Design of Experiments for Pharmaceutical Process Optimization
ASQ Fall Technical Conference October 15, 2004
Robert P. Cogdill, Arwa S. El-Hagrasy, Carl A. Anderson, James K. Drennen, III 1
www.dcpt.duq.edu
2
“(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 3
Hierarchy of Process Understanding Current State: • “Trial-n-Error” • Batch Processes • ‘silo’ conditions • ‘black-box’ controls • Quality-by-Inspection
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Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
Hierarchy of Process Understanding Desired State: • 1st Principles Understanding • Robust Processes • Total Quality Control
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Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
Hierarchy of Process Understanding Intermediate State: •DOE Optimization •Mechanistic Understanding •Process Analytical Technology (PAT) •Feed-forward control •Real-Time-Release (RTR) •Quality-by-Design
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Adapted from: Ajaz Hussain, AAPS 39th Pharm. Technologies Conf. at Arden House, Jan. 2004
Typical Solid Dosage Process PAT
FB Drier
Dispensory Wet Granulation
Milling/ Sizing
PAT
PAT
Blending
PAT
Coating Tablet Press
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PAT
PAT
PAT
Inspection & Release
“Ideal” Solid Dosage Process Dispensory Blending Pro duc t
Qu a l it
yE
xpe cta ti
Dry Granulation
on
• Embedded PAT • Feed-forward control •Transfer functions abstract unit operations •Model-predictive control •Continuous optimization via feedback 8
Milling/ Sizing Tablet Press
Inspection & Release Coating
The Optimization Problem y Experimental trials are expensive and timeconsuming (e.g.- dissolution) y The processes “cascade”, requires an integrated approach to optimization y Expect significant, complex interactions y Generally, must be performed anew for each formulation What will we learn that can be carried forward?
y Will process characterization endure scale up? 9
Fluidized Bed Drying y Input factors: Input air volume, humidity, temperature Product moisture content Material properties Loading
y Output factors: Drying time Material properties
y Used for other operations such as coating and granulation 10
Wet Granulation y Input factors:
Rotational speed Process scale Product moisture content Binder fluid application Material properties
y Output factors: 11
Granulation time Particle size distribution Material properties Tablet performance
Roller Compaction y Input factors:
Feed rate (volume) Roll speed Roll pressure Composition (compressibility)
y Output factors: Tensile strength/hardness Granule size Tablet performance 12
Tablet Compression y Input factors:
Compression force Dwell time Tablet size & shape Material properties
y Output factors: 13
Tablet hardness Friability Tablet performance Uniformity
DOE Case Study 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 1
Bristol-Myers Squibb, 2Duquesne University
-In Process www.dcpt.duq.edu
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Powder Blending y Kinetic mixing of active pharmaceutical ingredients (API) with excipients y Adequacy of blending directly impacts content uniformity y Optimal blend time is established during scaleup and validation y Blend time is affected by variation in material physico-chemical properties, environmental conditions, process scale, speed, loading 15
Powder Blending y Project objectives: ƒ Characterization of the powder blending process for a binary mixture ƒ Investigation of the sensitivity of near-infrared (NIR) spectroscopy to changes in the physicochemical properties of powders during mixing
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Powder Blending y Factors varied: Drug concentration Rotational speed Humidity
y Factors held constant 17
Material properties Temperature Fill level Loading scheme
Powder Blending y 8-qt plastic V-blender (Patterson-Kelly) y Blend composition Salicyclic acid (SA), 30.5 µm particle size Lactose, 115.5 µm particle size
y Input factor levels Relative humidity: 20%, 60% SA concentration: 3%, 7%, 11% Rotation speed: 12.8, 20.3 rpm 18
Powder Blending y Sampling method ƒ Blend process monitored for 50 minutes ƒ Stopped at pre-determined time intervals for sampling with thief probe and NIR analysis ƒ Thief samples analyzed via UV spectroscopy (296.9 nm)
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Powder Blending 80
•Typical powder blend profiles
70
Left shell %Relative Standard Deviation
60
Right shell Top
50
Middle Bottom
40
30
20
10
0 0
5
10
15
20
25 Time (min)
20
30
35
40
45
50
D-Optimal Design y Experimental design generated using JMP y ND = 16 experiments y D-Efficiency Calculation: ⎛ 1 D − efficiency = 100⎜⎜ X'X ⎝ ND
1
p
⎞ ⎟⎟ ⎠
y D-Efficiency of best design: 68%
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D-Optimal Design Experimental Conditions Order
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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
Thief-Sample Position Dependency • Outliers were flagged during UV analysis as samples exceeding 1.5x IQR
A
B 40 35
% Outliers
30
2
1 3
4
25 20 15
5
10 5 L
R 0 1
2
3 Location
23
4
5
Results Adj. R2 > 0.68 (.81, after removing outlier) A
B 25
6 4 End point (min) Residual
End point (min) Actual
20
15
10
5
0 -2 -4 -6
5
10
15
20
End point (min) Predicted
24
2
25
5
10
15
20
End point (min) Predicted
25
Results 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 25
P = 0.0002
P = 0.002 P = 0.0331
Blender Speed
Humidity
Concentration
Red = L.S. Effect Mean, Yellow = (10 x Stdev) 18 16 14 12 10 8 6 4 2 0 20%
60%
Humidity 26
3
7
11
SA Concentration
12.8
20.3
Rotational Speed
Case Study Conclusions y All main effects were found to be significant y Humidity is especially important since it is difficult to control y Poor repeatability of blend time was observed among replicate blends ƒ PAT will provide a more realistic means of controlling powder mixing
y NIR spectroscopy was found to be effective in determining blend uniformity (not shown) 27
Blueprint for Optimization y Feasibility study and risk analysis provide initial starting points, and optimization constraints y DOE Flexibility to accommodate disparate data types Include first-order/two-way interactions Should be sufficient to estimate transfer functions
y Subsequent experiments are used to “finetune” the transfer function 28
Blueprint for Optimization y DOE will reveal most effective CCP’s for process analysis y Transfer functions will allow estimation of “realistic” critical control limits y Transfer functions should include sensor data, enable model-predictive control y Process characterization should be integrated with PAT method development y Information management will be critical for continuous improvement of process control 29
Thank You!
• FOSS NIRSystems • Jim Fete, Beckman Coulter • Lamija Begic • Damir Begic • Amber Fullmer • Mohamed Ghorab • David Molseed • Katie Reardon 30
www.dcpt.duq.edu