1. Bayesian Applications to Quality-by-Design and Assay Development
Short Course on
Bayesian Applications to Quality-by-Design and Assay Development
John Peterson, Ph.D.
Director, Statistical Sciences Group
GlaxoSmithKline Pharmaceuticals
Collegeville, Pennsylvania, USA
Non-Clinical Statistics Conference, Brugge, Belgium, 8 October, 2014

2. OUTLINE What is QbD? Some Basic Concepts
Some Bayesian preliminaries
What is QbD? Some Basic Concepts
The Flaw of Averages vs. Predictive Distributions
Process Optimization using Bayesian Predictive Distributions
A Bayesian Approach to ICH Q8 Design Space & Scale-up
Bayesian Monte Carlo Studies for USP Test Assessment
Assay Development & Dissolution
Some Computational Recommendations
Acknowledgements
References

3. The Bayesian Paradigm = x prior distribution of
unknown model parameters
Posterior distribution of
unknown model parameters
Bayes’ rule
weighting function = x 3 3 3

4. The Bayesian Paradigm = x prior distribution of
unknown model parameters
Posterior distribution of
unknown model parameters
Bayes’ rule
weighting function = x 4 4 4 4

5. The Bayesian Paradigm = x
prior distribution of
unknown model parameters
Posterior distribution of
unknown model parameters
Bayes’ rule
weighting function = x
A simulation procedure known as “Markov Chain Monte Carlo” (MCMC)
can be used to sample from the posterior distribution of unknown model parameters given experimental data and prior information.
Other computational procedures can sometimes be used such as : numerical integration, generalized direct sampling, Laplace approximation approaches (e.g. INLA). 5 5 5 5 5

6. Three Practical Benefits of Bayesian Statistics
Provides a scientific way to incorporate informative prior information Probability theory and risk assessment can be used to build informative priors.
Priors can be used to provide a certain degree of “regularization” Priors can be used to induce necessary boundaries (e.g. positive variance components) Priors can be used to stabilize parameter estimates (e.g. ridge regression) Priors on model forms can be used to obtain “model averaging”, which improves predictions. (This has been successfully applied in data mining competition.)
Bayesian methodology provides a direct method for getting a predictive distribution For complex models (often needed for pharmaceutical process modeling) the frequentist approach may prove difficult here (e.g. nonlinear mixed-effect model).

7. Prior Distributions
Prior distributions can be used to incorporate prior information about a process (in a scientific way ….via Bayes Rule) This can be done by using a distribution that maps out the relative possibilities of parameter values.
- Or, a prior can simply designate parameter boundaries (e.g. a gamma distribution prior for a parameter that must be between positive.) q
shape=0.1
rate=0.1 s

8. Prior Distributions
Prior distributions can be weak or non-informative e.g. using a flat prior for the mean in a normal-theory model.
Sometimes priors are used to induce a certain degree of “regularization”.
- For example we may choose a multivariate normal prior for a vector of regression coefficients (intercept omitted) that has a mean vector of zero.
- This will tend to “pull” the posterior a bit towards the zero vector and thereby prevent regression coefficient estimates from becoming too large in situations of multi-collinearity. (Ridge regression can be thought of a Bayesian procedure with respect to a zero-centered prior
for the regression coefficients.) 8

9. Prior Distributions
In some cases, care must be used in choosing priors:
(i) For small sample sizes or weak information about a model parameter.
(ii) For informative priors
(iii) Even for non-informative priors in certain situations For details see: Seaman, J. W. III, et al. (2012). “Hidden Dangers of Specifying Noninformative Priors”, The American Statistician, 66(2),
Seaman et al. recommend that if one is interested a particular function of the model parameters, then one should use the chosen prior to observe the
induced prior for that function (e.g. by Monte Carlo simulation). 9 9

10. Posterior Predictive Distributions
Predictive distributions is why I became a Bayesian statistician.
Process optimization and QbD is why I desire predictive distributions.
Why?

11. Posterior Predictive Distributions
Predictive distributions is why I became a Bayesian statistician.
Process optimization and QbD is why I desire predictive distributions.
Why?
Because predictive distributions are a very good way to quantify process capability! 11

12. Posterior Predictive Distributions
Predictive distributions is why I became a Bayesian statistician.
Process optimization and QbD is why I desire predictive distributions.
Why?
Because predictive distributions are a very good way to quantify process capability!
A process can be optimized, but that does not imply that it is “capable”, i.e. likely to meet specifications. 12 12

13. Posterior Predictive Distributions
Posterior distribution
for model parameters
standard
normal
distribution
Posterior predictive
distribution 13 13 13

14. Posterior Predictive Distributions
Posterior distribution
for model parameters
standard
normal
distribution
Posterior predictive
distribution
Sampling from the posterior predictive distribution, we can then compute
as a measure of process capability. (Here, S is the specification interval.) 14 14 14 14

15. What is QbD?
Quality-by-Design (QbD): Quality by Design (QbD) is a concept put forth by Joseph Juran. (See Juran, J. M. (1992) Juran on Quality by Design)
Juran believed that quality could be designed into products and processes. He called this idea “Quality by Design”
QbD has been applied in many industries, most notably the automotive industry.
Recently, the US Food and Drug Administration has adopted QbD principles for drug manufacture.
The FDA QbD imperative is outlined in its report “Pharmaceutical Quality for the 21st Century: A Risk-Based Approach.”
Quote from Juran on Quality by Design: “Organizations that have adopted such methods of quantification (process capability) have significantly outperformed those which have not.”

