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

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

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

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

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

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

“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

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?

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

Wet Granulation

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Input factors:Rotational speedProcess scaleProduct moisture contentBinder fluid applicationMaterial properties

Output factors:Granulation timeParticle size distributionMaterial propertiesTablet performance

Roller Compaction

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Input factors:Feed rate (volume)Roll speedRoll pressureComposition (compressibility)

Output factors:Tensile strength/hardnessGranule sizeTablet performance

Tablet Compression

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Input factors:Compression forceDwell timeTablet size & shapeMaterial properties

Output factors:Tablet hardnessFriabilityTablet performanceUniformity

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

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

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

Powder Blending

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Factors varied:Drug concentrationRotational speedHumidity

Factors held constantMaterial propertiesTemperatureFill levelLoading scheme

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

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)

Powder Blending

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0 5 10 15 20 25 30 35 40 45 50

Time (min)

%R

elat

ive

Stan

dard

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iatio

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Left shell

Right shell

Top

Middle

Bottom

•Typical powder blend profiles

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

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

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1 2 3 4 5

Location

% O

utlie

rs

BA

Results

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Adj. R2 > 0.68 (.81, after removing outlier)

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End

poin

t (m

in) A

ctua

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5 10 15 20 25

End point (min) Predicted

-6

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End

poin

t (m

in) R

esid

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5 10 15 20 25

End point (min) Predicted

A B

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

Red = L.S. Effect Mean, Yellow = (10 x Stdev)

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0

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20% 60% 3 7 11 12.8 20.3

Humidity SA Concentration Rotational Speed

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)

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

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

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