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A Bayesian Approach to the ICH Q8 Definition of Design Space. 2008 Graybill Conference, Fort Collins, Co. June 11 th -13 th , 2008. John J. Peterson Senior Director, Research Statistics Unit GlaxoSmithKline Pharmaceuticals john.peterson@gsk.com. Graphically accessible.

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slide1

A Bayesian Approach to the ICH Q8 Definition of Design Space

2008 Graybill Conference, Fort Collins, Co.

June 11th-13th, 2008

John J. Peterson

Senior Director, Research Statistics Unit

GlaxoSmithKline Pharmaceuticals

john.peterson@gsk.com

Graphically accessible

Historical information (hard or soft) can be used

slide2

ICH Q8 Definition of Design Space

The ICH Q8 FDA Guidance for Industry defines "Design Space" as:

"The multidimensional combination and interaction of input variables

(e.g. material attributes) and process parameters that have been

demonstrated to provide assurance of quality.“

Further more….

“Working within the Design Space is not considered as a change. Movementout of the Design Space is considered to be a change and would normallyinitiate a post regulatory approval change process. Design Space is proposed by the applicant and is subject to regulatory assessment and approval”.

slide3

ICH Q8 Definition of Design Space

The ICH Q8 FDA Guidance for Industry defines "Design Space" as:

"The multidimensionalcombination andinteraction of input variables

(e.g. material attributes) and process parameters that have been

demonstrated to provide assurance of quality.“

slide4

ICH Q8 Definition of Design Space

  • The ICH Q8 FDA Guidance for Industry defines "Design Space" as:
  • "The multidimensional combination and interaction of input variables
  • (e.g. material attributes) and process parameters that have been
  • demonstrated to provide assurance of quality.“
  • Three key concepts:
  • 1. Measurement For example: controllable factors, input material attributes,in-process measurements, quality response measurements.
slide5

ICH Q8 Definition of Design Space

  • The ICH Q8 FDA Guidance for Industry defines "Design Space" as:
  • "The multidimensional combination and interaction of input variables
  • (e.g. material attributes) and process parameters that have been
  • demonstrated to provide assurance of quality.“
  • Three key concepts:
  • 1. Measurement For example: controllable factors, input material attributes,in-process measurements, quality response measurements.
  • 2. Prediction- Models to relate the measurements to the relevant quality responses. These need to be compared to specifications for quality.
  • - Need to be able to predict means AND variances of responses.
slide6

ICH Q8 Definition of Design Space

  • The ICH Q8 FDA Guidance for Industry defines "Design Space" as:
  • "The multidimensional combination and interaction of input variables
  • (e.g. material attributes) and process parameters that have been
  • demonstrated to provide assurance of quality.“
  • Three key concepts:
  • 1. Measurement For example: controllable factors, input material attributes,in-process measurements, quality response measurements.
  • 2. Prediction- Models to relate the predictive measurements to the quality responses. These need to be compared to specifications for quality.
  • - Need to be able to predict means AND variances of quality responses.
  • 3. ReliabilityTo quantify “How much assurance?”
  • The QbD-oriented guidance (PAT, ICH Q8, Q9, Q10, etc) is inundated with the words “risk” and “risk-based”.)
  • See presentation by H. Gregg Claycamp (CDER), “Room for Probability in ICH Q9”
slide7

Measurements

Input material measurements (W1, W2, …)

A Generic Process (or Unit Operation)

In-process measurements

(Z1, Z2, …) heat transfer, NIR

Control Factors/Parameters (F1, F2, …)

responses(Y1, Y2,…)

  • Responses have specification limits which define Quality: { AiL < Yi < AiU }
  • Vector of predictive variables, x = (f,w,z)
slide8

Prediction Models

The Standard Multivariate Regression Model

The Seemingly Unrelated

Regressions model

Other models?

e.g. Nonlinear, PLS,Wavelets, etc.

x = (x1,…xk)vector of predictive variables

r = no. of response types.

Fitted models give us predicted responses, i.e. but we need toknow the variances of the also to assess risk.

slide9

Reliability Model

How much assurance do we have of meeting specifications?

Consider

If we knew we could define a Design Space as

for some reliability level R.