16. The growing importance of process reliability and risk assessment
for pharmaceutical manufacturing
The FDA continues to push its initiative on
“Pharmaceutical Quality for the 21st Century”
The ICH Quality (Q) Guidances are supported by the FDA They outline (at a high level) what the pharmaceutical companies should do to improve the quality of the manufacturing of their products.
Such improved quality strategies are referred to as “Quality by Design” or QbD.
Currently, QbD is “optional” for Rx sponsors, but there appears to be increasing pressure to incorporate QbD as a strategy in Rx submissions.
Risk is an important concept for the ICH Q8-Q10 Guidances ICH Q8 - The word ‘risk’ or ‘risk-based’ occurs 35 times.
ICH Q9 - The word ‘risk’ or ‘risk-based’ occurs 290 times.
ICH Q10 - The word ‘risk’ or ‘risk-based’ occurs 34 times. 16

17. Some Basic Quality Concepts
Quality concepts for a single quality measure:
Process capability – is a measure of the inherent variability of a process measure about its target.
Quality improvement – reduction in variation of a process measure about its target.
Quality improvement can therefore be thought of as an improvement in process capability.

18. Some Basic Quality Concepts
Quality concepts for a single quality measure:
Process capability – is a measure of the inherent variability of a process
measure about its target.
Quality improvement – reduction in variation of a process measure about its target.
Quality improvement can therefore be thought of as an improvement in process capability.
But what about products and processes involving multiple quality criteria?
What do we do with multiple variance (and covariance) measures? Add them up? Find some function of a variance-covariance matrix? 18

19. Some Basic Quality Concepts
Quality concepts for a multiple quality measures:
Process capability – is the inherent (multivariate) distribution of the process measures about a (vector) target.
Quality improvement – shrinking of the (multivariate) distribution of the process measures about a (vector) target
Quality improvement can therefore be thought of as an improvement in process capability. 19

20. Some Basic Quality Concepts
Quality concepts for a multiple quality measures:
Process capability – is the inherent (multivariate) distribution of the process measures about a (vector) target.
Quality improvement – shrinking of the (multivariate) distribution of the process measures about a (vector) target
Quality improvement can therefore be thought of as an improvement in process capability.
Generalizing from variances to distributions helps us to think more clearly about dealing with single or multiple quality endpoints. 20 20

21. Some Basic Quality Concepts
A natural process capability index for both univariate and multivariate
quality measures is the proportion of the distribution of quality measures that are within the quality specification limits.
This process capability index can be used as a desirability function for process optimization.
Mathematically, we can express this as: where Y is a vector of quality measures, S is a specification region, and x is a vector of process conditions.
p(x) is a function of all of the means and variance components associated with the process.
The expression for p(x) involves the posterior predictive distribution for Y.

22. Some confusion about process capability
Process capability indices have been criticized as being incapable of capturing all aspects of a process’s capability of meeting specifications because they are a single number.
It has been argued that “process capability” should be assessed using various aspects of the distribution of a process’s responses, instead of using a single metric (Pignatiello and Ramberg, 1993)
I suspect that this confusion arises from not recognizing the difference between “exploratory analysis” and “confirmatory analysis”.
I believe that one should explore the nature of a process’s response distribution, but in the end one must choose some optimization criterion.
If we are to optimize a process, we should optimize it to be as capable as possible. Hence, the use of a process capability index makes sense for process optimization (Plante (2001), Jeang (2010) ). But care is required. 22

23. Some Points about Process Capability Assessment
Process Capability Assessment is important. - Just because your process mean is on target does not necessarily imply that your process will meet specifications a high proportion of the time.
Process Capability Assessment is statistically more difficult than other quality improvement methods, particularly for multiple responses.
(This is why statisticians are important here!)
It is important to have the process in control as much as possible for process capability assessment.
We need to assess process capability in both exploratory and confirmatory
ways.
Exploratory:
- Explore the process means relative to specifications.
- Identify and measure process variance components and correlations.
- Assess response distribution at various operating conditions.
Confirmatory:
- Compute process capability index (after process is in control) .
- Validate process capability dynamically during SPC phase. 23 23

24. Some Basic Quality Concepts
Common Cause vs. Special Cause Variation
Common Cause Variation: - Common cause variation is the process variation due to “common causes” Common causes are associated with the usual historically known, quantifiable variation of a process.
- Typically, it is variation that can be modelled.
Special Cause Variation: - Special cause variation is the process variation due to “special causes” (sometimes referred to as “assignable causes”) Special causes are typically come in the form of surprises or are outside the historical experience base.
- Some examples of special causes may be: operator error, machine malfunction, a surprisingly poor batch of raw material, or a change in a process condition that was not previously recognized as influential.

25. This process capability index as a desirability function
We assume that Y can be modeled as a (stochastic) function of process factors, x.
For example:
Note that
We can optimize a process (under the above model) by maximizing p(x) with respect to the factors in x.
p(x) forms a desirability function for process optimization that is easy to interpret and avoids flaws associated with mean response surface optimization.

