Quality by Design (QbD) and the
Design of Experiments (DoE):
Why, How, Who
Prof Ron Kenett
[email protected]
Agenda
•
•
•
•
2
Background
Introduction to QbD – Why
Introduction to DoE – How
Case studies - Who
© 2013 KPA Ltd., All rights reserved
http://apps.pharmacy.wisc.edu/esp/prog/IsraelQBD
http://ce.pharmacy.wisc.edu/courseinfo/archive/2012Israel
3
© 2013 KPA Ltd., All rights reserved
2007
2012
Modern Industrial Statistics with R,
MINITAB and JMP, Wiley, 2013
1998
2000
2004
Part IV: Design and Analysis of Experiments
11. Classical Design and Analysis of Experiments
12. Quality by Design
13. Computer Experiments
1998
4
© 2013 KPA Ltd., All rights reserved
Agenda
•
•
•
•
5
Background
Introduction to QbD – Why
Introduction to DoE – How
Case studies - Who
© 2013 KPA Ltd., All rights reserved
2004
6
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Food and Drug Administration, Challenge
and opportunity on the critical path to new
medical products, March 2004.
7
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Quality by Design (QbD)
Product
Understanding
Design
Space
Quality
by
Design
Process
Understanding
8
Moheb Nasr, Pharmaceutical Quality for the 21st
Century, 2nd QbD Conference; Jerusalem, 5-6 May 2010
Target
Product
Profile
Control
Strategy
Quality by Design - A 4 Stage Process
Design
Intent
The Active Pharmaceutical Ingredient chemical and physical
characteristics and Drug Product performance targets are
identified for the commercial product.
Design
Selection
The API manufacturing process and the DP formulation and
manufacturing process are selected to achieve the Design
Intent for the commercial product.
Control
Definition
The largest contributors to Critical Quality Attributes
variability are established and controls defined to ensure
process performance expectations are met.
Control
Verification
9
The performance of the API and DP processes in
manufacturing are measured to verify that the controls are
effective and the product performance acceptable.
© 2013 KPA Ltd., All rights reserved
Inputs
(Process Parameters
and Material Attributes)
Design
Intent
Desired
Outputs
(Drug Product CQAs)
Experiments
Analysis
Process Understanding
ie how the inputs effect
the outputs
Design
Selection
Risk Evaluation
(FMEA)
Control
Definition
Control Actions
and
Analytical Monitoring Strategy
Control
Verification
Measure Performance
10
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Quality by Design
Quality by Design
for Clinical Investigations
Quality by Design
for Clinical Practice
Process Design Intent
Process Design Selection
Quality by Design
for Analytical Methods
Method Design Intent
(Method Performance Requirements)
Process Control Definition
(Control Strategy)
Process Control Verification
Method Design Selection
(Method/Technology Development)
Method Control Definition
(Risk Assessment & Design Space Definition)
Quality by Design
for the Process and Product
Method Control Strategy
11
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Design Space
• Multidimensional combinations of the product
characteristics
• Interactions of inputs variables
• Interactions of process parameters
• Changes within the design space are not
considered a regulatory change
• QbD information and conclusions need to be shared
with the FDA – Do and Tell
12
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Design Space
Unexplored Space
Knowledge Space
Range per our partial experience
Traditional DOE, Mfg experience,
process understanding, multiproduct precedent
Design Space
Proven Acceptable Range
Control Space
Normal Acceptable Range
13
© 2013 KPA Ltd., All rights reserved
Agenda
•
•
•
•
14
Background
Introduction to QbD – Why
Introduction to DoE – How
Case studies - Who
© 2013 KPA Ltd., All rights reserved
Experimental Design Strategy
Scoping
Screening
Optimizing
Robustness
Initial
assessment
Fractional
designs
Response
surfaces
Robust
designs
Process knowledge
15
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Process
Confidence
A Serious Problem...
I want my
car to go
fast … like
that one!
16
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What Factors Affect the Speed?
