E11A Pediatric Extrapolation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This draft guidance, when finalized, will represent the current thinking of the Food and Drug 
Administration (FDA or Agency) on this topic. It does not establish any rights for any person and is not 
binding on FDA or the public.  You can use an alternative approach if it satisfies the requirements of the 
applicable statutes and regulations.  To discuss an alternative approach, contact the FDA staff responsible 
for this guidance as listed on the title page.  The draft guidance has been left in the original International 
Council for Harmonisation format.  The final guidance will be reformatted and edited to conform with 
FDAs good guidance practice regulation and style.  
 
 
 
For questions regarding this draft document, contact (CDER) Lynne Yao, (301) 796-2141.  
 
 
    FOREWORD 
 
The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for 
Human Use (ICH) has the mission of achieving greater regulatory harmonization worldwide to 
ensure that safe, effective, and high-quality medicines are developed, registered, and maintained 
in the most resource-efficient manner.  By harmonizing the regulatory expectations in regions 
around the world, ICH guidelines have substantially reduced duplicative clinical studies, 
prevented unnecessary animal studies, standardized safety reporting and marketing application 
submissions, and contributed to many other improvements in the quality of global drug 
development and manufacturing and the products available to patients.   
 
ICH is a consensus-driven process that involves technical experts from regulatory authorities and 
industry parties in detailed technical and science-based harmonization work that results in the 
development of ICH guidelines.  The commitment to consistent adoption of these consensus-
based guidelines by regulators around the globe is critical to realizing the benefits of safe, 
effective, and high-quality medicines for patients as well as for industry.  As a Founding 
Regulatory Member of ICH, the Food and Drug Administration (FDA) plays a major role in the 
development of each of the ICH guidelines, which FDA then adopts and issues as guidance to 
industry.   
 
 
 
 
     
 
 
 
INTERNATIONAL COUNCIL FOR HARMONISATION OF TECHNICAL REQUIREMENTS 
FOR PHARMACEUTICALS FOR HUMAN USE 
 
 
ICH HARMONISED GUIDELINE 
 
 
PEDIATRIC EXTRAPOLATION 
E11A 
 
 
Draft version 
Endorsed on 4 April 2022 
Currently under public consultation 
 
 
 
 
 
 
 
 
 
 
At Step 2 of the ICH Process, a consensus draft text or guideline, agreed by the appropriate ICH 
Expert Working Group, is transmitted by the ICH Assembly to the regulatory authorities of the 
ICH regions for internal and external consultation, according to national or regional procedures.  E11A 
Document History 
 
 
 
Code  History  Date 
E11A  Endorsement by the Members of the ICH Assembly  4 April 2022 
under Step 2 and release for public consultation. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Legal notice: This document is protected by copyright and may, with the exception 
of the ICH logo, be used, reproduced, incorporated into other works, adapted, 
modified, translated or distributed under a public license provided that ICH's 
copyright in the document is acknowledged at all times. In case of any adaption, 
modification or translation of the document, reasonable steps must be taken to 
clearly label, demarcate or otherwise identify that changes were made to or based 
on the original document.  Any impression that the adaption, modification or 
translation of the original document is endorsed or sponsored by the ICH must be 
avoided. 
The document is provided "as is" without warranty of any kind. In no event shall 
the ICH or the authors of the original document be liable for any claim, damages 
or other liability arising from the use of the document. 
The above-mentioned permissions do not apply to content supplied by third 
parties. Therefore, for documents where the copyright vests in a third party, 
permission for reproduction must be obtained from this copyright holder. 
  ICH HARMONISED GUIDELINE 
 
PEDIATRIC EXTRAPOLATION 
E11A 
ICH Consensus Guideline 
 
TABLE OF CONTENTS 
1.  Introduction...................................................................................................................... 7 
1.1.  Objectives of the Guideline ........................................................................................ 7 
1.2.  Background ............................................................................................................... 7 
1.3.  Scope ........................................................................................................................ 7 
1.4.  General concepts ....................................................................................................... 8 
2.  Pediatric Extrapolation Framework ................................................................................... 9 
3.  Pediatric Extrapolation Concept ...................................................................................... 10 
3.1.  Disease Similarity .................................................................................................... 10 
3.1.1.  Factors to Consider in the Evaluation of Similarity of Disease ............................ 11 
3.2.  Drug (Pharmacology) Similarity............................................................................... 12 
3.3.  Similarity of Response to Treatment ......................................................................... 12 
3.3.1.  Factors to Consider in the Evaluation of Similarity of Response to Treatment ..... 13 
3.4.  Sources and Types of Existing Data.......................................................................... 13 
3.5.  Safety Considerations in the Extrapolation Concept .................................................. 15 
3.5.1.  Extrapolation of Safety ..................................................................................... 15 
3.5.2.  Additional Safety Considerations ...................................................................... 17 
3.6.  Integration of Evidence and Development of the Pediatric Extrapolation Concept ...... 17 
3.7.  Presentation of the Pediatric Extrapolation Concept .................................................. 18 
4.  Pediatric Extrapolation Plan ............................................................................................ 19 
4.1.  Dose Selection ......................................................................................................... 19 
4.1.1.  When Dose Ranging Data Should be Collected? ................................................ 20 
4.1.2.  Use of Biomarkers ............................................................................................ 20 
4.1.3.  Scenarios for Dose Selection ............................................................................. 20 
4.1.4.  Other Considerations ........................................................................................ 23 
4.2.  Model-Informed Approaches.................................................................................... 23  4.3.  Efficacy Studies....................................................................................................... 24 
4.3.1.  Single Arm Efficacy Studies ............................................................................. 24 
4.3.2.  Externally Controlled Studies ............................................................................ 25 
4.3.3.  Concurrent Controlled Efficacy Studies ............................................................. 25 
4.3.4.  Incorporation of External Data .......................................................................... 26 
4.3.5.  Quantifying the Impact of Use of Reference Data .............................................. 26 
4.3.6.  Presentation and Justification for the Pediatric Trial ........................................... 27 
4.3.7.  Analysis, Reporting, and Interpretation.............................................................. 27 
5.  Additional pediatric extrapolation plan considerations ..................................................... 28 
5.1.  Safety Plan .............................................................................................................. 28 
5.2.  Inclusion of Adolescents in Adult Trials ................................................................... 29 
 
 
    1  1.  Introduction 
2   
3  1.1. Objectives of the Guideline 
4  The purpose of this guideline is to provide recommendations for, and promote international 
5  harmonization of, the use of pediatric extrapolation to support the development and authorization 
6  of pediatric medicines. Harmonization of the approaches to pediatric extrapolation should reduce 
7  the likelihood of substantial differences between regions. Importantly, harmonization should also 
8  reduce exposure of pediatric populations to unnecessary clinical trials and facilitate more timely 
9  access to pediatric medicines globally. 
10   
11  1.2. Background 
12  Regional guidelines discussing pediatric extrapolation have previously been issued by various 
13  regulatory agencies. Pediatric extrapolation is defined in the ICH E11(R1) guideline as an 
14  approach to providing evidence in support of effective and safe use of drugs in the pediatric 
15  population when it can be assumed that the course of the disease1 and the expected response to a 
16  medicinal product would be sufficiently similar in the pediatric [target] and reference (adult or 
17  other pediatric) population. Pediatric extrapolation can extend what is known about the 
18  reference population (e.g., efficacy, safety, and/or dosing) to the target population based on an 
19  assessment of the relevant similarities of disease and response to therapy of the two populations. 
20   
21  Historically, extrapolation of safety generally was considered unacceptable. However, our 
22  understanding of similarities and differences between reference and target populations with 
23  respect to safety has evolved. As described in the ICH E11(R1) guideline, the principle of using 
24  data generated in a reference population to define the scope and extent of data that should be 
25  collected in a target population can also apply to the generation of safety data (see section 3.5). 
26   
27  This guideline is intended to complement and expand on ICH E11(R1) to provide a more 
28  comprehensive framework for the use of pediatric extrapolation in optimizing pediatric drug 
29  development. This guideline provides a roadmap to aid drug developers and regulators on the 
30  degree to which pediatric extrapolation can be applied, and the information that should be 
31  collected to address gaps in knowledge supporting the safe and effective use of medicines in the 
32  pediatric population. 
33   
34  1.3. Scope 
35  This guideline provides a framework for using extrapolation as a tool to support pediatric drug 
36  development that encompasses an iterative process for understanding the existing information 
37  available, the gaps in information needed to inform development and ways to generate additional 
38  information when needed to support extrapolation for pediatric drug development. This guideline 
39  recommends approaches to assessing factors that influence the determination of the similarity of 
40  disease and response to treatment between a reference and pediatric target population. In 
41  addition, it discusses how the characteristics of the disease, drug pharmacology and the response 
42  to treatment may influence this determination. 
43   
44  The guideline discusses how the use of statistical and other quantitative tools (e.g., modeling and 
 