From a Bayesian perspective one could consider the posteriorexpectation:to obtain the Bayesian Design Space:

slide10

Aside….what is a posterior predictive distribution?

  • A posterior predictive distribution is used to compute
  • If is the pdf for Y , then g(y | x, data) is the posterior predictivepdf with
  • where is the posterior distribution of b and S.
  • So
slide11

In most situations, Markov Chain Monte Carlo techniques will be used to compute

MCMC

,……………....,

slide12

Design Space

From a Bayesian perspective one could consider the posteriorexpectation:

  • Computationally, is straightforward to compute using MCMC.
  • Experiments with multiple batches, split plots, missing data, noise variablesand even heavy-tailed residual distributions can be handled with MCMC.
  • In theory it is also possible to handle latent variable models that may be needed for “functional data” from in-process measurements (e.g. BayesianPLS, Wavelets, etc.)
  • The classical multiple response surface approaches found in Design Expert, JMP, Statistica, etc. fall short of providing a good reliability models!
slide13

ICH Q8 Annex:Design space can be determined from the common region of successful operating ranges for multiple CQA’s. The relations of two CQA’s, i.e., friability and dissolution, to two parameters are shown in Figures 2a and 2b. Figure 2c shows the overlap of these regions and the maximum ranges of the potential design space.

What do these contoursrepresent? Mean

response surfaces?

Taken from theICH Q8 Annex.

(August 2007)

This overlay plot does not quantify “How muchassurance?”!

slide14

Taken from PQLI* Design Space

by Lepore and Spavins (J. of Pharm. Innovation, 2008)

*PQLI = Pharmaceutical Quality Lifecycle Implementation

What do these contoursrepresent? Mean

response surfaces?

(The paper does not say.)

This overlay plot does not quantify “How muchassurance?”!

slide15

Overlapping Means vs. Bayesian Reliability Approach to Design Space:

An Example – due to Greg Stockdale, GSK.

  • Example: An intermediate stage of a multi-stage route of manufacture for anActive Pharmaceutical Ingredient (API).
  • Measurements:
  • Four controllable quality factors (x’s) were used in a designed experiment.
  • (x1=‘catalyst’, x2= ‘temperature’, x3=‘pressure’, x4=‘run time’.)
  • A (face centered) Central Composite Design (CCD) was employed.(It was a Full Factorial (30 runs), with no aliasing.)
  • Four quality-related response variables, Y ’s, were measured. (These were three side products and purity measure for the final API.) Y1= ‘Starting material Isomer’, Y2=‘Product Isomer’, Y3=‘Impurity #1 Level’, Y4=‘Overall Purity measure’
  • Quality Specification limits: Y1<=0.15%, Y2<=2%, Y3<=3.5%, Y4>=95%.Multidimensional Acceptance region,
slide16

Overlapping Means vs. Bayesian Reliability Approach to Design Space:

An Example – due to Greg Stockdale, GSK.

Prediction Models:

Temperature = x1 Pressure = x2 Catalyst Amount = x3 Reaction time = x4

slide17

An Overlapping means approach to Design Space for an Active Pharmaceutical Ingredient (API)

The Design Space is the “Sweet Spot” Highlighted in Yellow below

The so-called “sweet spot”

highlighted in yellow

slide18

Why is the “sweet spot” not so sweet?

  • If the mean of Y at a point x is less than an upper bound, u, then all that guarantees is that

(For there is no guarantee that

  • Suppose . If Y1 and Y2 were independent,then all that is guaranteed is thatFor k independent Yi’s the situation becomes:
  • If Y1 and Y2 are positively correlated then it may be easier to find x-points to make large. Likewise, if Y1 and Y2are negatively correlated (for each x) then it may be more difficult.Note: Corr(Y3, Y4 | x) is about -0.8 for the API experiment.
slide19

Why is a multivariate reliability approach needed?