26. 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

27. The Flaw of Averages vs. Predictive Distributions
Average depth of river is 3 feet.
The Flaw of Averages:
Why We Underestimate Risk in the Face of Uncertainty
by Dr. Sam Savage 27

28. The Flaw of Averages vs. Predictive Distributions
Inferences about population means are rarely sufficient to quantify good quality.
Recall:
Quality improvement – reduction in variation of a process measure about its target.
Or more generally…. Quality improvement – shrinking of the (multivariate) distribution of the process measures about a (vector) target 28 28

29. The Flaw of Averages vs. Predictive Distributions
All classical textbooks on response surface methodology state a response surface model for a process in the basic form:
where:
x is a vector of process factors (e.g. temperature, pressure, etc.)
and e is a “statistical error” or “random error” term, where E(e)=0.
In these textbooks it is stated that is a “mechanistic model” with a vector of unknown model parameters b ).
In cases where the form of is unknown, it is recommended that one approximate by a (second-order) Taylor series. 29

30. What is the truth?: mean models vs. distribution models
Given that the basic response surface model is represented in classical textbooks as
it is not too surprising that practitioners think (in the back of their minds) that a response surface model is:
observed process response = truth + measured error.
or possibly
observed process response = approx. truth + measured error.
Since I have seen some authors refer to the “true mean model” with regard to process optimization. 30

31. NO! Consider the following thought experiment…
Suppose you want to optimize a complex manufacturing process.
Suppose further that you are able to acquire instruments to measure the process response that are extremely accurate (producing truly negligible measurement error).
If you were to keep the process factors constant, but stop and started the process several times using different batches of raw materials each time,
would you expect to measure the exact same process response values?
NO! 31

32. Consider the following real experiment…
where yt is the measured amount of substrate remaining after time t.
The parameters in red are unknown model parameters and the factors in blue are process conditions of temperature, amount of catalyst, etc.
We want to be able to run the experiment long enough to remove most all of the substrate (less than 0.1% say) 32

33. Process modeling: Scientific modeling is the backbone… but is there more?
Chemistry equation: 33

34. Same chemistry but different profiles!
Process modeling: Scientific modeling is the backbone… but is there more?
Chemistry equation:
Three replicate batches run under the same experimental conditions:
Same chemistry but different
profiles! 34

35. … Traditional mechanistic model equation for prediction
Measurement
random error
Stochastic-mechanistic model equation that models batch-to-batch variation
Predictive
distribution for
risk assessment
Batch-to-batch
random effects
Possible drivers:
Reactor is charged with
slightly different amounts
of raw materials.
Proportion of catalyst is slightly different.
Changes in environmental
conditions.
Subtle operator differences … 35 35

36. Why is variation important to quality improvement ?
Traditional mechanistic model equation for prediction
Measurement
random error
Stochastic-mechanistic model equation that models batch-to-batch variation
Predictive
distribution for
risk assessment
Batch-to-batch
random effects
Why is variation important
to quality improvement ?
Because quality improvement
can be defined as “reduction in variation about a target” 36 36 36 36

37. Predictive Distributions and Multiple Response Process Optimization37

38. Predictive Distributions and Multiple Response Process Optimization38

39. Predictive Distributions and Multiple Response Process Optimization
Variation due to
Variation due to
common -
cause
estimating unknown
error variability
model parameters.
Multivariate predictive
distribution of quality
responses
Process
A very reliable process
control
values
Probability of meeting both
Y = f ( x , z , e | q )
specifications is well above !
Multivariate h 3%
predictive
model
60%
y2=friability
mean
85%
This quality response
distribution is from
99%
a much better x -
point
Variation due to noisy
in the process.
control variables, input 0%
raw materials, etc.
80%
y1=Percent dissolved
100%
(at 30 min.) 39

40. Multiple response surface optimization:
Beware the “sweet spot” for ICH Q8 Design Space
The classical textbooks in response surface methodology (RSM)
refer to “overlapping mean” response surfaces as a way to optimize processes with multiple response types.
Myers, Montgomery, Anderson-Cook
Box, Hunter & Hunter
Box & Draper
Popular point & click packages such as Design Expert and SAS/JMP
will produce such a “sweet spot” graph with a few clicks of your mouse.

41. ICH Q8 Regulatory Guidance: The “Design Space” Concept
ICH (International Congress on Harmonization) is an international body of representatives from the US, Europe, and Japan for the purposes of harmonization
of technical requirements for registration of pharmaceuticals for human use.
The ICH has a set of quality guidelines, all with the prefix Q. ICH Q8 addresses “pharmaceutical development” (i.e. development of the actual pill, liquid,
injectible drug product)
ICH Q8 contains a key concept called “design space”. (This is not a statistical term, but refers to the set of all manufacturing recipes that should produce acceptable product quality results.)
ICH Q8 defines the “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. 41

42. & ICH Q8 Regulatory Guidance: The “Design Space” Concept
ICH Q8 defines the “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.
A “design space” example from the ICH Q8 regulatory guidance document:
&
Contour plot of “dissolution”
Contour plot of “friability”
Design space (white region) 42 42

43. ICH Q8 Regulatory Guidance: The “Design Space” Concept
ICH Q8 defines the “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.
A “design space” example from the ICH Q8 regulatory guidance document:
Process distribution
Process distribution
Process distributio
Process distribution
Friability spec. limits
Specification Region
Dissolution Spec. Limits 43 43 43

