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Design of Experiments for Pharmaceutical Process Optimization
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ASQ Fall Technical ConferenceOctober 15, 2004
Robert P. Cogdill, Arwa S. El-Hagrasy, Carl A. Anderson, James K. Drennen, III
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www.dcpt.duq.edu
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“(Six Sigma/TQC) will not work (in pharma) because the processes and materials are too complex…”
-Anonymous
“No wind favors him who has no destined port.”-Montaigne
Effective DOE will provide the key to greater understanding of pharmaceutical processes…
…thereby enabling Total Quality Control
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Hierarchy of Process Understanding
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Current State:
• “Trial-n-Error”• Batch Processes
• ‘silo’ conditions• ‘black-box’ controls
• Quality-by-Inspection
Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
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Hierarchy of Process Understanding
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Desired State:
• 1st Principles Understanding• Robust Processes• Total Quality Control
Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
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Hierarchy of Process Understanding
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•DOE Optimization•Mechanistic Understanding•Process Analytical Technology (PAT)
•Feed-forward control•Real-Time-Release (RTR)•Quality-by-Design
Intermediate State:
Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
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Typical Solid Dosage Process
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FBDrier
Wet Granulation
Dispensory
Milling/Sizing
Blending
TabletPress
CoatingInspection &
Release
PAT PAT PAT PAT
PAT PAT PAT
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“Ideal” Solid Dosage Process
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DryGranulation
Dispensory
Milling/Sizing
TabletPress
Coating
Inspection &Release
BlendingProduct Quality Expectation
• Embedded PAT
• Feed-forward control
•Transfer functions abstract unit operations
•Model-predictive control
•Continuous optimization via feedback
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The Optimization Problem
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Experimental trials are expensive and time-consuming (e.g.- dissolution)The processes “cascade”, requires an integrated approach to optimizationExpect significant, complex interactionsGenerally, must be performed anew for each formulation
What will we learn that can be carried forward?Will process characterization endure scale up?
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Fluidized Bed Drying
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Input factors:Input air volume, humidity, temperatureProduct moisture contentMaterial propertiesLoading
Output factors:Drying timeMaterial properties
Used for other operations such as coating and granulation
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Wet Granulation
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Input factors:Rotational speedProcess scaleProduct moisture contentBinder fluid applicationMaterial properties
Output factors:Granulation timeParticle size distributionMaterial propertiesTablet performance
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Roller Compaction
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Input factors:Feed rate (volume)Roll speedRoll pressureComposition (compressibility)
Output factors:Tensile strength/hardnessGranule sizeTablet performance
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Tablet Compression
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Input factors:Compression forceDwell timeTablet size & shapeMaterial properties
Output factors:Tablet hardnessFriabilityTablet performanceUniformity
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DOE Case Study
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A Process Analytical Technology Approach to Near-Infrared Process Control of Pharmaceutical Powder Blending
Part I: D-Optimal Design for Characterization of Powder Mixing and Preliminary Spectral Data Evaluation
Arwa S. El-Hagrasy1, Frank D’Amico2, James K. Drennen, III2
1Bristol-Myers Squibb, 2Duquesne University
-In Processwww.dcpt.duq.edu
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Powder Blending
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Kinetic mixing of active pharmaceutical ingredients (API) with excipientsAdequacy of blending directly impacts content uniformityOptimal blend time is established during scale-up and validationBlend time is affected by variation in material physico-chemical properties, environmental conditions, process scale, speed, loading
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Powder Blending
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Project objectives:Characterization of the powder blending process for a binary mixtureInvestigation of the sensitivity of near-infrared (NIR) spectroscopy to changes in the physicochemical properties of powders during mixing
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Powder Blending
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Factors varied:Drug concentrationRotational speedHumidity
Factors held constantMaterial propertiesTemperatureFill levelLoading scheme
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Powder Blending
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8-qt plastic V-blender (Patterson-Kelly)Blend composition
Salicyclic acid (SA), 30.5 µm particle sizeLactose, 115.5 µm particle size
Input factor levelsRelative humidity: 20%, 60%SA concentration: 3%, 7%, 11%Rotation speed: 12.8, 20.3 rpm
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Powder Blending
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Sampling methodBlend process monitored for 50 minutesStopped at pre-determined time intervals for sampling with thief probe and NIR analysisThief samples analyzed via UV spectroscopy (296.9 nm)
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Powder Blending
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0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35 40 45 50
Time (min)
%R
elat
ive
Stan
dard
Dev
iatio
n
Left shell
Right shell
Top
Middle
Bottom
•Typical powder blend profiles
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D-Optimal Design
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Experimental design generated using JMPND = 16 experiments D-Efficiency Calculation:
D-Efficiency of best design: 68%
⎟⎟⎠
⎞⎜⎜⎝
⎛=− pefficiencyD
1'
D
XXN1100
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D-Optimal Design
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Experimental ConditionsOrder
Humidity Salicylic acid Concentration
Blender Speed *
I 20% 3% 12.8
II 20% 11% 12.8
III 20% 3% 20.3
IV 20% 7% 12.8
V 20% 7% 20.3
VI 20% 11% 20.3
VII 20% 11% 12.8
VIII 60% 3% 20.3
IX 60% 11% 20.3
X 60% 7% 12.8
XI 60% 7% 20.3
XII 60% 7% 20.3
XIII 60% 11% 12.8
XIV 60% 3% 20.3
XV 60% 7% 12.8
XVI 60% 3% 12.8
* Blender speed measured in rpm
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Thief-Sample Position Dependency
• Outliers were flagged during UV analysis as samples exceeding 1.5x IQR
RL
1 2
3 4
5
0
5
10
15
20
25
30
35
40
1 2 3 4 5
Location
% O
utlie
rs
BA
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Results
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Adj. R2 > 0.68 (.81, after removing outlier)
5
10
15
20
25
End
poin
t (m
in) A
ctua
l
5 10 15 20 25
End point (min) Predicted
-6
-4
-2
0
2
4
6
End
poin
t (m
in) R
esid
ual
5 10 15 20 25
End point (min) Predicted
A B
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Results
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00.10.20.30.40.50.60.70.80.9
1
Blender Speed Humidity Concentration
P = 0.0002
P = 0.002
P = 0.0331
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Red = L.S. Effect Mean, Yellow = (10 x Stdev)
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0
2
4
6
8
10
12
14
16
18
20% 60% 3 7 11 12.8 20.3
Humidity SA Concentration Rotational Speed
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Case Study Conclusions
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All main effects were found to be significantHumidity is especially important since it is difficult to controlPoor repeatability of blend time was observed among replicate blends
PAT will provide a more realistic means of controlling powder mixing
NIR spectroscopy was found to be effective in determining blend uniformity (not shown)
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Blueprint for Optimization
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Feasibility study and risk analysis provide initial starting points, and optimization constraintsDOE
Flexibility to accommodate disparate data typesInclude first-order/two-way interactionsShould be sufficient to estimate transfer functions
Subsequent experiments are used to “fine-tune” the transfer function
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Blueprint for Optimization
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DOE will reveal most effective CCP’s for process analysisTransfer functions will allow estimation of “realistic” critical control limitsTransfer functions should include sensor data, enable model-predictive controlProcess characterization should be integrated with PAT method developmentInformation management will be critical for continuous improvement of process control
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Thank You!
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• FOSS NIRSystems• Jim Fete, Beckman Coulter• Lamija Begic• Damir Begic• Amber Fullmer• Mohamed Ghorab• David Molseed• Katie Reardon
www.dcpt.duq.edu