1 For the purposes of this document disease includes both diseases and conditions  45  simulation) can be leveraged to fill in gaps in knowledge. This guideline is not intended to 
46  provide a comprehensive listing of all the situations where extrapolation of data can play an 
47  important role in pediatric drug development, but it does explain how pediatric extrapolation can 
48  be applied practically to support the safety and efficacy of a product in pediatric populations. 
49  This guideline does not discuss other types of extrapolation  for example, the ICH E5 
50  guideline should be consulted regarding the concept of "bridging studies" to leverage foreign 
51  clinical data from one region for extrapolation to another regions population as a basis for 
52  registration of a medicine.  Although there are some quantitative strategies mentioned or 
53  explained within the guideline, it is not meant to be a comprehensive instruction guide. Some 
54  basic understanding of the role of quantitative approaches used in clinical trial development is 
55  expected. 
56   
57  1.4. General concepts 
58  The use of pediatric extrapolation ensures that children only participate in clinical trials when 
59  necessary to further the scientific understanding of a medicinal products use in children. As per 
60  ICH E11(R1), a sufficient prospect of clinical benefit is required to justify the risks of exposing 
61  children to an investigational product. When regulatory authorities require pediatric studies as 
62  part of adult-driven drug development, the rationale for doing so can implicitly assume a degree 
63  of similarity between the reference and target (in this case pediatric) condition. Thus, it can be 
64  appropriate for a pediatric program for diseases associated with an adult condition to incorporate 
65  some degree of pediatric extrapolation. 
66   
67  In the ICH E11(R1) definition of pediatric extrapolation, sufficiently similar might suggest a 
68  threshold that must be exceeded for pediatric extrapolation to be acceptable for regulatory 
69  consideration. However, whether the course of disease and expected response to treatment can be 
70  considered sufficiently similar between a target and reference population is not simply a yes or 
71  no question. Therefore, this guidance does not use discrete categories (e.g., full, partial or none) 
72  to describe the different approaches to pediatric extrapolation, in favour of identifying the 
73  clinical trial designs which can address the remaining uncertainties based on an assessment of the 
74  existing data. The use of extrapolation as discussed in this guideline reflects that a continuum of 
75  dissimilarity/similarity may exist. There may be uncertainties associated with the data supporting 
76  extrapolation to the target pediatric population. The extrapolation approach should address these 
77  uncertainties, utilizing clinical judgement to establish the tolerable level of uncertainty (see 
78  Figure 1). Options for trial designs will depend on the level of uncertainty that needs to be 
79  resolved. 
80    81  Figure 1: Pediatric Extrapolation Approach 
82   
83   
84  2.  Pediatric Extrapolation Framework 
85  The extrapolation framework consists of three parts: development of a pediatric extrapolation 
86  concept; and the creation and execution of a pediatric extrapolation plan (see Figure 2). 
87   
88  The first step is the development of a pediatric extrapolation concept. The concept is developed 
89  through comprehensive and detailed review of existing information about the range of factors 
90  that define the disease, the drug pharmacology, and the clinical response to treatment across the 
91  reference and target populations. Factors that influence the effects of treatment in the reference 
92  and target populations should be identified. Once a review of the existing knowledge has been 
93  conducted, the data should be synthesized to develop the pediatric extrapolation concept. 
94  Methods to review and synthesize these data can include quantitative approaches such as 
95  statistical methods and modeling and simulation. Synthesis of the data should be conducted to 
96  both understand the strength of the known data as well as to identify important gaps in 
97  knowledge which will inform what additional data, if any, are required. 
98   
99  Once the pediatric extrapolation concept has been developed, the pediatric extrapolation plan 
100  should be developed. This plan should include the objectives(s) and methodological approaches 
101  for the data that need to be generated to support efficacy and safety in the target population for 
102  the purpose of regulatory decision-making. In addition, there may be an evolution of the 
103  pediatric extrapolation concept based on emerging clinical and scientific data. Rather than 
104  abandon an existing pediatric extrapolation plan based on a prior concept, the plan itself can be 
105  modified to reflect current scientific and clinical understanding. 
106    107  Figure 2: Pediatric Extrapolation Framework 
108   
109   
110  The execution of the plan should also include a review of the data generated to confirm any 
111  assumptions made and to address uncertainties identified in the pediatric extrapolation concept. 
112  A review of the results should also be used to identify whether a different approach can be 
113  considered in pediatric extrapolation plans for subsequent pediatric development programs. 
114   
115  3.  Pediatric Extrapolation Concept 
116  Development of a pediatric extrapolation concept requires an understanding of the factors that 
117  influence the similarity of disease, the pharmacology of the drug and the response to therapy as 
118  well as the safety of use in all the relevant populations. 
119   
120  3.1. Disease Similarity 
121  The assessment of similarities and differences of the disease between a reference and target 
122  population is a key factor in developing the pediatric extrapolation concept. Although 
123  historically, pediatric extrapolation was often based on a binary determination of disease 
124  similarity (i.e., either yes or no), the understanding of similarities and differences in disease 
125  between a reference and target population has become more nuanced (see Figure 1, Section 1.4). 
126   
127  The evaluation of disease similarity is not intended to determine whether the disease in the 
128  reference and target populations is exactly the same but rather whether the disease is different 
129  to a degree that would preclude pediatric extrapolation. Even if there are differences in the 
130  disease, some similarities may be present that would still allow for the use of pediatric 
131  extrapolation. 
132   
133  It can also be possible to identify disease subgroups in both the reference and target populations  134  that are sufficiently similar to support the use of pediatric extrapolation even if the disease in the 
135  overall population is not sufficiently similar. For example, anatomic congestive heart failure in 
136  children is not similar to adult heart failure, whereas heart failure due to dilated cardiomyopathy 
137  is similar between adult and pediatric populations, allowing for extrapolation from adult to 
138  pediatric patients with dilated cardiomyopathy. 
139   
140  To increase confidence in understanding the similarity of disease between the populations, 
141  evaluation of disease similarity should also attempt to determine the gaps in knowledge and 
142  uncertainties that exist in the evidence reviewed and identify what additional evidence is needed. 
143  Importantly, the evaluation of disease similarity is not a static or one-time exercise. As 
144  knowledge is gained, the additional knowledge should be incorporated into the evaluation of 
145  disease similarity in the pediatric extrapolation concept. 
146   
147  3.1.1. Factors to Consider in the Evaluation of Similarity of Disease 
148  Assessment of disease similarity between a pediatric population and a reference population 
149  should include a review of the following factors: 
150   
151  Pathophysiology of disease 
152  Evaluation of the pathophysiology and etiology of the disease between the reference and target 
153  populations should be conducted. Collection of relevant information can include biochemical, 
154  genetic/epigenetic, cellular, tissue, organ system, and epidemiologic information that describes 
155  similarities and differences between the reference and target populations. Evaluation can also 
156  include a determination about whether differences in the clinical presentation of disease may 
157  depend upon the age of onset, age-dependent phenotypic expression, or other age-related 
158  differences. Evaluation of biomarkers that are common in the pathophysiology of the disease, 
159  including disease progression, if available, are often helpful in establishing similarities in a 
160  disease between the reference and target pediatric populations. Similarities in the outcome of 
161  untreated disease should also be evaluated. 
162   
163  Disease definition 
164  Evaluation of disease definitions and diagnostic criteria between the reference and target 
165  populations should be conducted. When evaluating similarities and differences between 
166  reference and target populations, the following should be considered: 
167    What are the manifestations or diagnostic criteria that define the disease?  
168    How similar are the manifestations between the reference and target pediatric 
169  populations?  
170    How are the manifestations measured? 
171    Are there similar measurements used to define manifestations of the disease in the 
172  reference and target pediatric populations? 
173    Are there subtypes (e.g., based on severity, genetics, molecular markers, etc.) of the 
174  disease that occur in the reference or target populations? 
175    What are the similarities and differences in the subtypes of the disease in the reference 
176  and target population? 
177    Are there other factors to consider (e.g., genetic/epigenetic, etc.) that are needed to define 
178  the disease? 
179    180  Course of disease 
181  Evaluation of the similarities and differences in the course of disease between reference and 
182  target populations should be conducted. In the evaluation, the following should be considered: 
183   
184    What are the similarities and differences of the clinical course of the disease between 