44. Overlapping Mean Design Space Situation with Three Response Types
Three-dimensional distribution of
responses at a point on the boundary of an overlapping means design space.
Red points are “out of spec”. y3
85% Out of Spec
For this process, the overlapping means Design Space from the Design Expert package
harbored process configurations with a low
probability of meeting all three quality specifications.
The worst probability was about 15%. y1 y2
y1 = tablet disintegration time,
y2 = friability, and y3 = hardness
From Peterson, J. and Lief, K. (2010) “The ICH Q8 Definition of Design Space: A Comparison of the
Overlapping Means and the Bayesian Predictive Approaches”, Statistics in Biopharmaceutical Research, 2, 44

45. Examples from Six Different (Real Data) Experiments
Results Corresponding to Overlapping Mean Design Spaces
Example
Number of factors
for the DS
Number of responses for the DS
Minimum posterior
probability of acceptance
Maximum posterior 1 4 3
0.15
0.84 2
0.23
0.72
0.34 3*
0.26
0.74 5
0.11
0.33 6
* Mixture experiment. Factors sum to 1.
From Peterson, J. and Lief, K. (2010) “The ICH Q8 Definition of Design Space: A Comparison of the
Overlapping Means and the Bayesian Predictive Approaches”, Statistics in Biopharmaceutical Research, 2,

46. A Distribution Approach to Process Optimization
Build your process model taking into account all key sources of variation (e.g. batch-to-batch, measurement error)
Design an experiment to gather data that will enable you to compute a posterior distribution for the unknown model parameters.
Using the posterior distribution, compute a posterior predictive distribution
for your process.
Compute the probability of meeting specifications (or some other utility measure) as a function of process factors. For example: Optimize
Optimize the process using the utility measure in 4. above.
(Here, Y is a vector of response types and x is a vector of process factors)

47. 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 an Active 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 + axial points + 6 center points [30 runs])
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,

48. Overlapping Means vs. Bayesian Reliability Approach to Design Space:
An Example – due to Greg Stockdale, GSK.
Model Terms
Response x1 x2 x3 x4
x11
x22
x33
x44
x12
x13
x14
x23
x24
x34
SM Isomer D
Prod Isomer
Impurity
Purity
Prediction Models:
Temperature = x1 Pressure = x2 Catalyst Amount = x3 Reaction time = x4

49. Optimal Reaction Conditions
Design Space Table of Computed Reliabilities1 for the API (sorted by joint probability)
Note that the largest probability of meeting specifications is only about 0.75
Temp
Pressure
Catalyst
Rxntime
Joint
Prob SM
Isomer
Prod
Impurity
Purity 35 60 6 3
0.752 1
0.9985
0.8435
0.79
32.5 7
0.743
0.9995
0.7875
0.8295
37.5
0.7375
0.7855
0.8255
6.5
0.737
0.9975
0.821
0.7845 30
7.5
0.7335
0.7775
0.8175
0.725
0.7485
0.845
0.7225
0.77
0.812
0.7195
0.9955
0.864
0.7415
0.717
0.999
0.8075
0.759
0.716
0.734
0.859
5.5
0.7145
0.993
0.8065
0.7565
0.712
0.731
0.8555
Optimal Reaction Conditions
[1] This is only a small portion of the Monte Carlo output.
Marginal Probabilities

50. Overlapping Mean Contours from analysis of each response individually.
This x-point (in the yellow sweet spot)
has only a probability of
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

51. How was this model fit? Model Terms Response D SM Isomer Prod Isomer
Prediction Models:
Model Terms
Response x1 x2 x3 x4
x11
x22
x33
x44
x12
x13
x14
x23
x24
x34
SM Isomer D
Prod Isomer
Impurity
Purity

52. SUR=Seemingly Unrelated Regressions
How was this model fit? … Using a SUR Model
SUR=Seemingly Unrelated Regressions
Prediction Models:
Prediction Models in Matrix Form: 52

53. Fitting the SUR Model Prediction Models in Matrix Form:
The frequentist fit: 53 53

54. Fitting the SUR Model Prediction Models in Matrix Form:
The frequentist fit:
These two forms
strongly suggest
Gibbs sampling for a Bayesian analysis! 54 54 54

55. A Bayesian Analysis for the SUR Model
Consider a non-informative prior approach:
Gibbs sampling:
Notes: See John Geweke’s book: Contemporary Bayesian Econometrics and Statistics
for details.
The rsurGibbs function in the bayesm R package uses Gibbs sampling for the SUR model.
A similar analysis can also be done with BUGS with weak or informative priors.

