1. The Promise and Peril of Pediatric Extrapolation
Robert “Skip” Nelson, MD PhD FAAP
Child Health Innovation Leadership Department (CHILD)
July 30, 2018

2. Disclosure
This presentation is intended for educational purposes only. Statements of fact and opinions expressed are those of the participant individually and, unless expressly stated to the contrary, are not the opinion or position of any company, institution or third party entity.
Robert Nelson is a full-time employee of Johnson & Johnson.

3. Outline of Topics The Concept of Pediatric Extrapolation
Application to Efficacy, Safety and Dosing
Different Approaches to Extrapolation
FDA “categories” compared to EMA “continuum”
Statistical Approaches to Extrapolation
P values, type 1 error (α) rates and Bayesian inference

4. (Non-mathematical) Extrapolation†
Extrapolation is an inductive inference that extends known experience and/or data (“source”) into an area not known or previously experienced (“target”) to arrive at a (credible, but inherently uncertain or probabilistic) knowledge of the unknown area.
†In mathematics, extrapolation is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to interpolation, which produces estimates between known observations.
Start with a general definition of extrapolation, and distinguish this definition from a related but different use of the concept of extrapolation in mathematics.

5. Pediatric Extrapolation in Drug Development
The use of existing knowledge establishing the efficacy of an investigational product in one population (e.g., adult) to conclude (or infer) that the product is also effective in another (e.g., pediatric) population.
This inductive inference assumes that the two populations are sufficiently similar in those attributes that may impact on the inferred knowledge.
Pediatric extrapolation in the context of drug development involves the extrapolation of efficacy from the source population to the target population.

6. Extrapolation of Efficacy
Efficacy is a causal inference that is extrapolated from the “source” population to the “target” population based on a descriptive inference (i.e., sufficiently similar).
The (reasonable) clinical assumption that the course of the disease and the response to treatment are the two key “descriptive” attributes on which extrapolate efficacy was proposed a priori by FDA in 1992.†
As the science of extrapolation evolves, these attributes may change (e.g., tissue agnostic targeted therapies).
†“Specific Requirements on Content and Format of Labeling for Human Prescription Drugs: Proposed Revision of “Pediatric Use” Subsection in the Labeling” Federal Register/Vol. 57, No 201, dated October 16, 1992

7. Use of Extrapolation
“A pediatric use statement may also be based on adequate and well-controlled studies in adults, provided that the agency concludes that the course of the disease and the drug's effects are sufficiently similar in the pediatric and adult populations to permit extrapolation from the adult efficacy data to pediatric patients. Where needed, pharmacokinetic data to allow determination of an appropriate pediatric dosage, and additional pediatric safety information must also be submitted.”
59 Fed. Reg (proposed in 1992)

8. Extrapolation of Safety
The “orthodox” view was that safety could never be extrapolated. However, this position may reflect a categorical approach to extrapolation (i.e., yes/no), rather than “borrowing” data from other sources to varying degrees (i.e., to reduce need for additional pediatric-specific data).
The science of extrapolating safety has not developed to where there are descriptive attributes (with exceptions) on which we can infer safety from one population to another (but such a science could be developed).

9. Extrapolation of Dosing
Based on experience, we have learned that the necessary drug dosing regimen for an adolescent (e.g., ≥ 12 yrs.) to achieve the same drug exposure as an adult is the same. Strictly speaking, this is an inductive inference (i.e., extrapolation).
The use of physiologically-based pharmacokinetic (PBPK) modeling reduces the need for classic pharmacokinetic studies in younger children, and allows the selected pediatric dosing regimens to be confirmed using sparse PK sampling in safety and/or efficacy clinical trials.