185  the reference and target populations? Are there differences in the course of the disease 
186  based on factors such as the age of onset of the disease?  
187    Are there similar endpoints and/or biomarkers available that help to measure 
188  progression of disease in both the reference and target populations?  
189    Are the short-term or long-term outcomes of the disease similar for the reference and 
190  target pediatric populations and can these outcomes be measured similarly?  
191    Are there available treatments being used for both reference and target populations? 
192    What effect have these treatments (e.g., timing of treatment relative to onset of disease 
193  and age of the patient, frequency of treatment, length of treatment) had on the course of 
194  the disease in the reference and target populations? 
195   
196  Although the frequency, severity, or timing of the progression of the disease may differ between 
197  the reference and target populations, certain commonalities in the course of the disease may still 
198  allow for the use of pediatric extrapolation. For example, if a treatment becomes available that 
199  changes the course of the disease in the reference population, but the treatment has not yet been 
200  approved in the target population, this should not lead to the conclusion that the course of the 
201  disease between the two populations is now different for the purposes of pediatric extrapolation. 
202   
203  3.2. Drug (Pharmacology) Similarity 
204   
205  As part of an evaluation of the similarities and differences of the pharmacology of the drug 
206  between the reference and target populations, information that is known about the underlying 
207  absorption, distribution, metabolism, and excretion (ADME) properties and mechanism of action 
208  (MOA) of the study drug should be reviewed. Consideration should be given to the potential 
209  influence of body size (e.g., weight, body surface area [BSA]), age, organ maturation, 
210  concomitant medications, and other relevant covariates on ADME (e.g., protein binding, 
211  metabolic enzymes, transporters, renal function) and MOA properties (e.g., expression level and 
212  sensitivity of drug targets). 
213   
214  Differences in ADME processes can result in differences in pharmacokinetic (PK) parameters 
215  and resulting drug exposure. Exposure is a broad concept, ranging from measurement of the 
216  systemic (or other biological compartment) exposure of the drug (parent and/or metabolite(s)), at 
217  a single point in time (for example maximum or trough concentration), exposure over a time 
218  interval (for example AUC0-t or average concentration), or characteristics of the overall 
219  concentration-time curve (e.g., clearance, volume of distribution). In addition, differences in 
220  MOA properties can result in differences in an exposure-response (E-R) relationship between the 
221  reference and target population. Changes in these characteristics over time due to developmental 
222  maturation should be considered. 
223   
224  3.3. Similarity of Response to Treatment 
225  As with similarity of disease, the similarities, and differences in response to treatment between a  226  reference and target population should be understood as a continuum (see Figure 1, Section 1.4). 
227  To assess similarities and differences of response to treatment, a thorough review of available 
228  knowledge in both the reference and target populations should be conducted, including the 
229  response to the drug, other drugs in the same class and in other classes. Similarly, data generated 
230  in other indications for the drug can serve as a relevant source of knowledge when assessing the 
231  similarity or difference of response to treatment. An evaluation of data that inform exposure-
232  response (E-R) relationships between the target pediatric population and the reference population 
233  should be part of this assessment. 
234   
235  3.3.1. Factors to Consider in the Evaluation of Similarity of Response to Treatment 
236  The degree of similarity of response to treatment between the reference and target populations 
237  can also influence the degree of similarity of disease and vice versa. Assessment of similarity of 
238  response to treatment between a target pediatric population and a reference population should 
239  include a review of the following factors: 
240   
241  Pharmacokinetics and pharmacodynamics (PK/PD) 
242  The potential effect of developmental and maturational changes on the PK/PD relationship and 
243  clinical response should be evaluated. An understanding of the drug target and its role in normal 
244  development, disease pathology and expected response to therapy should be evaluated. For 
245  example, if a receptor does not exist in the first 6 months of life, no response to treatment would 
246  be expected for a drug only targeting this receptor in this age group. Factors that impact response 
247  that may differ between the reference and target populations (e.g., concomitant medications, 
248  comorbid disease, organ function, genetic makeup) should be evaluated to assess whether there 
249  is an impact on the extent to which pediatric extrapolation can be applied. 
250   
251  Response endpoint(s): 
252  When evaluating the similarity of response, the following questions should be considered: 
253    How is a response endpoint (e.g., clinical, biomarker, composite, etc.) measured in the 
254  reference and target populations?  
255    Is there a similar measurement of the endpoint used in both the reference and target 
256  populations?  
257    If the response endpoint or measurement of the endpoint is different in the reference and 
258  target populations, what is the relationship between the endpoints (e.g., clinical endpoint 
259  in the reference population in relation to a biomarker endpoint in the target population)? 
260   
261  When attempting to evaluate similarity of response to treatment, it may be that consideration 
262  should be given as to whether there may be age/maturity-related factors that could result in 
263  differences in the measured response between the target and reference populations. For many 
264  pediatric drug development programs, the primary endpoint(s) in the target pediatric population 
265  is/are different from that in the reference population. When this is the case, a comparison of one 
266  or more components of the primary endpoint(s) and/or secondary/exploratory endpoint(s) can be 
267  used to understand the relationship between the different endpoints. 
268   
269  3.4. Sources and Types of Existing Data 
270  Use of existing data should be fit-for-purpose (i.e., the context in which it was generated is 
271  applicable to the context in which it is intended to be used). It is important to consider both the  272  quantity and quality of data to evaluate the similarities and differences between the reference and 
273  target populations. All available data should be used to establish the extrapolation concept and 
274  formulate the extrapolation plan. Such information may also include data from ongoing 
275  adult/pediatric development programs, or relevant data from terminated programs. Examples of 
276  the sources and types of data that should be evaluated are included in Table 1 and are discussed 
277  further in this section. Given the considerable overlap in the data used to support similarities and 
278  differences in disease, pharmacology, and response to treatment, the sources of data are 
279  combined in Table 1. 
280   
281  Table 1: Examples of Sources and Types of Data to Evaluate for Similarity of Disease and 
282  Response to Treatment 
Sources of Data  Types of Data 
Clinical data  PK, PK/PD, E-R, and clinical data in the same condition 
for a drug or drugs in the same class 
PK, PK/PD, E-R, and clinical data in other related 
conditions for a drug or drugs in the same class 
PK, PK/PD, E-R, and clinical data in the same condition 
for a drug or drugs in a different class 
Nonclinical data  ADME Data from animal models 
In silico, in vitro, and in vivo data (e.g., disease-response, 
PK, PK/PD, semi-mechanistic, and mechanistic) 
Juvenile nonclinical toxicology data 
Real World Data  Data including but not limited to disease registries 
(regional, national, and international), electronic health 
records, health claims databases 
Other sources  Systematic reviews or meta-analyses, including those 
that can be used to evaluate suitable biomarkers 
Professional organization guidelines/Clinical practice 
guidelines/Consensus documents 
Published models/simulations (e.g., PK/PD, mechanistic) 
Expert opinion 
Standard of care/practice of medicine 
283   
284   
285  Clinical data 
286  Clinical data (e.g., from controlled trials, prospective observational studies, PK, PK/PD and/or 
287  biomarker studies) in populations with the same condition or related conditions should be  288  evaluated to understand similarities and differences between the reference and target populations. 
289  All available data for the drug/drug class should be evaluated including ongoing and completed 
290  studies, published or unpublished, whether results are positive or negative. 
291   
292  Nonclinical data 
293  Data from nonclinical sources such as in vivo, in vitro, and in silico models should also be 
294  evaluated when available. Data from in silico models may also include PK and/or PD, semi-
295  mechanistic, and mechanistic models. In general, when clinical data are available, data from 
296  animal models may be less relevant, but this is not always the case. In certain situations, disease 
297  similarity can be supported with only nonclinical data, especially when there is no ability to 
298  collect clinical data (e.g., anthrax or plague). 
299   
300  Real world data (RWD) 
301  The extent to which RWD can be used to support pediatric extrapolation, both the pediatric 
302  extrapolation concept and plan, is evolving. Therefore, the adequacy, relevance, and extent to 
303  which RWD can be used to support pediatric extrapolation should be discussed with regulatory 
304  authorities. In the development of the pediatric extrapolation concept, a review of data from 
305  RWD sources, including but not limited to electronic health records, claims databases, and 
306  registries, can be considered. The use of RWD in an extrapolation plan is discussed later (see 
307  section 4.3.2) 
308   
309  Other sources 
310  Expert opinions, including clinical practice guidelines developed by professional organizations, 
