1. Right the First Time Program Office
Utilizing RM in a Submission for Developing Critical Process Parameters and Critical to Quality Attributes
Kelly Canter, PhD
Right the First Time Program Office
Pfizer Inc., Groton, CT
FDA/Industry Statistics Workshop September 2006

2. Outline QbD Terminology and Value Proposition
Risk Assessment Process (Case Study)
Experiments, PAT and Prioritization
Creation of Design Space

3. Alignment of ICH Q(8) Enhanced knowledge of product performance . . .
Establish range of material attributes, processing options & process parameters
Demonstrated product/process understanding
Results from PAT, DOE, Science of Scaling
Appropriate application of risk management principles
Establish Design Space
Flexible regulatory approaches
Risk based regulatory decisions
Mfg. process improvements w/in approved design space
Real time quality control Reduce product release tests

4. Quality by Design – “Right First Time”
Process Understanding
Continuous Improvement
Process Control
Process Control Strategy
Process Capability Monitoring
Commercializable Manufacturing Process (API or DP)
e.g. Cpk
Change Control Strategy and Implementation
Continuous Improvement (Process Changes)
Risk Assessment
Prioritized Experimental Plans
Prioritized PAT Plans
Regulatory Filing/Approval
Experimentation /Method Dev/Documentation
Design Space Definition
Launch

5. Why Do QbD? (Value Proposition)
Getting at the Right Process Knowledge =
Value to Pfizer, FDA and Patients
Work Impact During Development
Decrease ICH re-do’s
Decrease Validation re-do’s
Decrease Clinical Batch re-do’s
Transparent assessment of risk
Prioritization
Improvements to our Products and Processes
Decrease Variability
Assure market supply
Faster change implementation
Science support Quality investigtations
Reduce COG
Streamline regulatory reviews (S&E)
Framework for decreased regulatory burden
Standardization

6. Process Parameters Quality Attributes
Process Understanding
Process Parameters
People
Quality Attributes
Inputs to the process
control variability
of the Output I N P U T S
(X)
Equipment
y = ƒ(x)
Measurement y
Process
OUTPUT
Materials
Environment
J. Scott, ASTM, London 2004

7. What is a Quality Attribute?
Definitions
Quality Attribute
A physical, chemical or micorbiological property or characteristic of a material.
Key Quality Attribute (KQA)
Potential to impact product quality or process effectiveness
Evaluated by an associated analytical method.
Critical Quality Attribute (CQA)
impacts the safety or efficacy of a drug products

8. What is a Process Parameter?
Definitions
Process Parameters
Broadly defined as machines, materials, people, processes, measurements and environments
Key Process Parameter (KPP)
Influences product quality or process effectiveness
Critical Process Parameter (CPP)
Influences a CQA and that must be controlled within predefined limits to ensure the API or product meets its pre-defined limits

9. Risk Assessment Work Process

10. Risk Assessment and Prioritization Decide what’s important to evaluate
Quality Attributes
Process Parameters
Many Y’s
Many X’s
Process
Consensus decisions
Use process experience
Use project process knowledge
Focus on the “Voice of the Customer”
Process
Cause and Effect Matrix with “Effects” focused on KQAs
Vital Few Y’s: Key Quality Attributes
Vital Few X’s: Key Process Parameters

11. The QbD Work Process at a “High Level”
Experimentation
Prioritization
Risk Assessment
Experimental
Planning
Process
Understanding

12. Risk Assessment Case Study
Dry Granulation Tablet

13. Risk Assessment Objectives
Gain agreement on process scope
Decide what’s important to evaluate
Prioritize parameters based on risk
Gain agreement on high level experimental strategy
Identify and prioritize PAT applications

14. Risk Assessment Work Process

15. Risk Assessment Meeting Participants
R&D
Co-Facilitator
API
Analytical
Formulation*
Chemical DP
Formulation
Chemical*
Ext. Subject matter experts
PAT R&D
Statistician
Scribe (workbook)
Line management
Team Co-Leader
Pfizer Global Manufacturing
Co-Facilitator
API Tech Services
DP Tech Services
Manufacturing Supervisor QC QA
Team Co-Leader
Subject matter experts
PAT PGM
Line management

16. Risk Assessment Work Flow
Create a Process Map with Focus Areas
Identify all Quality Attributes and Determine How To Measure
Identify and Prioritize all Process Parameters (KPPs)
Group KPPs into Experiments
Create PAT Prioritization Matrix
Document
Yellow font =Pre-work required.