(Accounting for correlation among the responses…a simple example)

  • Suppose we have a process with four key responses, Y1, Y2, Y3, Y4
  • For simplicity,let’s assume that
  • Let
  • Consider If S = I, then
  • But if then
  • and if
slide20

Overlapping Means vs. Bayesian Reliability Approach to Design Space:

An Example

  • Overlapping Mean Response Surface Approach –
  • Can be computed using SAS/JMP, Design Expert, Minitab, etc.
  • The “sweet spot” region is determined by the overlapping mean responsesurfaces that are all simultaneously within their specification limits.
  • However the overlapping mean response approach: (i) Does not take into account the model parameter uncertainty (ii) Does not provide a measure of assurance to say “How likely it is that future responses will meet their specifications.” (iii) Does not take into account the correlation structure of the multivariate distribution of future responses.
  • Thus the overlapping means approach does not address the questionbegged by the ICH Q8 definition of Design Space…namely, “How muchassurance do we have of meeting process quality specifications?”
slide21

Overlapping Means vs. Bayesian Reliability Approach to Design Space:

An Example

  • A Bayesian Reliability Response Surface Approach –
  • A posterior predictive approach: (i) Takes into account the model parameter uncertainty (ii) Provides a measure of assurance to say “How likely it is that future responses will meet their specifications.” (iii) Takes into account the correlation structure of the multivariate distribution of future responses.
  • Thus the Posterior predictive approach addresses the questionbegged by the ICH Q8 definition of Design Space…namely, “How muchassurance do we have of meeting process quality specifications?”
  • Both graphical and tabular approaches can be used with the posteriorpredictive approach to better understand the resulting Design Space.
slide22

A Bayesian Reliability Approach to Design Space:

API Example

  • A posterior Predictive Response Surface Approach –
  • How likely is it that a future multivariate response will meet specifications for a factor configuration in the sweet spot?
  • Consider the posterior predictive probability p(x)=Pr (Y is in A | x, data).
  • Here, Y is assumed to have a multivariate normal distribution. A is the multidimensional acceptance region. The standard noninformative prior for b and S is used, , where r = 4, the number of response types.
  • Pr (Y is in A | x, data) is computed using Gibbs Sampling, one of the Markov Chain Monte Carlo (MCMC) simulation methods.
  • The largest probability of meeting specifications is only about 0.75.
  • - This is corresponds to the best p(x) value within the yellow “sweet spot” of overlapping mean response surfaces. The worst p(x) value in the “sweet spot” is only 0.23 !
slide23

Design Space Table of Computed Reliabilities1for the API (sorted by joint probability2)

Note that the largest probability of meeting specifications is only about 0.75

Optimal Reaction Conditions

[1] This is only a small portion of the Monte Carlo output.

[2] values were computed using SAS IML

Marginal Probabilities

slide24

Overlapping Mean Contours from analysis of each response individually.

This x-point (in the yellow sweet spot)

has only a probability of 0.75 .

But this x-point (in the yellow sweet spot)

has a probability of only 0.23 !

Posterior Predicted Reliability with

Temp=20 to 70, Catalyst=2 to 12, Pressure=60, Rxntime=3.0

Rxntime

Pressure

70

0.7

0.6

60

= Design Space

0.5

Contour plot

of p(x) equal to

Prob (Y is in A

given x & data).

The region inside the

red ellipse is the

design space.

50

0.4

x2=

Temp

0.3

40

0.2

30

0.1

0.0

20

2

4

6

8

10

12

x1=

Catalyst

slide26

A Question for the Audience

Question…. “How large should R be to calibrate the Design Space:

Note: “Based upon some historical precedents (e.g. three out of three successful manufacturing validation runs), some deductions about a value of R can be made”

“Three consecutive successful batches has become the

de facto industry practice, although this number is

not specified in the FDA guidance documents.”

Schneider, Huhn, and Cini, (2006), PAT Insider Magazine, April issue.

slide27

“How large should R be to calibrate the Design Space?”

  • Suppose that Z ~ Bernoulli(p) and that p has a beta prior, Beta(a,b).
  • Consider the likelihood based on 3Bernoulli trials,
  • Consider
  • Let Then p has a beta posterior distribution Beta(s+a, 3-s+b).
  • The posterior predictive distribution of new Z is beta-binomial and
  • For a uniform prior on [0,1], i.e. Beta(1,1), and s=3 (out of 3 trials) we get:
  • So is R=0.8 a reasonable value with which to calibrate a Design Space?
slide28

“How large should R be to calibrate the Design Space?”