10. Outline of Topics The Concept of Pediatric Extrapolation
Application to Efficacy, Safety and Dosing
Different Approaches to Extrapolation
FDA “categories” compared to EMA “continuum”
Statistical Approaches to Extrapolation
P values, type 1 error (α) rates and Bayesian inference

11. New or Expanded Indication
Approaches to Extrapolation (Assessment of 166 products between )
Extrapolation
Supportive Evidence Requested From Pediatric Studies
Products n/N (%)
New or Expanded Indication
None
Two adequate, well-controlled, efficacy and safety trials plus PK data.
19/166 (11)
7/19 (37)
Oncology products only: sequential approach starting with phase 1/2. Do not proceed if no evidence of response.
10/166 (6)
3/10 (30)
Partial
Single, adequate, well-controlled, efficacy and safety trial (powered for efficacy) plus PK data.
67/166 (40)
35/67 (52)
Single, controlled or uncontrolled, efficacy and safety trial (qualitative data) plus PK data.
20/166 (12)
15/20 (75)
Single exposure-response trial (not powered for efficacy) plus PK and safety data, PK/PD and uncontrolled efficacy plus safety data, or PK/PD plus safety data.
26/166 (16)
19/26 (73)
Complete
PK and safety data.
9/10 (90)
Safety data only.
14/166 (8)
6/14 (43)
17%
68%
14%
Adapted from Table 1: Dunne J et al. Pediatrics 2011;128;e1242.

12. Substantial Evidence of Effectiveness Regulatory Basis for FDA Extrapolation Categories
“evidence consisting of adequate and well-controlled investigations, including clinical investigations, by experts qualified by scientific training and experience to evaluate the effectiveness of the drug involved” [1962] - Section 505(d), Food, Drug & Cosmetic Act
“Congress generally intended to require at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness.” (emphasis added)
“FDA has been flexible…, broadly interpreting the statutory requirements to the extent possible where the data on a particular drug were convincing.”
In 1997, “section 505(d) [was amended]… to make it clear that [FDA] may consider ‘data from one adequate and well-controlled clinical investigation and confirmatory evidence’ to constitute substantial evidence if FDA determines that such data and evidence are sufficient to establish effectiveness.” (emphasis added)
FDA Guidance - May 1998 (

13. New or Expanded Indication
Approaches to Extrapolation (Assessment of 166 products between )
Extrapolation
Supportive Evidence Requested From Pediatric Studies
Products n/N (%)
New or Expanded Indication
None
Two adequate, well-controlled, efficacy and safety trials plus PK data.
19/166 (11)
7/19 (37)
Oncology products only: sequential approach starting with phase 1/2. Do not proceed if no evidence of response.
10/166 (6)
3/10 (30)
Partial
Single, adequate, well-controlled, efficacy and safety trial (powered for efficacy) plus PK data.
67/166 (40)
35/67 (52)
Single, controlled or uncontrolled, efficacy and safety trial (qualitative data) plus PK data.
20/166 (12)
15/20 (75)
Single exposure-response trial (not powered or efficacy) plus PK and safety data, PK/PD and uncontrolled efficacy plus safety data, or PK/PD plus safety data.
26/166 (16)
19/26 (73)
Complete
PK and safety data.
9/10 (90)
Safety data only.
14/166 (8)
6/14 (43)
Adapted from Table 1: Dunne J et al. Pediatrics 2011;128;e1242.

14. New or Expanded Indication
Reframing Extrapolation of Efficacy Continuous concept reflecting percent “borrowing” of adult efficacy data
Extrapolation
Supportive Evidence Requested From Pediatric Studies
Products n/N (%)
New or Expanded Indication
None
Two adequate, well-controlled, efficacy and safety trials plus PK data.
19/166 (11)
7/19 (37)
Oncology products only: sequential approach starting with phase 1/2. Do not proceed if no evidence of response.
10/166 (6)
3/10 (30)
Partial
Single, adequate, well-controlled, efficacy and safety trial (powered for efficacy) plus PK data.
67/166 (40)
35/67 (52)
Single, controlled or uncontrolled, efficacy and safety trial (qualitative data) plus PK data.
20/166 (12)
15/20 (75)
Single exposure-response trial (not powered or efficacy) plus PK and safety data, PK/PD and uncontrolled efficacy plus safety data, or PK/PD plus safety data.
26/166 (16)
19/26 (73)
Complete
PK and safety data.
9/10 (90)
Safety data only.
14/166 (8)
6/14 (43)
No borrowing of adult data
Complete (100%) borrowing of adult data
Partial (1 to 99%) borrowing of adult data
Adapted from Table 1: Dunne J et al. Pediatrics 2011;128;e1242.