Yes
Fast
Air Holes
No
Slow
Shape
17
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Effect of Air Holes
Yes
Slow
Fast
Air Holes
No
Slow
Shape
18
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Effect of Air Shape
Yes
Slow
Fast
Slow
Slow
Air Holes
No
Shape
19
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Designed Experiments
Male
Female
20
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Interaction
Designed Experiments
21
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One Factor at a Time
OFAT
Experiment #1: Study Effects of Reaction Time on Yield
(Reaction Temperature held fixed at 225o C)
22
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One Factor at a Time
OFAT
Experiment #2: Study Effects of Reaction Temperature on Yield
(Reaction Time held fixed at 130 minutes)
23
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One Factor at a Time
T=130 m, T = 225 C
24
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DOE
One Factor at a Time
25
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Reaching the top
DoE
26
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Regression Model and the associated
Response Surface
yˆ  35.5  10.5 x1  5.5 x2
8 x1 x2
27
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The Effect of Interaction on the
Response Surface
yˆ  35.5  10.5 x1  5.5 x2
8 x1 x2
28
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The Path of Steepest Ascent
DoE
Design of
Experiments
29
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‫‪A DoE Checklist‬‬
‫ב מ ג ר צ מ ס ש‬
‫מה הבעיה הנחקרת ?‬
‫מה המשתנה הכמותי המאפיין את הבעיה הנחקרת ? ‪Response‬‬
‫מהם הגורמים הניתנים לשינוי במסגרת הניסוי ? ‪Factors‬‬
‫מהן רמות הגורמים המשתתפים בניסוי ? ‪Levels‬‬
‫מהם הצרופים הקובעים את מערך הניסוי ? ‪Experimental Array‬‬
‫מה מספר החזרות שיתבצעו בכל נקודת ניסוי ? ‪Replicates‬‬
‫מהו סדר או פרוטוקול הניסוי ? ‪Order‬‬
‫מהי שיטות איסוף וניתוח הנתונים מהניסוי ?‬
‫‪© 2013 KPA Ltd., All rights reserved‬‬
‫‪30‬‬
Factors and Levels
31
Formulation
Temperature
(°C)
Emulsion
creation time
(min)
Cooling time
(min)
D078
60
2
30
D081
67.5
3.5
105
D082
75
2
180
D077
75
5
30
D080
60
5
180
D079
60
2
180
D075
75
2
30
D084
67.5
3.5
105
D083
75
5
180
D076
60
5
30
© 2013 KPA Ltd., All rights reserved
Design Space
Contour Plot of Assay, Viscosity, PH, In-Vitro, ...
5.0
Hold Values
Cooling Time 30
Blending Time
4.5
4.0
3.5
3.0
2.5
2.0
60
32
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62
64
66
68
TEMP
70
72
74
Agenda
•
•
•
•
Background
Introduction to QbD – Why
Introduction to DoE – How
Case studies - Who
ACE
Foam
HPLC
33
© 2013 KPA Ltd., All rights reserved
http://www.pharmaqbd.com/files/articles/QBD_ACE_Case_History.pdf
http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/HowDrugsareDev
elopedandApproved/ApprovalApplications/AbbreviatedNewDrugApplicationANDAGeneri
cs/UCM304305.pdf
ACE
34
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Target Product Profile of ACE
35
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ACE
DOE Study – API and Magnesium stearate
(Lubricant) Interaction Study
ACE
Responses:
• Tablet hardness
• Dissolution average at 30 min.
• Tablet weight uniformity
Factors:
• Acetriptan particle size D90:
10, 25 & 40 μm
• Lubricant (Magnesium Stearate) level: 1, 1.5 & 2%
36
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Contour Plot of Dissolution at a Set Target
Tablet Hardness of 12kP
(Dissolution Acceptance Criteria > 80%(
37
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ACE
ACE
Summary – Formulation Components Studies
38
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Manufacturing Process Development
39
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ACE
Risk Assessment – Unit Operations
Variables and unit Operations
CQA’s
Formulation
Composition
Blending I
Lubrication
Compression
Appearance
Low
Low
Low
Low
High
High
Identity
Low
Low
Low
Low
Low
Low
Assay
Low
Low
Low
Low
Low
High
Impurities
High
Low
Low
Low
Low
Low
Content
Uniformity
High
High
High
High
Low
High
Dissolution
High
Low
High
High
High
High
40
© 2013 KPA Ltd., All rights reserved
Roller
Compression
Milling
Process Optimization –
Blending Unit Operation
ACE
Response:
NIR: Near-infrared spectroscopy – to test the uniformity of
the blending.
