19 august 20081 case studies in quality by design with design of experiments from pharmaceutical...
TRANSCRIPT
19 August 2008 1
Case Studies in Quality by Design with Design of Experiments From Pharmaceutical Technology
Lynn Torbeck19 August 2008
19 August 2008 2
Overview
A little, very little, history3 types of controlled experimentsKey literature and datesToday’s driving force behind QbD“Show me an example in my area of interest”Case Studies from Pharm Tech
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A Short Bit of History
Sir Ronald A. FisherBorn 1890, EnglandDied 1962, AustraliaGraduated college in 1913, math, genetics1919 joined Rothamsted Experimental Station in Harpenden, EnglandThe right person in the right place.
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Three Controlled Experiments
John S. Mill, System of Logic, 18431. Success / Failure One run, no factors varied, one outcome, yes/no Easy to design, easy to analyze Lack of comparison, inefficient
2. OFAT, One-Factor-at-a-Time We all learned this in school Several runs, one factor varied, two outcomes Easy to Design, has comparison of outcomes Can’t find interactions and is inefficient
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Fisher’s Experiments
Multiple runs, multiple factors variedMultiple outcomesWill find interactionsIs much more efficientComparison of outcomes
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Key Literature
1926, “The Arrangements of Field Experiments.” Journal of the Ministry of Agriculture of Great Britain. Fisher.1935, The Design of Experiments, Oliver & Boyd, London. Fisher.1951, “On the Experimental Attainment of Optimum Conditions,” Box and Wilson. The original source for QbD !
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Today’s Driving Force
FDA / PAT guidanceICH Q8 – Quality by DesignICH Q8 _ Annex with DOE exampleThe freedom of Design SpaceAbility to change within Design SpaceEconomics and cost savingsProduct / Process Knowledge
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State of the Topic
While there is more to Quality by Design than DOE, it seems to be the part that most people have the most trouble with.Chemometrics is many times more complicated than DOE but yet it seems to be more readily accepted than DOE.
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Show Me an Example
Many people have taken a DOE class at some time, but still have difficulty in getting started.The most common request is for examples in specific areas.Examples here show that it is not all that difficult to get started.QbD was being done before ICH Q8
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Six Steps to Designing
1. Do your homework2. Define the measured responses
(CQA)3. Brainstorm factors (CPP)4. Select 2-7 factors to be treatments5. Select levels or values for
treatments6. Select a design
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A Short List of DesignsNumber of Runs
4 8 8 9 12 16
Number 2 22 3*3, 32
of 3 23-1III 23
Factors 4 24-1IV PB9 24
5 25-2III PB8 PB9 25-1
V
6 26-3III PB8 PB9 26-2
IV
7 27-4III PB8 PB9 27-3
IV
8 PB9 28-4IV
9 PB12
10 PB12
11 PB12
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Pharm Tech Yearbook, 1999
“Functionality Testing of a Co-processed Diluent Containing Lactose and Microcrystalline Cellulose”Gohel, M., et allPre-formulation development of excipients
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Objective
“The objective of the present study was to prepare the directly compressible adjuvant by using a simpler process that could be adopted by any pharmaceutical company.Product is a tablet
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Treatments
A: Ratio of lactose to MCC 75:25, 85:15
Binding Agent Dextrin, HPMC
% binding agent 1.0%, 1.5%
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Held Constant
Stirring speed at 35 rpmStirring time at 90 minutes
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Agglomerate Responses
Bulk Density, Tapped DensityAngle of Repose, Flow RateHausner ratioCarr’s IndexFriability IndexMoisture uptake
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Statistical Design
Three treatmentsEach at two levelsEight sets of conditions or runs A 23 full factorial design
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Results
This is a complicated set of data with many two factor interactions, but it can be understood by looking at a geometric presentation of the factors and the responses for flow rate.Ratio is on the horizontal, A, axisAgent is on the vertical, B, axisPercent is on the third, C, axis
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AgentB
HPMC
16.00 16.00
19.00 14.00
RatioDextrin 14.80 18.00 A
75/25% 85/25%
1.0%
15.00 14.60
C Percent1.5%
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Observations for Flow Rate
1. Within these bounds, flow is 14.0 to 19.0 g/s
2. Slowest is 85/15, HPMC, 1.5%.3. Fastest is 75/25, HPMC, 1.5%4. Fast is 85/15, Dextrin, 1.0%
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Pharm Tech, November 1999
This is a related example.“An Investigation of the Direct-Compression Characteristics of Co-processed Lactose-Microcrystalline Cellulose Using Statistical Design.”Gohel, M., and Jogami, P.