56. How do We Sample Posterior Predictive Values
from the SUR Model?

57. A Preposterior Analysis
Suppose that is not large enough.
The spread of the posterior predictive distribution depends upon both the process variation and the model parameter uncertainty.
If the sample size for the experiment was small, it may be that the model parameter uncertainty is playing a significant factor in the size of
By “simulating” additional data from the model, one can assess how much data is likely to be needed to substantially reduce the model parameter uncertainty and thereby increase
Experimental region

58. A Preposterior Analysis for the SUR Model
Gibbs sampling:
Increase the rows of X to
correspond to additional
experimental runs.
Increase N to correspond to the additional rows
of X.
Note: In some cases, parametric bootstrap simulations will be needed to generate the additional data. Simulating from the posterior predictive distribution may not work! For details see:
See Peterson, J. J., Miró-Quesada, G., and del Castillo, E. (2009), “A Bayesian Reliability Approach to Multiple Response Optimization with Seemingly Unrelated Regression Models”, Quality Technology and Quality Management, 6(4), 58

59. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Process: Lubricant Blending process for theophylline tablets.
Optimization factors: x1=Froude number, x2=blending time
Responses: Y1=“compression rate of powder mixture”
Y2=“tablet hardness (N)”
Y3=“dissolution percentage at 30 min.”
Specification limits: Y1<=0.32, Y2>=30, Y3>=75

60. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Small-scale design: 32 factorial.
Large-scale design: three replications at x1=0.36, x2= plus experimental runs at:
x1=0.20, x2=2
x1=0.40, x2=58

61. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Optimization factors: x1=Froude number, x2=blending time
Responses: Y1=“compression rate of powder mixture”
Y2=“tablet hardness (N)”
Y3=“dissolution percentage at 30 min.”
Small-scale Model: Seemingly Unrelated Regressions (SUR) model.

62. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Optimization factors: x1=Froude number, x2=blending time
Responses: Y1=“compression rate of powder mixture”
Y2=“tablet hardness (N)”
Y3=“dissolution percentage at 30 min.”
Small-scale Model: Seemingly Unrelated Regressions (SUR) model. 62

63. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Optimization factors: x1=Froude number, x2=blending time
Responses: Y1=“compression rate of powder mixture”
Y2=“tablet hardness (N)”
Y3=“dissolution percentage at 30 min.”
Large-scale Model: A SUR-like model
is a additive adjustment coefficient.
is a multiplicative adjustment coefficient.
j = 1,2,3 63

64. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Optimization factors: x1=Froude number, x2=blending time
Responses: Y1=“compression rate of powder mixture”
Y2=“tablet hardness (N)”
Y3=“dissolution percentage at 30 min.”
Large-scale Model: A Reduced SUR-like model
is a additive adjustment coefficient.
is a multiplicative adjustment coefficient.
j = 1,2,3 64 64

65. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
S is the specification region.
simulated from the posterior
N = 1,000 simulations
for some reliability level R. 65 65 65

66. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Data from the Small-scale & Large-scale Experiments:
X X Response Types
Froude no. Blending Time Y Y Y3
Small-scale data
Specification limits: Y1<= Y2>= Y3>=75
Large-scale data 66 66 66 66

67. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Data from the Small-scale & Large-scale Experiments:
X X Response Types
Froude no. Blending Time Y Y Y3
Small-scale data
Specification limits: Y1<= Y2>= Y3>=75
Large-scale data
Small-scale data
Large-scale data
Small-scale data
Large-scale data 67 67 67 67 67

68. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Model fits for small-scale experiments:
Multivariate Wilks-Shapiro normality test on SUR residuals: p=0.014
Scatter plot matrix of SUR residuals
Multivariate normal scores plot
No outliers detected.

69. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
Model fits for small-scale to large-scale analysis:
SS=“small-scale”
Specification limits: Y1<= Y2>= Y3>=75
Large-scale prediction
No outliers detected.
Large-scale Design Points
X X Y1 (Y1-pred.) Y2 (Y2-pred.) Y3 (Y3-pred.)
(0.285) (29.8) (76.8)
(0.335) (51.4) (84.2)
(0.217) (13.5) (66.6) 69

70. for the Maeda et al. (2012) Study
Design Space Scale-up
for the Maeda et al. (2012) Study
S is the specification region.
simulated from the posterior
N = 1,000 simulations
for some reliability level R. 70 70 70 70

71. Both small & large scale information used
0.92 probability
0.80 probability
= 3 points
= small-scale point
= large-scale point

72. The Posterior Predictive Approach Easily Solves the
Multivariate Robust Parameter Design Problem
Sometimes we have process factors that we can control in a lab setting but are noisy in a manufacturing plant.
If such noise factors interact with other completely controllable factors, we may be able to dampen the effects of the noise factors to improve the probability of meeting specifications.
Suppose where x1,…, xk are control factors and Xk+1,…, Xn are (random) noise factors. .
and use p(x1,…,xk) to optimize the process.
See Miró-Quesada, del Castillo, Peterson (2004), Journal of Applied Statistics for details. 72 72

73. Advantages of the Multivariate Bayesian Predictive Approach to Process Optimization and Design Space
It provides a quantifiable assessment of process capability for a single operating target or a region of process operating conditions.
It considers the uncertainty of all of the model parameters.
It models the correlations among the response types.
It is easy to add noise variables.
It allows for a “preposterior” analysis to see how much further data would reduce the model parameter uncertainty.
It can be easily adapted to special desirability or cost functions.
It has Bayesian “flexibility” Informative prior information can be used if desired. - Bayesian regularization can be used (e.g. to induce model stability or parameter constraints) - Predictive distributions for complex (e.g. nonlinear mixed effects) models can obtained. 73

74. Some Cautions on the Multivariate Bayesian Predictive Approach to Process Optimization and Design Space
Process capability surface is statistically more difficult to estimate than a mean response surface By its definition, process capability is sensitive to distributional assumptions Of course, this is not a reason to avoid inference for process capability.
In most all cases, process capability cannot be quantified to depend additionally on special cause variation For example, the “probability of meeting specification” in most cases cannot be easily modeled to take into account issues related to machine failure, unexpected contamination in raw materials, mistakes by workers, etc. - As such, the overall probability of being out of spec. will be somewhat larger than that based upon the common cause variation of the process. - Nonetheless, actions can be taken to lessen the extent of special cause variation (better machine maintenance, better raw material acquisition & screening, better staff training).