Factors:
•
Range of Humidity:
20-70%RH
•
Acetriptan Particle Size: D90 10-40μm
•
MCC Particle Size:
D50 30-90μm
41
© 2013 KPA Ltd., All rights reserved
NIR For the DOE Study
42
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ACE
Process Optimization –
Roller Compaction and Milling
Variables and unit Operations
CQA’s
43
Roller Compression
Milling
Appearance
Low
Low
Identity
Low
Low
Assay
Low
Low
Impurities
Low
Low
Content
Uniformity
High
High
Dissolution
High
High
© 2013 KPA Ltd., All rights reserved
ACE
Process Optimization –
Roller Compaction and Milling
Responses
Final Product Attributes:
• Tablet Weight
• Tablet Hardness
• Tablet Friability
• Tablet Disintegration Time
44
© 2013 KPA Ltd., All rights reserved
ACE
Process Optimization –
Roller Compaction and Milling
ACE
Factors Investigated
Ingredients:
1. Acetriptan Particle Size (10 and 40μm)
2. Magnesium Stearate Level (1.25 and 2.25% w/w)
3. Croscarmellose Sodium Level (3 and 4% w/w)
Process:
1. Roller Pressure (50 and 150 bar)
2. Mill Screen Size (0.039 and 0.062 inches)
3. Mill Speed (600 and 1200 rpm)
45
© 2013 KPA Ltd., All rights reserved
Significant Factors for Final
Product Attributes
Response:
Dissolution
A: API level
B: MgSt level
C: CCS level
D: Roller pressure
E: Mill screen size
F: Mill speed
46
© 2013 KPA Ltd., All rights reserved
ACE
DOE 2 - Roller Compaction
ACE
Factors:
• Acetriptan PS:
d90 10-40μm
• Magnesium stearate level: 1-2% intragranular
• Roller Pressure:
50-150 bar
47
© 2013 KPA Ltd., All rights reserved
Contour Plot For API particle size and Roller
Pressure vs. Tablet Dissolution (at 1% Mg. St.
Level)
40.00
90.53
32.50
API PS
25.00
17.50
101.58
10.00
50.00
75.00
100.00
125.00
Roller Pressure
48
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150.00
ACE
Design Space for ACE tablets
49
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ACE
Foam
An Oily Foam Drug Product Example
The goal is to design a process suitable for
routine commercial manufacturing that
consistently delivers a product that meets
its quality attributes.
50
© 2013 KPA Ltd., All rights reserved
The Product
•
•
•
•
•
•
•
51
2% of Active Material
Oily Foam
Dip Tube (?)
Propellant
Valve
Actuator
Can
© 2013 KPA Ltd., All rights reserved
Foam
Target Product Profile
Foam
Target at time 0
Target at
end of shelf life
Criticality
Foam
Foam
NA
2%
2%
NA
White foam
White foam
Critical
95-105%
90-110%
Critical
Not More Than…
As per USP 33 monograph
Critical
Water
NMT
-
Critical
Safety
NLT other products in
the market
NLT other products in
the market
Critical
Microbiology
Meets USP criteria
Meets USP criteria
Critical
Delivery amount
Meets USP criteria
Meets USP criteria
Critical
NLT 24 months
NLT 24 months
Critical
Quality Attribute
Dosage form
Potency
Appearance
Assay
Impurities
Shelf Life
52
© 2013 KPA Ltd., All rights reserved
Foam
Prior Knowledge
• API is sensitive to extensive heat. (24 hr. at 800C are
equivalent to 1 month at 400C.)
• API is sensitive to water.
• Manufacturing and Packaging Processes includes heating.
• Manufacturing Process: API is exposed to 600C.
• Packaging Process: Above 550C.
• Bulk should be visually clear.
53
© 2013 KPA Ltd., All rights reserved
Questions that are looking for answers:
Foam
Manufacturing:
1. Specify manufacturing equipment
2. Filtering the bulk; is it essential?
3. Is there a need to cool down the bulk in the storage container?
Packaging:
1) Immediate filling vs. bulk reheating for packaging, any differences?
2) Epoxy containers vs. PAM containers, any differences?
3) Propellant type and concentration – Determine levels
4) Dip tube – Can we use them?
5) Determine the limits of filling specification for the bulk and propellant.
6) Leakage tests – Do we pass them?
7) Vacuum effect: Design on target formulation
54
© 2013 KPA Ltd., All rights reserved
Foam
Manufacturing and Packaging Process
Inactive ingredients
Heating to
80°C
Cooling to
60°C
Active ingredient
Dissolution of active
ingredient, for 30
minutes at 60°C
Packaging at minimum
temperature of 55°C
55
© 2013 KPA Ltd., All rights reserved
Foam
1. What is the preferable packaging temperature?
Aerosol Performance
Data Means
Deliverable Amount (gram)
Deliverable Amount:
Deliverable amount vs. Bulk Temperature
50.5
50
50.0
49.5
49.0
48.5
48.0
47.5
47.0
58
68
bulk temp
56
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Foam
Packaging Experiment: Factors and Levels
Packaging Process Parameters:
1. Packaging Temp.:
2. Vacuum:
3. Bulk Filling Range:
4. Gas Filling Range:
58
or 680C
-0.2 or -0.5bar
50.5 - 56.0g
3.5 6.5g
Packaging System Parameters:
1.
2.