15. Extrapolation EMA Reflection Paper (9 October 2017)
Extrapolation - inference from known (source) to unknown (target).
to use known facts as the starting point from which to draw inferences or conclusions about something unknown
to predict by projecting past experience or known data
Use of extrapolation reduces “the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions.”
Requirements for evidence in target population will be a continuum, from identification of appropriate dosing, quantification of PK/PD relationship, to a full clinical development if no extrapolation possible.
Extrapolation is defined as “extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, in order to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions.”
Requirements for evidence generation in the target population will be a continuum, ranging from identification of an appropriate posology for the target population and quantification of a PK/PD relationship through to a full clinical development in the event that no extrapolation is possible.

16. Extrapolation Framework Stepwise Approach; Emphasis on Quantitative Methods
Extrapolation Concept
Evidence synthesis
Disease manifestation and progression
Clinical response
Characterization of PK and PD
Explicit predictions of drug effects in target population
Document and evaluate sources of uncertainty and assumptions
Extrapolation Plan
Address scientific questions that remain to be answered through clear study objectives
Regulatory decision-making based on totality of evidence (source & target population)
Validation of Extrapolation Concept
If data do not confirm extrapolation concept, concept and plan to generate more data should be re-assessed.
Revising
Learning
Based on the draft EMA “Reflection Paper on the use of extrapolation in the development of medicines for paediatrics” (EMA/199678/2016; 9 Oct. 2017)

17. Outline of Topics The Concept of Pediatric Extrapolation
Application to Efficacy, Safety and Dosing
Different Approaches to Extrapolation
FDA “categories” compared to EMA “continuum”
Statistical Approaches to Extrapolation
P values, type 1 error (α) rates and Bayesian inference

18. P Values and Type 1 (α) Error Rates
“The P value is defined as the probability, under the assumption of no effect or no difference (the null hypothesis), of obtaining a result equal to or more extreme than what was actually observed.”
Common error: P value is the probability that the null hypothesis is true.
Type 1 (α) Error: concluding there is a nonzero effect when there is, in truth, no effect (i.e., wrongly rejecting the null hypothesis or false- positive error).
Common misperception: P value “is a special kind of false-positive error rate, specific to the data in hand.”
Problem: clinical judgment should be used (but has largely disappeared) in choice of error rates given relative seriousness of two types of errors (α, β).
Goodman SN. “Towards Evidence-Based Medical statistics. 1: The P Value Fallacy.” Ann Intern Med. 1999;130:

19. Bayesian Inference Learning from Experience
Prior Distribution
(evidence-based plausibility of different values of the treatment effect excluding clinical trial data)
Likelihoods (Bayes Factor)
(support from clinical trial data alone for different treatment effect values)
Posterior Distribution
(final opinion about the treatment effect)
p(H|E)
p(H)
p(E|H)
p(E|~H) p
Probability H
Hypothesis is true ~H
Hypothesis is not true E
Evidence (Data)
p(H|E)
Probability hypothesis is true given the evidence
In short, we combine the past (prior) with the present (likelihoods) to make decisions about the future (posterior conclusions).