75. How Can We Deal with these Cautions?
Use good experimental design.
Use sensitivity analyses for regression models, not just priors. - If you can, fit more than one model form and compare the results.
Make sure clients and managers understand the difference between
common cause and special cause variation….particularly if the estimated process capability is large.
If the estimated process capability is too small, first consider a “preposterior” analysis, particularly for experiments with small sample sizes It may be that by simulating additional data, one can show that the process capability can be increased to acceptable levels by additional experiments.
- If additional data still does not increase process capability far enough,
further process variation reduction and/or mean improvement may be needed.

76. Some Future Challenges for Process Optimization
using a Predictive Distribution Approach
Processes with many response types and associated
specification limits.
- In biopharmaceutical manufacturing it is common to have about a dozen or more different response types, each with their own specification limits that need to be met.
- Often, too little experimental runs will be available with which to check distributional assumptions in a clear way.
- Current research underway involving use of a special desirability function to reduce the dimensionality of the problem.
Processes that have many latent variables.
- Modeling of raw material properties can help with building more realistic predictive models for pharmaceutical manufacturing.
- However, it is still “early days” for Bayesian predictive modeling using these types of models.

77. Some Future Challenges for Process Optimization
using a Predictive Distribution Approach
Functional responses
- Some manufacturing processes produces a functional trace or profile as a o measured response.
del Castillo, E., Colosimo, B. M., and Alshraided, H., (2012), “Bayesian Modeling and Optimization of
Functional Responses Affected by Noise Factors”, Journal of Quality Technology 44, 77

78. Summary
Classical response surface methodology (based on means) has had a profound
influence on process optimization…but more needs to be done!
The “more” involves thinking about processes as entities which produce distributions of results over time.
In other words, process optimization needs to be a distribution oriented
endeavor.
“Quality improvement” has been defined in a nutshell as “the reduction in variation about a target”. This can be generalized to “the shrinking of a multivariate distribution about a vector of quality target values”.
The Bayesian posterior predictive distribution can be an effective tool for quality improvement, particularly for processes that involve complex modeling. 78

79. Some Further Applications

80. Bayesian Monte Carlo Studies for USP Test Assessment
Many USP tests involve multi-stage algorithms Here it is not obvious what the probability of acceptance or rejection would be for processes with assumed means and variance components (within and between batch variances).

81. Bayesian Monte Carlo Studies for USP Test Assessment
Many USP tests involve multi-stage algorithms Here it is not obvious what the probability of acceptance or rejection would be for processes with assumed means and variance components (within and between batch variances).
Suppose we have b batches of data
- Let A(X) = 1 if the batch is accepted, 0 if rejected., where A is a USP data algorithm.
- Assumed model:
- Consider the reliability measure: Based upon data, X, from several batches we can compute a posterior distribution for to make process qualification decisions about a process For example we could compute the posterior probability
batch 1,……...…..…, batch b 81

82. Bayesian Monte Carlo Studies for USP Test Assessment
Using Monte Carlo simulations to generate data from given numbers of batches,
we can determine the number of batches (of a given size) needed to have a specified degree of certainty for process qualification This determination would make use of for simulated values of x.
A less conservative approach could involve the use of instead of Note:
Of course, prior information about the product quality endpoint (content uniformity, dissolution, etc.) can be used to possibly reduce the number of batches needed for process qualification.
For further details see:
LeBlond , D. and Mockus, L. (2014) “The Posterior Probability of Passing a Compendial
Standard, Part 1: Uniformity of Dosage Units”, Statistics in Biopharmaceutical Research, DOI: / 82

83. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
- The reference standard and test sample are similar if they contain the same effective constituent.
- Under such condition, the test sample behaves as a dilution (or concentration) of the reference standard.
Graphically, this line is a copy of shifted by a fixed amount on the log-concentration axis.
In other words, there exists a number  such that for all x.
This calibration constant  is commonly known as log-relative potency of the test sample. xL xU 83

84. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
If two lines are ”close” to parallel over a finite interval, then there should exist a shift that will bring the lines close together over that interval.
As such, the criterion below could be used to quantify assay parallelism in a practical way, over a finite interval. 84 84

85. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
Therefore, one can assess assay parallelism (for linear assay profiles) by computing
For sigmoidal assay profiles, one can assess parallelism by computing
For details see:
Novick, S. J., Yang, H., and Peterson, J. J., (2012) “A Bayesian Approach to Parallelism in Testing in Bioassay”. Statistics in Biopharmaceutical Research, 4(4) 85 85 85

86. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
- Note, for sigmoidal assay profiles, some people might prefer to assess differences after a logistic transformation has been done so that comparisons will be between linear forms. 86 86

87. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
- Note, for sigmoidal assay profiles, some people might prefer to assess differences after a logistic transformation has been done so that comparisons will be between linear forms.
But the transformed data depends upon the (unknown) parameters A and B ! 87 87 87