  • IfR=0.8 is not large enough (e.g. we want R=0.95, say), but some manufacturing processes have been approved, based upon 3 out of 3 successfully manufactured batches, what does this mean?
  • Clearly, some prior information must have been utilized.
  • Consider a beta prior Beta(a,b) with a=16, b=1. Then for 3 out of 3 successes:
  • What does a beta prior distribution with a=16, b=1 look like?

The 5th percentile is 0.83

slide29

“How large should R be to calibrate the Design Space?”

Based on historical precedent….

  • This deduction implies that either: R=0.8 is an acceptable de facto lower bound for calculating a Design Space.
  • OR
  • Strong prior information should be allowed in the
  • calibration of a Design Space.
slide30

The Flexibility Offered by a Bayesian Approach to Design Space

  • A “pre-posterior analysis” can be performed to identify where additional information may be needed to improve design space calibration (by reducing model parameter uncertainty).
  • Noise variables are easily incorporated so that “robust parameter design” optimization can be done. (This is important for multistage processes.)
  • Can accommodate small amounts of missing data in a straightforward fashion.
  • The Bayesian approach can handle mixed-effect models in a straightforward manner. This has useful applications for split-plot designsand (random) batch effects.
  • The Bayesian approach can also be adapted to nonlinear mechanistic models.
slide31

Challenges to Constructing the ICH Q8 Design Space

Summary

  • The challenges….my opinion….
  • Getting clients to recognize the key elements of MPR: Measurements, Prediction model, Reliability Model,
  • particularly the importance of a reliability model to quantify “How much assurance?”
  • Computational issues. These can be solved with sufficienteffort. The Bayesian approach provides a unifying paradigm.
slide32

From the ICH Q8 Annex: (my highlights in red)

  • An enhanced quality by design approach to product development would additionally include the following elements:
  • • A systematic evaluation, understanding and refining of the formulation and
  • manufacturing process, including:
  • Identifying, through e.g., prior knowledge, experimentation, and risk
  • assessment, the material attributes and process parameters that can
  • have an effect on product CQAs;
  • Determining the functional relationships that link material attributes
  • and process parameters to product CQAs.
  • • Using the enhanced process understanding in combination with quality risk
  • management to establish an appropriate control strategy which can, for
  • example, include a proposal for design space(s) and/or real-time release.

The Bayesian approach can address the concerns of ICH Q8 in a coherent, unifying manner.

slide33

Acknowledgements

  • Gregory Stockdale
  • Aili Cheng
  • Tim Schofield
  • Paul McAllister
  • Michael Denham
  • Gillian Amphlett
  • Mohammad Yahyah
  • Kevin Lief
  • Val Fedorov
  • Darryl Downing
slide34

References

Claycamp, H. G. (2008), “Room for Probability in ICH Q9: Quality Risk Management“, presented at the Pharmaceutical Statistics 2008: Confronting Controversy conference., March 2008, Arlington, VA. (sponsored by the Institute of Validation Technology).

ICH Q8 (2006), “Guidance for Industry Q8 Pharmaceutical Development”.

ICH Q8 (2007), “Pharmaceutical Development Annex to Q8”

Miró-Quesada, G., del Castillo, E., and Peterson, J. J. (2004), “A Bayesian Approach to for Multiple Response Surface Optimization with Noise Variables”, Journal of Applied Statistics, 31, 251-270.

Peterson, J. J. (2004), “A Posterior Approach to Multiple Response Surface Optimization, Journal of Quality Technology,

Peterson, J. J. (2007) “A Bayesian Approach to the ICH Q8 Definition of Design Space”. Proceedings ofThe American Statistical Association, Biopharmaceutical Section. (Also to appear in the Journal of Biopharmaceutical Statistics in fall 2008.)

Peterson, J. J. (2008). “A Bayesian Reliability Approach to Multiple Response Surface Optimization with Seemingly Unrelated Regressions Models”, Quality Technology and Quantitative Management, (to appear).

Stockdale, G. and Chen, A. (2008), “Finding Design Space and Reliable Operating Region using a Multivariate Bayesian Approach with Experimental Design”, Quality Technology and Quantitative Management, (to appear).

If you are interested in a copy of the slides send e-mail to: john.peterson@gsk.com