a term of time should be incorporated into the model.
For example, when it takes time to reach the maximum effect, the Emax model with timedependent onset of the maximum effect may be considered, as shown in Equation 4.
Similarly, when the longitudinal binary or ordered categorical data is modeled, a modelthat incorporates a term of time is considered.
Equation 4.

Example of the Emax model with time-dependent onset of the maximum
effect:𝐸𝑚𝑎𝑥 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒
𝐸𝑖 = 𝐸0,𝑖 +× (1 − 𝑒 −𝑘𝑡𝑟×𝑇𝑖𝑚𝑒𝑖 )𝐸𝐶50 + 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒

Ei: Response measure at time point i
Timei: Time at time point i
Exposure: Exposure measure
E0,i: Response measure without exposure at time point i
Emax: Maximum effectktr: Rate constant for the onset of effect
EC50: Measure of exposure resulting in 50% of the maximum effect20

If response variables in the placebo group change over time, a placebo model as shownin Equation 5 may be considered.
Equation 5.

Example of a placebo model:
𝐸𝑝𝑙𝑎𝑐𝑒𝑏𝑜,𝑖 = 𝐸𝑚𝑎𝑥,𝑝𝑙𝑎𝑐𝑒𝑏𝑜 × (1 − 𝑒 −𝑘𝑝𝑙𝑎𝑐𝑒𝑏𝑜 ×𝑇𝑖𝑚𝑒𝑖 )Eplacebo,i: Response measure of the placebo group at time point i
Timei: Time at time point iEmax,placebo: Maximum placebo effectkplacebo: Rate constant for the onset of the placebo effect
Disease progression models are used to examine the long-term effects of a drug on adisease that progresses over a long period of time. In disease progression models, clinicalsymptoms in patients on drug treatment can be expressed as a composite of diseaseprogression and the effect of the drug. Although the example of Equation 6 represents thesymptomatic effect of a drug, the effect of a drug on disease progression may beinvestigated by examining the impact on the slope of the disease progression over time.
Equation 6.

Example of a disease progression model:
𝐸𝑖 = 𝐸0 + 𝛼 × 𝑇𝑖𝑚𝑒𝑖 + 𝑆𝑙𝑜𝑝𝑒 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒

Ei: Response measure at time point i
E0: Response measure with an exposure measure of 0
α: Slope of disease progression over time
Timei: Time at time point i
Slope: Slope of change in response with exposure
Exposure: Exposure measure
Survival analyses may be used to analyze the time-to-event data. A proportional hazard
model etc. may be used where the probability that an event may occur at a certain moment,i.e., so-called hazard, is used to build a model and an exposure measure is incorporated
as an explanatory variable for the hazard (Equation 7).
Equation 7.
Example of proportional hazard model：ℎ(𝑡) = ℎ0 (𝑡) × 𝑒𝑥𝑝(𝛽 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 + 𝛽1 × 𝐶𝑜𝑣1 + ⋯ + 𝛽𝑛 × 𝐶𝑜𝑣𝑛 )21

h(t)：hazard at time point t
h0(t)：baseline hazard
β：Slope of change in hazard with exposure
Exposure：Exposure measure
β1、βn：Slope of change in hazard with covariate
Cov1、Covn：covariate

22