88. Assay Development This does not stop the Bayesian approach!
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
- Note, for sigmoidal assay profiles, some people might prefer to assess differences after a logistic transformation has been done so that comparisons will be between linear forms.
But the transformed data depends upon the (unknown) parameters A and B !
This does not stop the Bayesian approach! 88 88 88 88

89. Assay Development
Assessing Assay Parallelism over a Pre-specified Interval , [xL, xU]
- Note, for sigmoidal assay profiles, some people might prefer to assess differences after a logistic transformation has been done so that comparisons will be between linear forms.
Compute instead: 89 89 89 89 89

90. Assay Development Assay ruggedness
- Bayesian design space modeling ideas can be used here as well.
- Suppose one had several factors, say x1, x2,…, x8 , in a “screening design” over a small (robustness) factor region, X.
- Suppose also that one had one (or more) process response types:
- One could compute
Specification
region
For further details see:
Peterson, J. J. and Yahyah, M., (2009) "A Bayesian Design Space Approach to Robustness and System Suitability for Pharmaceutical Assays and Other Processes", Statistics in Biopharmaceutical Research 1(4), 90

91. Assay Validation
The “total error concept” in analytical method validation: A Bayesian perspective.
Suppose we have an assay response, X, and a true (gold standard) value, T.
Suppose further that
Note:
Consider
This is equivalent to
The “closeness” of a result X to the unknown true value of the sample, T,
is simultaneously linked to both the size of the bias and precision of the method.
For further details see: Boulanger, B. et al. (2007), “Risk management for analytical methods based on the total error concept: Conciliating the objectives of the pre-study and in-study validation phases”,
Chemometrics and Intelligent Laboratory Systems 86, 198–207. 91

92. Dissolution
Consider the dissolution data for a test and reference batch:
Reference
Reference
Test
Test
The f2 statistic is commonly used to make decisions about equivalence of reference and test batches.

93. Dissolution
The f2 statistic is commonly used to make decisions about equivalence of reference and test batches.
F2 has been proposed as a population-based analogue of f2. 93

94. Dissolution
16%
Guidance for Industry: SUPAC-MR: Modified Release Solid Oral Dosage Forms. Scale-Up and Postapproval Changes: Chemistry, Manufacturing and Controls; In Vitro Dissolution Testing and In Vivo Bioequivalence Documentation.
f2=52.3
“An f2 value between 50 and 100 suggests the two dissolution profiles are similar. Also, the
average difference at any dissolution sampling time point should not be greater than 15% between
the changed drug product and the biobatch or marketed batch (unchanged drug product)
dissolution profiles.”
So we might want to consider: 94 94

95. Dissolution However, frequentist inference related to
16%
However, frequentist inference related to
could be difficult to implement.
f2=52.3
But, if we have the posterior for the model parameters, the computation of
is straightforward (at least from a Monte Carlo approach). 95 95 95

96. Some Computational Recommendations
Generally, the most difficult part of a Bayesian analysis is computing the posterior distribution of model parameters.
Posterior predictive distributions and associated risk
probabilities are more straightforward to compute.
As a statistical consultant, it is advisable to have one (or more) backup strategies for statistical computing.
Sooner or later, WinBUGS or PROC MCMC will fail to produce adequate results! 

97. Some Computational Recommendations
“Access” to other Bayesian applications or specialty R
packages can be useful.
In addition to the software, “access” can mean Working knowledge of the software. or
- Access to someone who is knowledgeable and willing to help to help you get up to speed quickly.
But it can be useful to know, and often use, 2 or 3 different Bayesian applications.
- MCMC algorithms can be delicate and it may be prudent to check any important results using a different algorithm. 97

98. Some Computational Recommendations
Some alternative Bayesian applications to WinBUGS, jags, or PROC MCMC in SAS:
STAN Uses the ‘No-U-Turn Sampler’ algorithm Generally faster convergence for analysis situations where BUGS will have problems Can be invoked in R using the RStan package.
Generalized Direct Sampling (GDS) - Does not use MCMC, but rather a sophisticated rejection algorithm GDS produces independent samples from the posterior! - Requires finding the posterior mode There is a R package for this: bayesGDS 98

99. Some Computational Recommendations
Some alternative Bayesian applications to WinBUGS, jags, or PROC MCMC in SAS:
The R package, MCMCglmm glmm stands for “generalized linear mixed-effect models” It can also sample from the posterior for multivariate normal mixed-effect models.
- It can sample from the posterior for binary, multinomial, and Poisson models. 99 99

100. Acknowledgements Steven Novick Harry Yang Stan Altan David LeBlond
Yan Shen
Bruno Boulanger
Mohammad Yahyah
Kevin Lief

101. Some Useful Bayesian Books
Christensen, R., Johnson, W., Branscum, A., and Hanson, T. E. (2011). Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians, Chapman & Hall, CRC, Boca Raton, FL.
del Castillo, E. (2007), Process Optimization - A Statistical Approach, Springer, New York, NY. (Chapters 11 & 12 discuss Bayesian analysis for process optimization)
Krusche, J. K. (2010), Doing Bayesian Data Analysis: A Tutorial with R and BUGS, Academic Press, Oxford, UK.
 Lunn, D., Jackson, C., Best, N., Thomas, A., Spiegelhalter, D., (2013), The BUGS Book – A Practical Introduction to Bayesian Analysis, CRC Press, Boca Raton, FL.
Ntzoufras, I. (2009). Bayesian Modeling Using WinBUGS, John Wiley and Sons, Inc. Hoboken, NJ.
Gellman, A. and Hill, J, (2006), Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, NY, NY.