311  can be used to support the extrapolation concept. Published clinical practice guidelines from 
312  professional organizations are considered more informative than unpublished expert opinions. 
313  However, published guidelines and expert opinions can vary between regions based on 
314  differences in standard of care. Reliance on expert opinion or standard of care without an 
315  assessment of the strength of the evidence is generally not sufficient. 
316   
317  The sources and types of data that are described above each have strengths and weaknesses. The 
318  confidence in the degree to which the sources and types of data support similarities between the 
319  reference and target populations requires an assessment of the quantity and quality of data from 
320  each source as well as the context in which the data are being evaluated. A critical and 
321  multidisciplinary assessment of all the data should be conducted to justify the use of the evidence 
322  to support the extrapolation concept. 
323   
324  3.5. Safety Considerations in the Extrapolation Concept 
325  Basic considerations for the development of an overall safety data collection and adverse event 
326  reporting plan are discussed in other guidance (ICH E2, ICH E6, ICH E11, ICH E11(R1)). This 
327  section describes specific considerations related to the extrapolation of safety as part of the 
328  overall development of the safety evaluation for a pediatric population(s). 
329   
330  3.5.1. Extrapolation of Safety 
331  The principles underlying the appropriate use of data generated in a reference population(s) to 
332  define the scope and extent of efficacy data that needs to be collected in a target population can 
333  also apply to the generation of safety data (see section 1.2). Extrapolation of safety data could be  334  considered based on the available knowledge of the known and/or potential safety issues in the 
335  reference population that are relevant to the target pediatric population. Other information (e.g., 
336  nonclinical, mechanistic) should be considered as part of this analysis. These data should help 
337  increase certainty about the expected safety profile of a drug in a particular pediatric population 
338  and determine if additional gaps in knowledge need to be addressed in the pediatric program. 
339  Evaluation of the suitability and extent to which safety will be extrapolated should be included in 
340  the extrapolation concept and plan. 
341   
342  The source and amount of safety data to support the extrapolation of safety data to a target 
343  population should be considered early in drug development planning. The reference 
344  population(s) can include children and/or adults exposed to the same drug or class of drugs. Data 
345  can also be leveraged in reference populations who have been treated with different dosing 
346  regimens and/or for different diseases/indications. Enrollment of adolescents in or concurrent 
347  with the adult trials may allow for earlier evaluation of safety for the adolescent population (see 
348  section 5.2). The collection of safety data in adolescents may also provide important information 
349  to support the safe use of a drug in younger patients. 
350   
351  When considering extrapolation of safety, the following questions should be considered: 
352    What is the age-range of pediatric patients to be studied as part of the safety 
353  extrapolation? 
354    What amount/quality of safety data is available from the reference population? 
355    Are there known on- or off-target effects of the investigational drug relevant to pediatric 
356  safety? 
357    Are data needed to account for age-specific short- and longer-term adverse effects in 
358  pediatric populations, which may not have been identified in studies in the reference 
359  population?  
360    How does the expected treatment duration and treatment effect size in the reference 
361  population compare with the target pediatric population? 
362    How do the expected drug exposures in the reference and target pediatric populations 
363  compare? Does the exposure needed to target a specific PD effect or clinical response 
364  predict a specific toxicity in the target pediatric population?  
365    What information is already known from non-clinical (including mechanistic, in vitro, in-
366  vivo) sources that can be leveraged to the target population?  
367    Are there other differences between the reference and target population that could limit 
368  the extrapolation of safety (e.g., a background therapy used in a target population that 
369  may potentiate a safety signal but is not used in the reference population)? 
370   
371  The amount of safety data that can be extrapolated will depend on the answers to these questions. 
372  Under certain circumstances, no additional safety data will need to be collected beyond that 
373  which has already been collected as part of the efficacy extrapolation approach. If there is 
374  confidence that the available safety data collected are sufficient and address the relevant safety 
375  questions, there is no need to collect additional safety data in a pediatric pre-authorization 
376  program (reference E11(R1)). 
377    378  3.5.2. Additional Safety Considerations 
379  After an assessment of safety extrapolation has been made, there may be a need to collect 
380  additional safety data over and above what has already been collected. This could be the case 
381  when there are remaining gaps and/or age-specific safety concerns in the target population (e.g., 
382  the effect of corticosteroids on reduction in growth velocity in prepubertal children with open 
383  epiphyseal growth plates). Consequently, it may be that longer-term safety data should be 
384  collected in target pediatric populations post-approval. 
385   
386  Special consideration should be given to the collection of pediatric safety data in certain 
387  situations. Examples include: 
388    When the drug is a new molecular entity for a new class of drugs 
389    When there are known on-target age-related safety concerns 
390    When there are significant safety findings noted in the reference population that would be 
391  of special importance in children 
392    When the drug has a narrow therapeutic index 
393   
394  Ultimately, the design of the study(ies) that should be conducted will depend on the identified 
395  gaps in knowledge regarding the safety in the target population(s). Moreover, the use of arbitrary 
396  sample sizes without appropriate scientific justification is discouraged. Early discussion with 
397  regulators is recommended. 
398   
399  3.6. Integration of Evidence and Development of the Pediatric Extrapolation Concept 
400  The goal of the development of the pediatric extrapolation concept is not only to determine 
401  whether pediatric extrapolation can be used, but also to describe assumptions made, detail any 
402  gaps in knowledge, and assess the impact of uncertainties in the available evidence. This section 
403  provides guidance on the review, synthesis, and presentation of information that should be 
404  included in a pediatric extrapolation concept. 
405   
406  Integration of existing evidence 
407  Integration of existing evidence involves a comprehensive review to evaluate the similarities of 
408  the disease and response to treatment between a reference and target population. Once the 
409  evidence is reviewed and integrated, the strength of the evidence is evaluated and gaps in the 
410  evidence are identified. Integration of the evidence should address the following questions: 
411    What is the body of evidence and what is the clinical relevance of the evidence?  
412    What are the strengths and the limitations of the evidence?  
413    How consistent are the findings across the sources and types of data?  
414    What differences exist in the data and how do these differences affect assessment of 
415  similarity? 
416   
417  The answers to these questions will inform what additional information, if any, is recommended 
418  prior to finalizing an extrapolation concept and/or what additional data should be collected in the 
419  extrapolation plan. 
420   
421  Methodologies that can be used to integrate evidence 
422  Quantitative synthesis of existing data should be used to integrate the evidence (see section 4.2). 
423  Use of mechanistic and/or empirical approaches in the synthesis of data should be considered.  424  Inclusion of systems biology/pharmacology data from the reference population(s) should be 
425  considered when population-level data (epidemiological, diagnosis and non-interventional study 
426  data) are available. Meta-analytic techniques for synthesizing efficacy data in the reference 
427  population(s) should also be considered. 
428   
429  There are a variety of approaches available for quantitatively evaluating the similarity of disease 
430  and/or response to therapy in different populations. The most appropriate method will depend 
431  upon the parameters being evaluated for similarity assessment. Frequentist approaches to 
432  evaluate similarity of response between the reference and target populations can be informed by 
433  a comparison of point estimates and their associated confidence intervals. Given the different 
434  levels of precision typically available for estimating parameters in different populations, it will 
435  often be inappropriate to declare similarity purely based on overlapping confidence intervals. 
436  Communication of the manner in which uncertainty has been defined, specified, and otherwise 
437  accounted for in the model development and any simulations used to assess similarity of disease 
438  and/or response is recommended. In addition, any relevant assumptions with respect to the 
439  definition or expression of uncertainty should be specified. 
440   
441  Other exploratory analyses of the available data to assess similarity can also be considered. For 
442  example, if a trial conducted in a reference population has recruited across age groups, 
443  evaluation of the consistency of response in each age group can be considered. Approaches that 
444  can be used to evaluate the consistency of response across subgroups is described in other ICH 
445  guidance (ICH E17 section 2.2.7). 
446   
447  When evaluating similarity of disease and/or response between reference and target populations, 
448  the available data may not permit definitive conclusions to be drawn given the inherent 