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Pharm Tech, June, 1993
A bottle packaging example.“The Effect of Rayon Coiler on the Dissolution of Hard-Shell Gelatin Capsules.Hartauer, K.; Bucko, J.; Cooke, G; Mayer, R.; Schwier, J. and Sullivan, G.
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BioPharm, October 1997
“Demonstrating Process Robustness for Chromatographic Purification of a Recombinant Protein.”Kelly, B.; Jennings, P.; Wright, R. and Briasco, C.
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Objective
“Control is achieved by setting operating ranges for manipulated process variables. Those ranges should ensure that a process does not fail within the multidimensional operating space defined by those limits.”That is, the Design Space !
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Treatments
1. Load Mass 2.4 – 15.52. Load Conductivity 2.5 – 4.23. % Cleavage 63 – 754. Wash pH 9.4 – 9.65. Wash volume 9.7 – 11.66. Elution pH 9.4 – 9.67. Elution conductivity 8.6 – 14.4
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Responses
1. Recovery %2. Purity %3. rhIL-11 mass4. Product pool volume5. Elution pool concentration
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Statistical Design
Wash pH / Wash volume confoundedElution pH / Elution conductivity confounded
1. Five factors each at two levels2. 16 runs will still find the two factor
interactions3. Design is a 25-1 fractional factorial
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A:Elution pH B:Conductivity C:Cleavage D:Load Mass E:Wash pH Recovery % Purity %-1 -1 -1 -1 1 112.6 96.31 -1 -1 -1 -1 90.7 96.9-1 1 -1 -1 -1 104.9 97.11 1 -1 -1 1 72.8 97.3-1 -1 1 -1 -1 99.6 96.11 -1 1 -1 1 84.2 97.1-1 1 1 -1 1 98.4 97.31 1 1 -1 -1 104.2 97.8-1 -1 -1 1 -1 104.3 91.81 -1 -1 1 1 79.0 94.9-1 1 -1 1 1 94.8 96.61 1 -1 1 -1 93.7 96.0-1 -1 1 1 1 95.5 94.41 -1 1 1 -1 88.5 93.9-1 1 1 1 -1 78.7 96.51 1 1 1 1 58.7 98.4
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Design Space
IndependentFactorSpace
?DependentResponse
Space
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Conceptual Design Space
Uncertain space
Region of operability
Operation Space Opt
Region of Interest
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Statistical Design Space
“The mathematically and statistically defined combination of Factor Space and Response Space that results in a system, product or process that consistently meets its quality characteristics, SSQuIP, with a high degree of assurance.” LDT
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Analysis
Analysis is done by fitting a mathematical model to the factors (CPP) and the responses (CQA) that includes the factor main effects and the significant two factor interactionsThe model is then used to find contour plots for recovery and purity.
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Design-Expert® Software
Recovery112.6
58.7
X1 = A: Elution pHX2 = B: Conductivity
Actual FactorsC: Cleavage = -1.00D: Load Mass = 0.00E: Wash pH = 0.00
-1.00 -0.50 0.00 0.50 1.00
-1.00
-0.50
0.00
0.50
1.00Recovery
A: Elution pH
B: C
ondu
ctiv
ity86.779291.133395.487599.8417
104.196
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Design-Expert® Software
Purity98.4
91.8
X1 = A: Elution pHX2 = B: Conductivity
Actual FactorsC: Cleavage = -1.00D: Load Mass = 0.00E: Wash pH = 0.00
-1.00 -0.50 0.00 0.50 1.00
-1.00
-0.50
0.00
0.50
1.00Purity
A: Elution pH
B: C
ondu
ctiv
ity
94.8167
95.2708
95.725
96.1792
96.6333
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Design-Expert® Software
Overlay Plot
RecoveryPurity
X1 = A: Elution pHX2 = B: Conductivity
Actual FactorsC: Cleavage = -1.00D: Load Mass = 0.00E: Wash pH = 0.00
-1.00 -0.50 0.00 0.50 1.00
-1.00
-0.50
0.00
0.50
1.00Overlay Plot
A: Elution pH
B:
Co
nd
uct
ivity
Recovery: 90Recovery: 100
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Pharm Tech, February 1999
“Blow-Fill-Seal Technology: Part II, Design Optimization of a Particulate Control System.”Price, J.