20. Example 1: Pediatric Priors with Different Levels of Borrowing (%)
HR=0.6
Log hazard ratio (HR) normally distributed, with mean equivalent to observed source population.
Schoenfeld (1981)
Variance is inversely related to the number of borrowed events.
Down-weighting reduces strength of prior information (i.e., reduce number of borrowed events).
The fewer borrowed events, the more vague the prior.
Adapted from a Presentation by Adele Morganti (Actelion)

21. Example 2: Graph of Pediatric Priors
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

22. Example 2: Graph of Pediatric Priors
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

23. Example 2: Graph of Pediatric Priors
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

24. Example 2: Graph of Pediatric Priors
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

25. Example 2: Pediatric Posterior Distributions
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

26. Example 2: Pediatric Posterior Distributions
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

27. Example 2: Pediatric Posterior Distributions
Adapted from a Presentation by Jingjing Ye and James Travis, Office of Biostatistics (FDA)

28. Example 3: Weighting Prior Data (“Type 1 Error”)
Pediatric Sample N
1.5 N
2 N

29. Example 3: Weighting Prior Data Power
Pediatric Sample N
1.5 N
2 N

30. Example 3: Choice of Level of Weighting Prior Data
Pediatric Sample N
1.5 N
2 N N
1.5 N
2 N

31. Example 3: Choice of Level of Weighting Prior Data

32. Example 3: Choice of Level of Weighting Prior Data

33. Example 3: Choice of Level of Weighting Prior Data

34. Example 3: Choice of Level of Weighting Prior Data

35. (This is a clinical, not statistical, problem!)
Drugs Receiving New or Expanded Indication Extrapolation is a powerful tool to be used carefully!
If we are wrong about extrapolation, drugs will be labeled as effective that may be, in fact, ineffective.
(This is a clinical, not statistical, problem!)
† Adequate, well-controlled, efficacy and safety trial(s) (powered for efficacy), plus PK data.
‡ Single, controlled or uncontrolled, efficacy and safety trial (qualitative data) plus PK data; or single exposure-response trial (not powered for efficacy) plus PK and safety data, PK/PD and uncontrolled efficacy plus safety data, or PK/PD plus safety data.
Adapted from Table 1: Dunne J et al. Pediatrics 2011;128;e1242

36. Concluding Remarks
Conceptually, Bayesian statistical inference is a better fit with using extrapolation in pediatric drug development.
Whether (and how much) prior (adult and/or pediatric) data can be used to support pediatric efficacy is a clinical (not statistical) judgment.
Children must not be exposed to unnecessary or overly burdensome clinical trials by failing to design adult trials (e.g., evaluating exposure-response, incorporating suitable endpoints) to support pediatric extrapolation.

37. Thank you

38. Statistical Model Data Presented on Slide 20
According to Schoenfeld (1981), log hazard ratio (HR) is normally distributed with:
mean (μ) = log(0.60)= -0.51
SE (σ) = = 0.12
Down-weighting using power prior with parameter α
Normal prior for the log-hazard ratio is:  
log HR ~N −0.51, α
Variance of the prior is inversely related to the number of events borrowed (i.e., fewer borrowed events, the more vague the prior)
Under normal distribution for HR, Bayesian operating characteristics can be investigated using closed forms for posterior probability calculations.

39. Bayesian Inference Calculating Posterior Distribution of Parameter of Interest (θ)
Definitions:
p(θ) = prior distribution of parameter θ
p(data|θ) = likelihood, i.e., distribution of observed data given θ
p(θ|data) = posterior distribution, i.e., distribution of θ given the data
Example: Normal data with known σ
Ȳ = estimation of mean (µ) of θ given data
p(Ȳ|µ) = likelihood given µ
N(µ, σ2/n) = normal distribution with mean µ and variance σ2/n
p(μ) = prior distribution of mean μ
p(µ|Ȳ) = posterior distribution of μ given the data
Example: Normal data with known σ
Likelihood
p(Ȳ|µ) = N(µ, σ2/n)
Prior
p(µ) = N(µ0, σ2/n0)
Posterior ∝ Likelihood × Prior
p(µ|Ȳ) = N((n0µ0+nȲ)/(n0+n), σ2/(n0+n))
Probability of success (hypothesis is true): posterior probability (θ > x)
As if n0 additional patients with average response µ0 had been included.