449  uncertainties in the data. As such, it is recommended that sponsors discuss the acceptability of 
450  the proposed approach with regulatory authorities. 
451   
452  Knowledge gap identification 
453  Once the available evidence has been integrated, gaps in knowledge should be identified. It may 
454  be that these gaps in knowledge should be addressed before the pediatric extrapolation concept 
455  can be finalized. However, gaps in knowledge do not necessarily preclude a pediatric 
456  extrapolation concept from being finalized. The pediatric extrapolation plan should address what 
457  data should be collected to fill these gaps in knowledge. Knowledge gap identification should 
458  address the following questions: 
459    What are the identified gaps in knowledge?  
460    Do these gaps in knowledge require additional data collection before the pediatric 
461  extrapolation concept can be finalized? If so, when and how will these data be collected? 
462    If these gaps in knowledge do not preclude finalization of the pediatric extrapolation 
463  concept, when and how will these gaps in knowledge be addressed in the pediatric 
464  extrapolation plan? 
465   
466  3.7. Presentation of the Pediatric Extrapolation Concept 
467  Presentation of the pediatric extrapolation concept should include a summary of the overall 
468  similarities between the reference and target populations, the current knowledge gaps, and 
469  limitations of the data. This presentation should include the following:  470    An assessment of the evidence (i.e., overall strengths and weaknesses) of the similarities 
471  and differences between the reference and target population (disease, drug 
472  (pharmacology), response to treatment). This should also include an assessment of the 
473  quantity and quality of evidence. 
474    An assessment of the gaps in knowledge and how they affect the confidence and 
475  uncertainties in the extrapolation concept. In addition, the summary should describe when 
476  and how the gaps in knowledge will be addressed. 
477    An assessment of the available safety information and how this safety information affects 
478  the extrapolation concept. 
479   
480  4.  Pediatric Extrapolation Plan 
481  Once a pediatric extrapolation concept has been developed, the relevant study(ies) should be 
482  detailed in the extrapolation plan. The design of the study(ies) should reflect the information that 
483  needs to be collected as presented in the extrapolation concept. The approach can range from 
484  matching effective and safe exposures in the reference population to generating controlled 
485  efficacy and safety data in the target population. The design, timing, analysis, interpretation and 
486  reporting of studies included in the pediatric extrapolation plan are considered below. 
487   
488  4.1. Dose Selection 
489  Evaluation and selection of an appropriate dose to be studied is critical to achieve target 
490  exposures and responses. Before initiating pediatric studies, the available scientific information 
491  pertaining to the mechanism of action of the drug, the pharmacokinetics of the drug (ADME), 
492  and the effects of physiologic maturation of any organs and targets that are involved in the 
493  predicted exposures and responses to the drug and/or its active metabolites should be assessed 
494  (see section 3.2). As part of planning for dose selection, other considerations (e.g., safety, 
495  formulation, final dosing regimen) should be incorporated. 
496   
497  Exposure-response (E-R) relationships developed from data collected in a reference population 
498  can provide a strong pharmacological basis for justification of the exposure(s) ranges to be 
499  targeted. Subsequent simulations, incorporating relevant knowledge and available models, can be 
500  performed to inform dose selection (see section 4.2). 
501   
502  It is important to note that the identification of safe and effective dose(s) in the program with the 
503  reference population does not always require or result in the demonstration of an exposure-
504  response (E-R) curve. As such, there is no requirement to establish an E-R curve in pediatrics. 
505  However, the lack of demonstrable E-R relationship in the reference population or the inability 
506  to demonstrate similar E-R curves in the reference and target populations does not preclude the 
507  use of exposure matching for dose selection purposes in the pediatric extrapolation plan. Dose 
508  selection based on exposure matching under such circumstances is reasonable and pragmatic and 
509  is predicated on the expectation that comparable response at the target drug response is likely to 
510  be achieved. Furthermore, there are situations in which randomization of pediatric  
511  patients to subtherapeutic doses may be unethical, and available safety data may not support 
512  evaluation of higher doses/exposures. 
513   
514  The aim of pediatric dose selection often is to target exposures similar to those known to be safe 
515  and efficacious in a reference population for further evaluation in a pediatric efficacy/safety  516  study (see section 4.3). In this setting, data in the reference population may be sufficient to 
517  predict doses in the target population. Therefore, separate PK studies may not always be needed 
518  in some age ranges. Confirmatory PK data can be collected as part of the pediatric 
519  efficacy/safety studies with use of sparse PK strategies. However, a separate PK study should be 
520  conducted in certain situations (e.g., drugs with narrow therapeutic range, non-linear PK, and/or 
521  potential differences in the effect of disease on the PK of the drug between the reference and 
522  target populations). Lastly, when PK data are available in an adult reference population with the 
523  disease, and the exposure is within an observed exposure range in a reference pediatric 
524  population with a different disease(s), additional PK assessment may not be necessary in the 
525  target population; however, this approach relies on understanding the effect of disease on the PK 
526  of the drug. 
527   
528  4.1.1. When Dose Ranging Data Should be Collected? 
529  Dose ranging data may be needed as part of the pediatric extrapolation plan. Such circumstances 
530  may include when there is uncertainty in the disease similarity and/or response to treatment; 
531  when there are potential age-related differences in target expression; or when there is lack of 
532  correlation between systemic drug exposures and therapeutic response (e.g., locally acting 
533  drugs). E-R studies can rely on a clinical endpoint or a biomarker response (see sections 4.3 and 
534  4.1.2). Depending on the biomarker and the time course of the disease, dose-ranging to achieve 
535  different degrees of biomarker/clinical response or an intra-patient dose titration to a target 
536  biomarker effect can be considered. 
537   
538  4.1.2. Use of Biomarkers 
539  When available, biomarkers that can be used to support both adult and pediatric development 
540  programs are desirable as are biomarkers that specifically track pediatric disease progression 
541  and/or treatment effect. As an adjunct to the observed biomarker time course, a physiologic 
542  and/or mechanistic representation that describes such data in response to drug therapy is highly 
543  beneficial. Modeling and simulation approaches such as physiologically based pharmacokinetic 
544  (PBPK) modeling and quantitative system pharmacology (QSP) models can be useful to support 
545  the biomarker strategy and choice of clinical endpoints in children. 
546   
547  A biomarker may or may not need to be validated, although use of a validated biomarker may 
548  require less justification. Methodological considerations (e.g., the effect of missing data, and the 
549  results of sensitivity analyses to departures from any assumptions) should also be included in the 
550  evaluation of the proposed endpoint [see ICH E9(R1)]. 
551   
552  If a biomarker has been proposed for use as a primary analysis in the target population and 
553  cannot be measured in the reference population, relevant clinical outcomes in the target 
554  population should at least be measured as well, to try and understand the relationship between 
555  the variables. 
556   
557  4.1.3. Scenarios for Dose Selection 
558   
559  4.1.3.1. When only PK data are Needed to Establish Efficacy 
560  When there is strong evidence 1) to support similarity of disease between the reference and 
561  target population and 2) that exposures in the reference population will provide similar response  562  in the target population (e.g., infectious diseases, partial onset seizures), targeting effective 
563  exposures in the reference population as the basis for pediatric extrapolation (i.e., exposure 
564  matching) may be reasonable. Modeling and simulation strategies should be applied to support 
565  the initial dose selection in the exposure matching study in the target population (see section 
566  4.2). Allometric scaling can be used to account for weight-based changes in clearance and 
567  volume of distribution and maintain consistent exposures across various age/body weight groups. 
568  Models should also take into account other factors that may contribute to variability in exposures 
569  such as maturation. In addition, model-informed dose selection should include an assessment of 
570  the feasibility and practicality of the dosing strategies. For example, fixed-dose combinations, 
571  dose volume constraints, and drug-device combination can influence the dosing strategy. Once 
572  PK data are obtained in the target population, the proposed dosing regimen should be re-
573  evaluated through simulation techniques before a final dosing regimen for proposed product 
574  labeling is selected. 
575   
576  Endpoint: Target exposure metric 
577  When the pediatric extrapolation strategy relies on matching adult exposures, the target exposure 
578  metric(s), range, and acceptance criteria should be prospectively specified and should be defined 
579  in the context of the disease, treatment regimen, route of administration, and formulation. The 
580  target exposure metric should be based on the exposure range associated with treatment response 
581  (efficacy and/or safety) and can be derived from established exposure-response relationships or 