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Objectives
1. Optimize the particulate control system
2. Find cause and effect relationships
3. Alter the system settings to improve performance
4. Find interactions between factors
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Treatments
1. HEPA flow rate % 20 50 802. Damper % open 30 55 803. Chimney air ft/min 300 550 8004. HEPA height in 0 0.3755. Isolation plate Slotted –
Hole6. Knife cut Double Single
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Response
Particulate level.Three measurements at each of the 24 conditions
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Statistical Design
Six factors Three at two levels Three at three levels
16 combinations8 center pointsDesign is a 26-2 fractional factorialDesign is resolution IV
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Analysis
Analysis of Variance, ANOVA, was used.15 effects were included5 were statistically significant Damper HEPA height Knife cur Isolation plate HEPA flow * HEPA height OR {damper*knife cut}
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Conclusions
“The study met the design objective of minimizing the particulate levels while the particulate control system operated in the dynamic state. … a more thorough understanding of the cause and effect relationships between the critical input factors and the particulate levels was obtained using the DOE.”
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Pharm Tech, Analytical Validation, 1999
Robustness Testing of an HPLC Method Using Experimental Design.”Peters, P. and Paino, T.
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Objective
“This article describes an experimental design that challenged an analytical method that assays two components in a solid dosage drug product.”Confirm the robustness of an HPLC method.
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Treatments
HPLC system A, BHPLC column Y, XWavelength A 270, 290 B 215, 235
Flow rate 0.7, 1.3
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Treatments
Injection volume 10, 30Column tempAmbient, 30Mobile phase TFA 85, 75 MeCN 15, 25
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Responses
1. Resolution of component A and B2. Theoretical plates for A and B3. Tailing factor for A and B4. %RSD of the peaks for A and B
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Statistical Design
7 factors each at two levelsWavelength A and B are confoundedMobile phase TFA and MeCN are confounded8 runs done in triplicate for 24 totalDesign is a 27-4 fractional factorialDesign is resolution III.
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Analysis and Results
Visual inspection of an overlay of the 8 chromatograms shows that the method is robust within the tolerance limits of the parameters tested. They have acceptable resolution and peak shape.
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Compare Chromatograms
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Pharm Tech, May 1998
“A Systematic Formulation Optimization Process for a Generic Pharmaceutical Tablet.”Hwang, R.; Gemoules, M; Ramlose, D. and Thomasson, C.
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Objective
“ … optimizing an immediate release tablet formulation for a generic pharmaceutical product.”Develop a generic tablet with a disintegration time of 6-12 minutes, 5 minute dissolution of 40-60% and 45 minute dissolution of greater than 90%.
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Treatments
API particle size small largeAPI % 5% 10%Lactose MCC ratio 1:3 3:1MCC particle size small largeMCC density low high
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Treatments
Disintegrant cornstarch, glycolateDisintegrant % 1% 5%Talc 0 5%Mag Sterate 0.5% 1%
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Responses
Blend homogeneityCompression force %RSDEjection forceTablet weight %RSDTablet hardness
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Responses
Tablet friabilityTablet disintegration timeTablet dissolution at 5 minutesTablet dissolution at 45 minutes
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Statistical Design
9 factors each at two levels16 runsDesign is a 29-5 fractional factorialResolution III
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The best formulation:
API 7.14%Fast-Flo lactose 60.74%Avicel PH-302 30.37%Talc 1%Mag Stearate 0.75%
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Conclusion
“The formulation was successfully scaled up to a 120 kg batch size and the manufacturability and product quality were confirmed.”“This study has demonstrated the efficiency and effectiveness of using a systematic formulation optimization process … “
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Pharm Tech, March 1994
“Evaluation of a Cartridge and a Bag Filer System in Fluid-Bed Drying.Bolyard, K. and McCurdy, V.
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Pharm Tech Europe, April 2000
“Response Surface Methodology Applied to Fluid Bed Granulation.”Wehrle, P. et all
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Pharm TechMarch 1992 and May 1992
“A Compaction Study of Directly Compressible Vitamin Preparations for the Development of a Chewable Tablet, Parts I and II.Konkel, P. and Mielck, B.
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Pharm Tech, March 1994
“Computer Assisted Experimental Design in Pharmaceutical Formulation.”Dobberstein, R. et all.
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Pharm Tech, April 1998
“A Unique Application of Extrusion for the Preparation of Water Soluble Tablets.”Murphy, M. and Hollenbeck, R.
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Pharm Tech, June 2000
“Artificial Neural Network and Simplex Optimization for Mixing of Aqueous Coated Beads to Obtain Controlled Release Formulations.”Vaithiyalingam, S. et all.
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Summary
Looked at 13 Case studiesShown 3 types of analysis Shown several areas of applicationIllustrated how to get startedShown that Q8 QbD has a precedentDOE has been used for a long time
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Acknowledgements
The University of Adelaide Library is the owner of the image of Sir R. A. Fisher.Pharmaceutical Technology holds the copyright for the journal articles used in this presentation.Opinions in this presentation are that of Lynn Torbeck alone.