102. Concerned about becoming a Bayesian?
You might consider reading the recent book:
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code,
Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries
of Controversy by Sharon Bertsch McGrayne.

103. References
Bernardo, J. M. and Irony, T. Z. (1996), "A General Multivariate Bayesian Process Capability Index", The Statistician, 45,
del Castillo, E. (2007), Process Optimization - A Statistical Approach, Springer, New York, NY.
(This is a good book for Bayesian process optimization)
del Castillo, E., Colosimo, B. M., and Alshraided, H., (2012), “Bayesian Modeling and Optimization of Functional Responses Affected by Noise Factors”, Journal of Quality Technology 44,
Duncan, A. J. (1986). Quality Control and Industrial Statistics. Irwin, Homewood, IL.
Flaig, J. J. (1999), “Process Capability Sensitivity Analysis”, Quality Engineering, 11(4),
Fu, Z., Leighton, J., Cheng, A., Appelbaum, E., and Aon, J. C. (2012) “Optimization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach”, Biotechnology Progress (to appear).
Gelman, A. and Hill, J., (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, New York, NY. (This is a good book for Bayesian mixed effect modeling.)

104. References (continued)
Jeang, A. (2010), “Optimal Process Capability Analysis”, International Journal of Production Research, 48(4),
LeBlond , D. and Mockus, L. (2014) “The Posterior Probability of Passing a Compendial
Standard, Part 1: Uniformity of Dosage Units”, Statistics in Biopharmaceutical Research, DOI: /
Maeda, J., Suzuki, T., and Takayamab, K. (2012) “Novel Method for Constructing a Large-Scale Design Space in Lubrication Process by Using Bayesian Estimation Based on the Reliability of a Scale-Up Rule”, Chem. Pharm. Bull. 60(9) 1155–1163.
Miró-Quesada, G., del Castillo, E., and Peterson, J.J., (2004) "A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise variables", Journal of Applied Statistics, 31,
Montgomery, D. C. (2009), Introduction to Statistical Quality Control (6th ed), John Wiley & Sons,
Inc., Hoboken, NJ.
Ng, S. H. (2010), “A Bayesian Model-Averaging Approach for Multiple-Response Optimization”,
Journal of Quality Technology, 42,
Peterson, J. J., (2004) "A Posterior Predictive Approach to Multiple Response Surface Optimization", Journal of Quality Technology, 36,
104

105. References (continued)
Peterson, J. J. (2006), "A Review of Bayesian Reliability Approaches to Multiple Response
Surface Optimization", Chapter 12 of Bayesian Statistics for Process Monitoring, Control,
and Optimization , pp , (eds. Colosimo, B. M and del Castillo, E.) Chapman and Hall/CRCPress Inc.
Peterson, J. J. (2007), “A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization”, in Bayesian Process Monitorng , Control & Optimization, Chapman & Hall/CRC.
Peterson, J. J. (2008), “A Bayesian Approach to the ICH Q8 Definition of Design Space”, Journal of Biopharmaceutical Statistics, 18,
Peterson, J. J., Miró-Quesada, G., and del Castillo, E. (2009), “A Bayesian Reliability Approach to Multiple Response Optimization with Seemingly Unrelated Regression Models”, Quality Technology and Quality Management, 6(4),
Peterson, J. J. (2009) “What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution”,
Pharmaceutical Manufacturing, 8(10)
Peterson, J. J. and Lief, K. (2010) "The ICH Q8 Definition of Design Space: A Comparison of the Overlapping Means and the Bayesian Predictive Approaches", Statistics in Biopharmaceutical
Research, 2,

106. References (continued)
Plante, R. D. (2001), “Process Capability: A Criterion for Optimizing Multiple Response Product and Process Design”, IIE Transactions, 33,
Polansky, A.M. (2001), “A Smooth Nonparametric Approach to Process Capability”,
Technometrics, 43(2),
Rajagopal, R. and del Castillo,, E. (2005), “Model-Robust Process Optimization Using Bayesian Model Averaging”, Technometrics, 47,
Rajagopal, R., del Castillo, E., Peterson, J. J. (2005), "Model and Distribution-Robust Process
Optimization with Noise Factors", Journal of Quality Technology 37,
Rajagopal, R. and del Castillo, E. (2006), “A Bayesian approach for multiple criteria decision making
with applications in Design for Six Sigma”, Journal of the Operational Research Society, 1–12.
Robinson, T.J., Anderson-Cook, C.M. and Hamada, M.S. (2009). “Bayesian Analysis of
Split-Plot Experiments with Non-Normal Responses for Evaluating Non- Standard Performance Criteria”.
Technometrics, 51, pp
Savage, S. (2009) The Flaw of Averages - Why We Underestimate Risk in the Face of Uncertainty , John Wiley and Sons, Inc. , Hoboken, NJ.

107. References (continued)
Stockdale, G. and Cheng, Aili (2009), “Finding Design Space and a Reliable Operating
Region using a Multivariate Bayesian Approach with Experimental Design”, Quality
Technology and Quantitative Management 6(4),