582  observed data in the reference population. The selected target exposure metric(s) should be 
583  associated with the treatment response, and an adequate discussion and justification should be 
584  provided based on, but not limited to, the mechanism of action and the metrics previously 
585  established in the exposure-response relationships in the reference population. It is often useful 
586  to present several exposure metrics. For example, AUC0-t or Cmin may correlate with efficacy 
587  whereas Cmax may be more informative for safety. In cases where systemic exposure does not 
588  correlate with efficacy (e.g., most locally acting drugs), additional assessment of response might 
589  be needed (see section 4.1.3.2 and 4.3). 
590   
591  Sample size 
592  The sample size for a pediatric PK study should be sufficient to meet the objectives of the study 
593  and be based on quantitative methods (modeling and simulation and/or statistical approaches). 
594  Adequate representation of subgroups (e.g., body weight ranges, age ranges) should be 
595  considered and justified. The sample size justification and its feasibility in the targeted indication 
596  should include the following: 
597    The availability of patients in a specific body weight/age range  
598    The adequacy of the sample size to demonstrate precision in key PK parameters in the 
599  pediatric population such as clearance and volume of distribution 
600    The adequacy of the sample size to match the pre-specified target exposure range (e.g., 
601  the interquartile range for the PK metric(s) in the reference population) 
602    The methodology(ies) used to determine the sample size 
603   
604  Modeling and simulation techniques such as optimal design and/or clinical trial simulation 
605  should be conducted to justify the timing and number of PK samples. The timing of sample 
606  collection should be aligned with clinical care whenever possible [see ICH E11(R1) section 
607  2.4.1].  608   
609  Analysis and reporting 
610  Different presentations of the exposure data in the target and reference populations should be 
611  available to inform regulatory decision making. A single acceptance boundary for all drug 
612  products and drug classes (as compared to bioequivalence testing) will not provide a meaningful 
613  approach in the setting of pediatric extrapolation. An evaluation of confidence intervals for the 
614  mean differences in key exposure metrics such as AUC and Cmax could be an acceptable 
615  approach. The chosen boundaries of the confidence interval should reflect the context of the 
616  therapeutic range of the drug and the risk-benefit of the product for a given pediatric indication. 
617   
618  A model-based comparison (that can integrate all available data) is generally preferred rather 
619  than a descriptive comparison of observed adult and pediatric exposure data alone. In addition, 
620  inter-individual variability should be considered in establishing exposure similarity rather than 
621  comparing means alone. A simulation of the percent of subjects at different age/weight ranges 
622  that lie within (or outside) a pre-defined exposure range may provide a more meaningful 
623  assessment of exposure similarity. 
624   
625  In general, the most relevant covariate to influence PK in pediatric patients is body weight. In the 
626  youngest pediatric patients (e.g., infants and neonates), in addition to body weight, age is also an 
627  important covariate to account for relevant organ maturation. 
628   
629  Relevant predefined exposure metrics should be presented graphically versus body weight and/or 
630  age on a continuous scale. Relevant age and body weight ranges should be depicted in figures to 
631  allow for clear visualization of important covariates (e.g., dose(s), age, weight) as well as in 
632  tabular format. The reference range in the adult population (e.g., median and outer percentiles of 
633  the distribution of observed or simulated data) should also be presented graphically and in 
634  tabular format. 
635   
636  4.1.3.2. When Effect on a Biomarker is Needed to Establish Efficacy 
637  When exposure matching alone is insufficient to establish efficacy, biomarkers can be used as 
638  part of the extrapolation plan. In this situation: 
639    Use of a validated biomarker as a surrogate endpoint is recommended but not required. 
640    The choice of the biomarker endpoint should be supported by available data in the 
641  reference and target populations and justified in the extrapolation plan. 
642    A biomarker on the causal pathway that is correlated with clinical efficacy in the 
643  reference population is often acceptable and should be justified also with regard to its 
644  relevance to the target population. 
645    Models can be used to estimate the quantitative relationships between biomarkers and 
646  clinical efficacy (see section 3.6). 
647   
648  In order to rely on the use of dose/exposure to achieve a biomarker effect, it is important to have 
649  confidence that there is a relationship between the biomarker effect and efficacy in the reference 
650  population. Models could investigate the mechanistic basis for selected biomarkers, facilitate the 
651  analysis of biomarker data, and optimize the data collection needed to support and/or confirm the 
652  relationship between the biomarker and efficacy in the reference population (see section 4.2). 
653    654  Sample size 
655  Quantitative methods (modeling and simulation or statistical approaches) should be used to 
656  derive sample size for PK/biomarker and biomarker endpoints. The sample size for the study can 
657  vary depending on variability in key drivers such as PK and PK/PD. Consideration of the timing 
658  and number of data points per subject for both PK and PK/PD should determine the appropriate 
659  sampling. 
660   
661  Analysis and reporting 
662  The data used in the analysis should be described, with a focus on the important elements 
663  relevant to the objectives of the analysis, i.e., the comparison between the biomarker effect in the 
664  target population and that in the reference population. A therapeutic range of the biomarker 
665  effect that provides a meaningful assessment of similarity between the reference and target 
666  populations should be pre-defined. 
667   
668  Results should be summarised with adequate graphical and tabular displays, e.g., illustrative 
669  plots for clinical interpretation. The clinical relevance of the results should be discussed, 
670  including the impact of any sensitivity analyses (see section 4.1.3.1 Analysis and reporting). The 
671  analysis and reporting should confirm a dose-exposure-response relationship that establishes the 
672  effective dose(s). 
673   
674  4.1.4. Other Considerations 
675  As has been emphasized throughout this guideline, pediatric extrapolation should be considered 
676  as a continuum. Because of this continuum, there can be some overlap in the types of 
677  extrapolation plans that are developed. For example, an extrapolation plan can include a scenario 
678  that only requires collection of PK in the target population as the primary objective, but 
679  additional secondary clinical outcome measures can be included in order to increase confidence 
680  with the PK-only approach. There can also be some overlap between the design of a single-
681  arm PK/PD study and a single-arm, uncontrolled study that relies on a clinical efficacy endpoint 
682  (see section 4.3.1). Ultimately, the specific study designs used in any extrapolation plan should 
683  be justified based on the extrapolation concept and discussed with regulatory authorities. 
684   
685  4.2. Model-Informed Approaches 
686  Modeling and simulation approaches are powerful tools that can be used, for example, to 
687  examine and inform study design, derive dosing recommendations, or perform sensitivity 
688  analyses. Quantification of relevant relationships (e.g., dose-exposure, exposure-response) 
689  provides an important foundation to conduct simulations in support of the dose selection. In 
690  addition, simulations of therapeutic window(s) associated with relevant PK or PK/PD endpoints 
691  can be explored prior to conducting a pediatric study. Modeling and simulation can be used to 
692  validate the pediatric extrapolation concept after completion of the pediatric study. When 
693  simulations are used for regulatory decisions, it is important to provide information that the 
694  models are fit for simulation purposes and that model assumptions and the simulation set up are 
695  clearly reported. Typically, this information would be provided in the form of a modeling and 
696  simulation plan that the sponsor generates for internal documentation purposes but is also 
697  suitable for interaction with regulators. 
698   
699  The availability of the various data sources dictates, in part, the methodologic approach, with  700  more top-down approaches (e.g., traditional PK/PD, population-based PK/PD) reliant on adult 
701  data and bottom-up approaches (e.g., PBPK, QSP) dependent on physicochemical, in vitro and 
702  preclinical in vivo data. For ADME prediction, data of interest include the physicochemical 
703  properties of the drug, in vitro data describing individual PK attributes, PK/PD data from 
704  preclinical in vivo experiments, and any PK/PD data from adults. 
705   
706  When using existing models (e.g., population PK, PBPK, population PK/PD models), the 
707  specific characteristics of the target population, such as relevant body size and organ maturation, 
708  should be incorporated in the model. Depending on the available data and goals of the modeling, 
709  there are several techniques that can be used to incorporate information from the reference 
710  population in the analysis of the target population; for example, using models based on the 
711  reference population, analysis with pooled datasets, or Bayesian approaches with prior 
712  distributions for model parameters. 
713   
714  When making model-based assessments, the components of the model may have complex 
715  interrelationships (e.g., correlation of parameters and/or assumptions) that should be captured in 
716  the structure of the model along with any time dependencies. These features should be 
717  incorporated into the model at inception. Model equations and assumptions underlying the model 
718  structure or dataset should be clearly presented so that their relevance to the overall strategy, 