17. Risk Assessment Step 1. Create a Process Map Describes the composition and boundaries of each focus area.
Process Step
Commercial Manufacture
Boundaries
Raw Material Dispensing
CP-526, , Cellulose microcr, PH200, Calcium Hydrogrenphosphate (amhydrous), colloidal Silicon dioxide, Croscarmellose Sodium
Raw Material Dispensing
Focus Area 1
Preblending
300 L bin 15 minutes
Initial Blend
Initial Blend
Focus Area 2
Comil 0.8 mm sieve
Sieving
De-lumped Unlubed Blend
Focus Area 3
300 L bin 2 minutes
De-lumped Unlubed Blend
Lube Blend
Lubed Blend
Dry Granulation and Blend
Bepex K 200/50 Roll: Deep Pocket
Screen Size: 0.8 mm
Lubed Blend
Focus Area 4
Blending
300 L bin 3 minutes
Unlubed Granulation
Unlubed Granulation
Focus Area 5
300 L bin 3 minutes
Lube Blend
Final Blend
Final Blend
Focus Area 6
Compression
IMA Comprima 300
Tablet Cores
Focus Area 7
Tablet Cores
Film Coating
Glatt GC 1250
Film Coated Tablets

18. Particle Size Distribution Content Uniformity (Focus Area 6)
Risk Assessment Step 2. Identify QAs and How Measured Step 3. Identify and Prioritize PPs Focus Area 4 - Dry Granulate + Blend
Key Attribute Y N
Rank 7 5 10
Process Parameter
Sieve Cut Potency
Blend Uniformity
Particle Size Distribution
Mill Choking
Surface Area
Hardness (Focus Area 6)
Content Uniformity (Focus Area 6)
Score
Exp./ Approach
Operator Training Procedures
840
FMEA
Roll Force 1
777
DOE
Screen Size
632
Gap Width
585
Material Throughput
437
Roller Compaction Calibration
427
Sampling Size
421
MSA
Roll Speed
370
Equipment Aging
286
Transfer Distance into Roller
278

19. Risk Assessment Step 4. Group Key PPs by Experiments Focus Area 4 - Dry Granulate + Blend
Raw Materials
Unit Op1
Unit Op2
Define Process Flowchart …
KQA1
KQA2
KQA3
KQA4
KQA5
Define Focus Areas …
KPP1
KPP2
KPP3
KPP4
KPP5
Identify KQAs and Associated Measurement …
Experiment1
Experiment3
Experiment2
Identify and Prioritize KPPs
Define Experiments …
Prioritize Experiments
Next step:

20. Risk Assessment Step 5. Create PAT Prioritization Matrix Focus Area 4 - Dry Granulate and Blend
Quality Attributes
Metric/ Unit
Measurement System
Probability of Success (H/M/L)
Criticality/ Benefit (H/M/L)
Cost (H/M/L)
Key Attribute (Y/N) 4
Sieve cut potency
% Intent
HPLC M L Y
Flowability H
Blend Uniformity
% rsd
Segregation Index
J&J Tester
Particle Size Distribution
Size
Sieve Analysis

21. Risk Assessment Step 6. Document the Process Understanding
Experimental Strategy
Protocols
Primary Data
Scientific Reports
Global Document Management System

22. Initial Risk Assessment Complete

23. The Work Process
Experimental
Planning
Risk Assessment

24. Particle Size Distribution Content Uniformity (Focus Area 6)
Experimental Planning “Example DOE” Focus Area 4 - Dry Granulate + Blend
Key Attribute Y N
Rank 7 5 10
Parameter
Sieve Cut Potency
Blend Uniformity
Particle Size Distribution
Mill choking
Surfac e Area
Hardness (Focus Area 6)
Content Uniformity (Focus Area 6)
Score
Exp. Strategy
Operator Training Procedures
840
FMEA
Roll Force 1
777
DOE
Screen Size
632
Gap Width
585
Material Throughput
437
Roller Compaction Calibration
427
Sampling Size
421
MSA
Roll Speed
370
Equipment Aging
286
Transfer Distance into Roller
278

