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1 Utilizing RM in a Submission for Developing Critical Process Parameters and Critical to Quality Attributes Kelly Canter, PhD Right the First Time Program.

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Presentation on theme: "1 Utilizing RM in a Submission for Developing Critical Process Parameters and Critical to Quality Attributes Kelly Canter, PhD Right the First Time Program."— Presentation transcript:

1 1 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 Kelly Canter, PhD Right the First Time Program Office Pfizer Inc., Groton, CT FDA/Industry Statistics Workshop September 2006

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

3 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 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 4 Quality by Design – Right First Time Commercializable Manufacturing Process (API or DP) Risk Assessment Prioritized Experimental Plans Prioritized PAT Plans Experimentation /Method Dev/Documentation Design Space Definition Process Control Strategy Change Control Strategy and Implementation Regulatory Filing/Approval Process Capability Monitoring Continuous Improvement (Process Changes) e.g. Cpk Launch Process Understanding Process Control Continuous Improvement

5 5 Why Do QbD? ( Value Proposition) Work Impact During Development Decrease ICH re-dos Decrease Validation re-dos Decrease Clinical Batch re-dos Transparent assessment of risk Prioritization Work Impact During Development Decrease ICH re-dos Decrease Validation re-dos Decrease Clinical Batch re-dos 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 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 Getting at the Right Process Knowledge = Value to Pfizer, FDA and Patients

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

7 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 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 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 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 9 Risk Assessment Work Process

10 10 Risk Assessment and Prioritization Decide whats important to evaluate 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 Ys: Key Quality Attributes Vital Few Xs: Key Process Parameters Many Ys Quality Attributes Many Xs Process Parameters

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

12 12 Risk Assessment Case Study Dry Granulation Tablet

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

14 14 Risk Assessment Work Process Risk Assessment

15 15 Risk Assessment Meeting Participants R&D Co-Facilitator API –Analytical –Formulation* –Chemical DP –Analytical –Formulation –Chemical* Ext. Subject matter experts PAT R&D Statistician Scribe (workbook) Line management Team Co-Leader R&D Co-Facilitator API –Analytical –Formulation* –Chemical DP –Analytical –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 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 17 Risk Assessment Step 1. Create a Process Map Describes the composition and boundaries of each focus area. Focus Area 1 Raw Material Dispensing Preblending CP-526, 555-18, Cellulose microcr, PH200, Calcium Hydrogrenphosphate (amhydrous), colloidal Silicon dioxide, Croscarmellose Sodium 300 L bin 15 minutes Sieving Focus Area 2 Comil 0.8 mm sieve Lube Blend Focus Area 3 300 L bin 2 minutes Dry Granulation and BlendBepex K 200/50 Roll: Deep Pocket Screen Size: 0.8 mm Focus Area 4 Blending300 L bin 3 minutes Lube Blend Focus Area 5 300 L bin 3 minutes Compression Focus Area 6 IMA Comprima 300 Film Coating Focus Area 7 Glatt GC 1250 Process StepCommercial ManufactureBoundaries Raw Material Dispensing Initial Blend De-lumped Unlubed Blend Lubed Blend Unlubed Granulation Final Blend Tablet Cores Film Coated Tablets

18 18 Key AttributeYYYYNYY Rank7777510 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 10 0 840FMEA Roll Force10 10 777DOE Screen Size10 055632DOE Gap Width10 55055585DOE Material Throughput1015 011437DOE Roller Compaction Calibration 5551055427FMEA Sampling Size10 1015421MSA Roll Speed55510011370DOE Equipment Aging51101011286 Transfer Distance into Roller 10511015278 Risk Assessment Step 2. Identify QAs and How Measured Step 3. Identify and Prioritize PPs Focus Area 4 - Dry Granulate + Blend

19 19 KQA 1 KQA 2 KQA 3 KQA 4 KQA 5 Risk Assessment Step 4. Group Key PPs by Experiments Focus Area 4 - Dry Granulate + Blend Raw Materials....... … Define Process Flowchart Define Focus Areas Identify KQAs and Associated Measurement Identify and Prioritize KPPs Define Experiments KPP 1 KPP 2 KPP 3 KPP 4 KPP 5 Experiment 1 Experiment 2 Experiment 3 Unit Op 1....... … Unit Op 2....... … Prioritize Experiments Next step:....... …

20 20 Risk Assessment Step 5. Create PAT Prioritization Matrix Focus Area 4 - Dry Granulate and Blend Focus Area 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 HPLCMLMY 4FlowabilityLLHY 4 Blend Uniformity % rsdHPLCMMHY 4 Segregation Index % rsdJ&J TesterLLHY 4 Particle Size Distribution SizeSieve AnalysisHLHY

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

22 22 Initial Risk Assessment Complete

23 23 The Work Process Risk Assessment Experimental Planning

24 24 Experimental Planning Example DOE Focus Area 4 - Dry Granulate + Blend Key AttributeYYYYNYY Rank7777510 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 10 0 840FMEA Roll Force10 10 777DOE Screen Size10 055632DOE Gap Width10 55055585DOE Material Throughput1015 011437DOE Roller Compaction Calibration 5551055427FMEA Sampling Size10 1015421MSA Roll Speed55510011370DOE Equipment Aging51101011286 Transfer Distance into Roller 10511015278

25 25 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 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 Experimental Design for Gerteis Study Roll force Gap width Granulating sieve size Granulator speed

26 26 DOE Regression Models Model Coefficients (p - values) Main EffectsInteractionsQuad. Quality Attributes (Intercept) Roll ForceGap 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) --- -17.9 (<0.0001) Log (Gran RSD) (-0.07) 0.10 (0.0758) --- 0.17 (0.0051) --- Log (Tablet Potency RSD) (-0.15) -0.08 (0.0025) -0.06 (0.0308) 0.06 (0.0180) --- CF @ Tablet Hard. = 7 kP (6.8) 2.0 (<0.0001) -0.6 (<0.0001) --- -0.5 (0.0002) --- FRI @ Tablet Hard. = 7 kP (0.06) --- 0.02 (0.0320) ---

27 27 Requirements to Map Design Space Boundary Conditions Process Parameters Gap Width1.7 – 3.5 mm Mill Screen0.8 – 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 28 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 29 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 Response (intercept) RF Coefficient (p-value) GW Coefficient (p-value) RF*GW Coefficient (p-value) Ln [Bypass Wt%] (0.70) -0.71 (0.0045) 0.37 (0.0479) -0.81 (0.0046) Statistics and Model Roll Force 4681012 1.4 2.0 2.6 3.2 3.8 Gap Width (mm) Unacceptable space

30 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) 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 31 The Work Process Risk Assessment Experimental Planning Prioritization

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

33 33 The Work Process Risk Assessment Experimental Planning Prioritization Experimentation

34 34 Building Models: KQA = f (KPP 1, KPP 2, …KPP i ) 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 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 35 Control-, Design- and Knowledge space Knowledge Space Design Space Control Space Proven Acceptable Range Normal Operating Range

36 36 Design Space

37 37 Acknowledgements Chris Sinko Roger Nosal Jim Spavins Vince McCurdy Tom Garcia Christina Grillo Mary Am Ende Dan OConnell Chris Sinko Roger Nosal Jim Spavins Vince McCurdy Tom Garcia Christina Grillo Mary Am Ende Dan OConnell


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