719  model predictions and elements of uncertainty can be properly assessed. Not all data and model 
720  elements are equally valuable; therefore, assumption testing is an important aspect of any 
721  extrapolation exercise and should be integrated into the analysis plan and report. Given the scope 
722  of model assumptions, there should be multidisciplinary input to fully evaluate the assumption-
723  testing exercise. 
724   
725  It is important to distinguish between different sources of uncertainties and variance. For 
726  example, there is inherent variability in samples taken between individuals (i.e., between subject 
727  variability), which is a biological phenomenon and the magnitude of which can be directly 
728  supported by data. There is also uncertainty in model parameters which cannot be measured 
729  directly but are influenced by data content, or lack thereof. Collecting additional data can help 
730  improve the precision of these estimates. There are also parameters that should be specified 
731  where there is more limited or no data to support values chosen, and there is a degree of 
732  arbitrariness in their choice, which is inherently uncertain. All of these can contribute to overall 
733  uncertainty in the results, and the different contributions that these could have should be 
734  addressed and justified during the exercise. 
735   
736  4.3. Efficacy Studies 
737  When clinical studies are required in order to generate efficacy data in a pediatric extrapolation 
738  plan, one of the most important design decisions will be the choice of control arm. The options 
739  may include a randomised concurrent control, a formal statistical comparison against an external 
740  control, or a single arm trial. The choice will be influenced by the scientific question(s) identified 
741  in the pediatric extrapolation concept. 
742   
743  4.3.1. Single Arm Efficacy Studies 
744  In some situations, single arm studies may be the most appropriate way of generating the 
745  required evidence. This would be the case, for example, when the standard of evidence in the  746  reference population is a single arm trial. When designing the study, how the primary efficacy 
747  objective would be evaluated should be defined using a pre-specified threshold. 
748   
749  The sample size of studies should be calculated to ensure the threshold is met, or to ensure that 
750  an estimate of sufficient precision is obtained. External data can be used to contextualise the 
751  results (e.g., using published literature to understand the context of the results of the study with 
752  respect to current clinical practice, but without requiring a formal comparison of efficacy to 
753  external data). 
754   
755  4.3.2. Externally Controlled Studies 
756  It may be possible and appropriate in some circumstances to use external data as the formal 
757  comparator in a trial. This could be from the comparator arm in the reference population, 
758  relevant control arms from other randomized controlled trials (RCTs), or real-world evidence 
759  sources in the target population. Using external data beyond these sources, e.g., from different 
760  pediatric populations, different diseases or where different endpoints are used, is more 
761  challenging and should be justified. 
762   
763  As with any other study without randomized concurrent control, drawing causal inferences is 
764  more challenging. Since the data are compared directly with a data source external to the study, 
765  appropriate statistical methods should be used to account for differences between the 
766  populations. It is important to reflect that these studies would still be controlled, albeit with a 
767  non-randomized control, which differs from the approach of just comparing to a threshold. 
768   
769  4.3.3. Concurrent Controlled Efficacy Studies 
770  In some situations, the data generated to date and the outputs of the pediatric extrapolation 
771  concept are such that randomized controlled efficacy studies would be needed as part of the 
772  pediatric extrapolation plan to be able to draw benefit risk conclusions. Based on the pediatric 
773  extrapolation concept, the need for controlled studies and the ability to extrapolate leads to study 
774  designs different than those that were required in the reference population. This will lead to a 
775  different relationship between the false positive rate, the false negative rate and sample size that 
776  is not the same as it is in the reference population. When the sample size is limited, the relative 
777  importance of false positive and false negative results should be considered carefully. 
778   
779  It follows that extrapolation options may comprise many different design options that can be 
780  used to generate data, but not according to the traditional approach (e.g., p-value less than 0.05 
781  generated in a frequentist fashion from an RCT). The extrapolation approach will result in a 
782  sample size smaller than one would expect for a standalone efficacy study. If the study is 
783  powered to meet a relaxed success criterion with a significance threshold larger than 0.05, this 
784  should be justified in advance. 
785   
786  An alternative approach for active controlled trials may be to maintain the conventional type I 
787  error rate but widen the non-inferiority margin usually used in de novo adult development, 
788  especially when the aim is not to demonstrate efficacy per se but to demonstrate that efficacy is 
789  in line with prior expectations based on the extrapolation concept. It will be important to ensure 
790  the point estimate obtained should be consistent with that in the reference population. 
791    792  4.3.4. Incorporation of External Data 
793  When identifying which information will be incorporated into the analysis of the pediatric study, 
794  relevant data should be identified through a systematic search using pre-specified 
795   selection criteria. Ideally, the sources of information to be leveraged should be agreed upon with 
796  regulatory authorities ahead of time. However, it is possible that the external data themselves 
797  may not be available yet, for example, if generated from trials running in the reference 
798  population in parallel to the study in the target population or borrowed across age groups in the 
799  same study. 
800   
801  The types of information that could be leveraged in an analysis include individual patient data 
802  and/or aggregate data from other sources. Having access to individual patient data in the 
803  reference population enables comparison of the distribution of baseline prognostic factors with 
804  the target population. Potential differences between the study from which the reference data will 
805  be derived and the data generated in the target population can be adjusted and accounted for in 
806  the analysis as much as possible. 
807   
808  4.3.5. Quantifying the Impact of Use of Reference Data 
809  It is important to understand a priori how much available information is being incorporated into 
810  the design and analysis to support the interpretation of the pediatric trial. In particular, it is of 
811  relevance to know how much of the data that has been generated in the reference population is 
812  being used in the exercise, but also how much of the data generated in the reference population is 
813  relative to the amount of data generated in the target population. If the available information 
814  (based on reference data, or outputs from a modeling and simulation exercise) is summarised as a 
815  statistical distribution, then the effective sample size is a good way of describing how much 
816  information is being used. 
817   
818  If Bayesian approaches are used, different ways of using the prior information, for example, by 
819  using a mixture prior or power prior, will have a different effective sample size depending on the 
820  choice of parameters used in the model. If such strategies are employed, sensitivity analyses 
821  looking at the effective sample size under different values of these parameters will better help 
822  understand the design properties. Regardless of the approach used, the method of borrowing 
823  proposed should be pre-specified, and sensitivity analyses to understand the effect on operating 
824  characteristics of different amounts of borrowing will better help understand the design 
825  properties. 
826   
827  Sometimes it may not be appropriate to use the reference data as is, and the data should be 
828  modelled to match the target population more closely. This will be the case when there exist 
829  known differences in the disease (e.g., severity) that can be quantified and predicted based on 
830  measured covariates, though the extrapolation concept is still applicable. In other situations, 
831  there exist known differences in study design (e.g., the endpoint measured is different in the 
832  target population or the endpoint is measured at a different time) though the disease is considered 
833  to be similar to a degree that allows extrapolation. How the reference data are used in this 
834  situation would have to be considered on a case-by-case basis depending on the degree of 
835  similarity of disease, drug pharmacology, and response to treatment. 
836   
837  It can be possible to base a pediatric extrapolation plan using a biomarker, surrogate endpoint, or  838  clinical endpoint as the primary endpoint in the target population, even if it is not the primary 
839  endpoint in the reference population [see ICH E11(R1) section 5.1.1]. In this scenario, an 
840  evaluation of the robustness of the correlation of the proposed endpoint to the primary efficacy 
841  endpoint in the reference population should be conducted. Where relevant, it may be prudent to 
842  initiate the evaluation of potential pediatric endpoints as part of the adult development program 
843  prior to their incorporation into the pediatric program. 
844   
845  4.3.6. Presentation and Justification for the Pediatric Trial 
846  Diagrams that represent the overall planned trial design for the extrapolation plan are helpful, 
847  especially if the design is complex. This may be the case if, for example, there is an adaptive 
848  design, or a trial with multiple stages evaluating different aspects of clinical development in each 
849  stage. When evaluating a trial design, determining what potential results will lead to a successful 