25. Experimental Design for Gerteis Study
D-Optimal Design
Process Parameters
Quality Attributes
Granulation particle size
Sieve cut uniformity
Blend potency & uniformity
Tablet potency & uniformity
Hardness at 7 kP compress. force
Friability at 7 kP compression force
Roll force
Gap width
Granulating sieve size
Granulator speed

26. DOE Regression Models Model Coefficients (p - values)
Main Effects
Interactions
Quad.
Quality Attributes (Intercept)
Roll Force
Gap Width
Mill Screen Size
Mill Speed
Roll Force x Gap Width
Roll Force x Mill Screen Size
Mill Screen Size 2
Gran Particle Size (216)
51 (<0.0001)
---
68 (<0.0001)
38 (0.0006)
Sieve Cut RSD (41.4)
-6.4 (<0.0001)
1.2 (0.2650)
(<0.0001)
Log (Gran RSD) (-0.07)
0.10 (0.0758)
0.17 (0.0051)
Log (Tablet Potency RSD) (-0.15)
(0.0025)
(0.0308)
0.06 (0.0180)
Tablet Hard. = 7 kP (6.8)
2.0 (<0.0001)
-0.6 (<0.0001)
-0.5 (0.0002)
Tablet Hard. = 7 kP (0.06)
0.02 (0.0320)

27. Requirements to Map Design Space
Boundary Conditions
Process Parameters
Gap Width
1.7 – 3.5 mm
Mill Screen
0.8 – 1.5 mm
Quality Attributes
Sieve Cut Variability (% RSD)
<35%
% Bypass
<15%
Compression Force at 7 kP Hardness
<8.5 kN
Tablet Uniformity
<1.0%

28. Yellow Region: Acceptable combinations of process parameters.
Rationale for Process Ranges within Design Space (0.8 mm Mill Screen Size and 50 rpm Granulator Speed)
Yellow Region: Acceptable combinations of process parameters.
Unacceptable space

29. Coefficient (p-value) Coefficient (p-value)
Rationale for Process Ranges within Design Space Contour Map – Bypass Weight %
Bypass weight loss is highest in upper left quadrant of Roll Force vs Gap Width
3.8
Statistics and Model
3.2
Response (intercept) RF
Coefficient (p-value) GW
Coefficient (p-value)
 RF*GW
Ln [Bypass Wt%]
(0.70)
(0.0045)
0.37 (0.0479)
(0.0046)
2.6
Gap Width (mm)
2.0
1.4 4 6 8 10 12
Roll Force
Unacceptable space

30. Conclusions from DOE (D-Optimal)
Increasing roll force improved (lowered RSD) granulation and tablet uniformity.
Increasing roll force also reduced % bypass
However, increasing roll force increased the tablet compressional force required (Safety Margin 8.5 kN)
Acceptable process range for roll force is 5-9 kN (see Design Space)

31. The Work Process
Prioritization
Risk Assessment
Experimental
Planning

32. Full Factorial w/center
Experimental Strategy & Prioritization Example
Fractional Factorial
(Focus Areas1&2)
Central Composite
Focus Areas 1&2) 1
Full Factorial w/center
Add axial points
to Full Factorial 2
Gage R&R
(Focus Area 3) 3
FMEA
(Focus Areas 2&3) 4
Etc…

33. The Work Process Experimentation Risk Assessment Prioritization
Experimental
Planning

34. Building Models: KQA = f (KPP1, KPP2, …KPPi) Conclusions:
Operating target and ranges were identified for each of the following key parameters, key attributes:
Roll force (KPP1)
Impacts particle size, blend uniformity, tablet uniformity (KQA1, KQA2, KQA3)
Gap width (KPP2)
Impacts tablet uniformity (KQA3)
Screen size (KPP3)
Impacts sieve cut uniformity (KQA4)
Granulator speed (KPP4)
Not significant for KQAs investigated

35. Control-, Design- and Knowledge space
Design Space
Control Space
Proven Acceptable Range
Normal Operating Range

36. Design Space

37. Acknowledgements Chris Sinko Roger Nosal Jim Spavins Vince McCurdy
Tom Garcia
Christina Grillo
Mary Am Ende
Dan O’Connell