850  study based on pre-defined criteria can help to understand what magnitude of treatment effect 
851  would need to be observed for a trial to be declared a success. Tables or plots of different critical 
852  thresholds could be useful if there is uncertainty around the most appropriate threshold. 
853   
854  If a Bayesian design is used, the full operating characteristics should be provided. Additionally, 
855  the results of an analysis of the data alone should always be provided. 
856   
857  4.3.7. Analysis, Reporting, and Interpretation 
858  If a frequentist design is used, an alternative threshold to cross other than the standard two- sided 
859  significance level of 5%. should be agreed upon in advance, and a frequentist analysis compared 
860  to this alternative threshold provides a justification of the pediatric extrapolation concept. If the 
861  endpoint is the same in the reference population as the target, ideally the same analysis method 
862  should be used in the target population as in the reference population. A frequentist meta-
863  analysis approach combining reference and target data could be conducted if it is appropriate to 
864  formally analyze the data together. 
865   
866  If a Bayesian design is used, which explicitly leverages external data, there are many more 
867  choices to be made for the analysis. This analysis should be pre-specified and updated as data are 
868  generated. Visualisations to better understand the relationship between operating characteristics 
869  and underlying parameters and assumptions are helpful. Plots of posterior distributions resulting 
870  from Bayesian analyses may better contextualize the summary statistics derived from Bayesian 
871  distributions. If data external to the trial are incorporated into the analysis, the reporting should 
872  explicitly describe this and discuss how and when these data were originally generated and 
873  where they were reported, along with a justification as to why their inclusion is considered to be 
874  appropriate. 
875   
876  Ideally, the interpretation of a study is aided if the success criteria are described and agreed upon 
877  in advance with regulatory agencies. The criteria for success can be a p-value, or if reference 
878  data are explicitly borrowed, Bayesian success criteria, such as credible intervals, excluding 
879  critical values, or the probability that one treatment is better than the other by at least a certain 
880  pre-specified amount. More than one success criterion may be appropriate. For example, if a 
881  non-inferiority margin wider than would be accepted in adults is used, it is also possible to 
882  specify the point estimate of treatment effect that would need to be demonstrated for non-
883  inferiority to be met for any given sample size and variance. This could help in demonstrating  884  efficacy by providing additional reassurance of the expected treatment effect. It is important to 
885  understand how similar the target data are to the reference data and to use metrics to define such 
886  similarity. If the observed data in the study are not similar to the observed reference data, this may 
887  limit the applicability of the pediatric extrapolation concept and the amount of data that may be 
888  considered reasonable to borrow. 
889   
890  Nevertheless, if the data in the target population is substantially better than the reference 
891  population in terms of the point estimate of effect, but statistical significance without borrowing 
892  has failed to be achieved due to a small sample size, it may be of interest to understand how 
893  much weight needs to be put on this reference data before a positive conclusion is drawn (i.e., 
894  using a tipping point analysis). 
895   
896  The more complex a statistical model, and the more parameters that need to be assumed, the 
897  greater the need for appropriate and wider ranging sensitivity analyses [ICH E9 (R1)]. It is 
898  beneficial to discuss these sensitivity analyses in advance, and to investigate how robust the 
899  interpretation of the primary analysis might be to changes in these parameters. Such analyses 
900  should be carefully selected to investigate the assumptions made with the primary estimator and 
901  other limitations with the data. 
902   
903  Methods of leveraging source data in the analysis of a pediatric trial 
904  When deciding on the method to use, simulation can be a useful tool to inform the choice of 
905  analysis strategy, with a view to optimizing the trade-off between bias, power, and type I error 
906  rate control. Various methods exist that aim to limit the borrowing if the data generated are not 
907  similar to the prior belief about them. As an example, one possible method amongst many is to 
908  use a robust prior: a two-component mixture prior where one component is an informative prior 
909  based on the source data and the second is a weakly informative prior independent of the source 
910  evidence. The weakly informative component should be carefully chosen to ensure adequate 
911  borrowing behavior. The prior weight attributed to the informative component of the mixture 
912  prior can be considered as the prior belief about the plausibility and acceptability of the 
913  extrapolation concept. The closer the value to 1, the more confidence there is. If small changes in 
914  the pre-specified parameters such as the weighting parameter above, lead to large changes in the 
915  operating characteristics of the study, the method may not be sufficiently robust. 
916   
917  A sensitivity analysis such as a tipping point analysis can be a useful tool for retrospectively 
918  assessing the robustness of conclusions to the strength of prior assumptions about similarity of 
919  source and target population parameters. When source data are drawn from several different 
920  sources, such as adult RCTs, epidemiological studies or registry data, the quality of data from the 
921  various sources may differ, and their relevance to the new pediatric trial may differ. In this case, 
922  careful consideration should be given to both the construction of the prior itself, and the method 
923  used to include the data in the analysis. 
924   
925  5.  Additional pediatric extrapolation plan considerations 
926   
927  5.1. Safety Plan 
928  As described above, the extrapolation concept should include a discussion of the extrapolation of 
929  safety and a thorough justification to support any conclusions about the acceptability to  930  extrapolate safety information from the reference to the target population (see section 3.5). The 
931  approach to safety data collection should reflect the scientific question(s) that needs to be 
932  answered, the knowledge gaps identified, and the uncertainties that are being addressed to 
933  support the safety of the drug in the target population. Even when extrapolation of safety data is 
934  justified, there may be additional safety issues that should be addressed. A comprehensive safety 
935  plan, including the need for pre- and post-marketing safety data collection, should be described 
936  in the extrapolation plan. 
937   
938  5.2. Inclusion of Adolescents in Adult Trials 
939  The enrollment of adolescents into adult clinical trials may hasten adolescent access to safe and 
940  effective treatments as well as accelerate the gathering of needed pediatric data. Historically, 
941  pediatric trials have not been initiated until after adult development has been completed and/or 
942  after the drug has been approved for adults. As a result, enrollment into pediatric trials may be 
943  slow due to the off label pediatric use of the drug, further delaying broader pediatric and 
944  adolescent access to effective treatments. Inclusion of adolescents in some disease- and/or target-
945  appropriate adult trials may address this problem. If the adolescent results are used to bridge the 
946  extrapolation of adult efficacy and/or safety to younger children, the similarity of disease and 
947  response to treatment between the younger children and adolescents, and any uncertainties, 
948  should be addressed. 
949   
950  The decision to include a pediatric cohort (e.g., an adolescent subgroup 12 to 17 years of age) in 
951  an adult (e.g., > 18 years of age) clinical trial assumes the disease and response to treatment are 
952  sufficiently similar between the adolescent and adult patients. As such, the objective(s) of 
953  including adolescents and adults in a single trial should be framed within the context of the 
954  extrapolation concept. Additional data to inform adolescent dosing may not be necessary as the 
955  adolescent and adult PK are generally similar. In such situations, the impact of lower body 
956  weight in adolescents should be carefully considered. 
957   
958  If the disease and response to treatment are sufficiently similar, the adolescent and adult 
959  populations can be combined into a single analysis of efficacy. The purpose and statistical 
960  methods for a separate analysis of the adolescent subgroup should be carefully considered so that 
961  any identified differences or uncertainties are addressed. Such subgroup analyses should be 
962  interpreted cautiously; the strength of any conclusion about the extrapolation of efficacy (or lack 
963  thereof) based solely on exploratory subgroup analyses may be limited (see ICH E9). 
964   
965  There may be ethical and operational challenges associated with including adolescents in an 
966  adult trial, such as: (1) different standards for the acceptable balance of risk and potential benefit; 
967  (2) whether adolescents should be exposed to a placebo control (which may be used more often 
968  in an adult trial); (3) the need for parental permission in addition to adolescent assent; (4) the use 
969  of the same primary endpoint in both the adolescent and adult population; (5) the need for 
970  pediatric-specific study sites; and (6) the willingness of pediatric investigators to participate in a 
971  subsequent pediatric only trial that would now exclude adolescents. If confronted with these 
972  challenges, different trial designs can also be considered (such as an adolescent trial run in 
973  parallel to the adult trial). Nevertheless, when the disease and response to treatment are 
974  sufficiently similar between adolescent and adult subjects, there should be a strong justification 
975  for why adolescents are not being included in an adult clinical trial or being studied in a parallel  976  trial.