57
Can:
Dip tube:
© 2013 KPA Ltd., All rights reserved
Epoxyphenolic or PAM
With or without
Foam
2. What is the effect of the factors on pressure?
Main Effects Plot for Pressure
Data Means
Gas amount
vacuum
2.7
Boxplot of Pressure
2.6
58
Mean
2.5
68
4.0
4
5
3.5
-0.2
Pressure
bulk temp
-0.5
2.7
3.0
2.5
2.6
2.0
2.5
1.5
58
68
1.0
Panel variable: bulk temp
When packaging at 580C the
pressure variability between
different cans is higher
58
© 2013 KPA Ltd., All rights reserved
Foam
3. What is the preferable gas filling range?
Appearance:
√
X
5.0-6.5g
Main Effects Plot for Deliverable amount
Deliverable Amount:
Data Means
51.5
Mean
51.0
50.5
50.0
50
49.5
49.0
4.5
59
© 2013 KPA Ltd., All rights reserved
5.0
5.5
Gas amount
6.0
6.5
Packaging Ranges and Design Space
Parameter
Packaging Temperature
Immediate/ Reheating for
packaging
60
Approved Range/ Design Space
65-700C
Immediate/ Reheating
Gas Filling Range
5.0g-6.5g
Bulk Filling Range
51.0-54.0g
Dip Tube
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Foam
Better without dip-tube
HPLC
Analytic Methods Development
DryLab simplifies and speeds the
process of developing good
chromatographic separations or
methods by allowing to model
changes in separation conditions
using a personal computer.
61
© 2013 KPA Ltd., All rights reserved
HPLC
Chromatogram with true signal at 4.9 min.
0
,
0
3
0
4,87
0
,
0
2
5
0
,
0
2
0
0
,
0
0
0
28,64
11,1626,03
0
,
0
0
5
8,13
Intensity(AU)
0
,
0
1
0
16,91,Propionatedefluticasone
0
,
0
1
5
0
,
0
0
5
0
,
0
1
0
0
5
1
0
1
5
2
0
R
e
t
e
n
t
i
o
nT
i
m
e(
m
i
n
)
62
© 2013 KPA Ltd., All rights reserved
2
5
3
0
3
5
4
0
HPLC
Chromatogram with false negative signal at 4.9 min.
0,030
0,025
0,015
1122,,0575,Impurete3
13,89
0,005
8,19
Intensity(AU)
0,010
0,000
16,95,Propionatedefluticasone
18,65, SS-M‫י‬thyl
0,020
-0,005
-0,010
0
5
10
15
20
RetentionTime(min)
63
© 2013 KPA Ltd., All rights reserved
25
30
35
40
Chromatogram with false positive
signal at 11 min.
10
HPLC
10
PDA-246nm
T=0 Specificity solution ( 3IMQMN0E002 +~0.15% of impurities)
04A04.dat
5
mAU
17.465
5
8.035
10.470
mAU
Retention Time
0
0
0
10
20
30
40
50
60
Minutes
10
10
UV1000-246nm
System specificity (3IMQMN0E002+~0.15%imp's)
5
7.002
0
0
10
20
30
Minutes
64
© 2013 KPA Ltd., All rights reserved
40
50
60
mAU
14.908
5
9.350
11.008
mAU
Retention Time
0
Typical
chromatogram of
related
substances.
Chromatogram of
related substances
with False-positive
Signal (peak at 11
min).
Simulated Chromatograms
65
© 2013 KPA Ltd., All rights reserved
HPLC
Development of
Analytical Methods
Run
Order
nHexane/EtOH
[v/v]
DEA[ml]
T[°C]
1
4
2ml
15°C
2
5.7
0.5ml
15°C
3
5.7
2ml
40°C
4
4
0.5ml
40°C
Bates, R., Kenett R., Steinberg D.
and Wynn, H. (2004), Robust Design
using Computer Experiments, The
13-th Conference on Mathematics for
Industry 21-25 June 2004 Eindhoven,
The Netherlands.
66
© 2013 KPA Ltd., All rights reserved
HPLC
HPLC
Norm.
16.892
VWD1 A, Wav elength=262 nm (040413-1\004-0401.D)
12.683
Development of
Analytical Methods
2500
2000
1
1500
1000
500
0
0
5
10
15
20
25
30
35
min
2
4
DEA[ml]
T[°C]
Norm.
2500
2000
2ml
15°C
2
1500
1000
500
5.7
0.5ml
15°C
0
0
5.7
2ml
40°C
4
4
0.5ml
40°C
10
15
20
25
30
35
40
45
min
VWD1 A, Wav elength=262 nm (040414-1\004-0401.D)
15.347
3
5
Norm.
20.963
1
nHexane/EtOH
[v/v]
26.121
Run
Order
19.112
VWD1 A, Wav elength=262 nm (040413-1\004-0801.D)
2500
2000
1500
3
1000
500
0
0
5
10
15
20
25
30
min
Norm.
13.803
VWD1 A, Wav elength=262 nm (040414-2\004-0401.D)
10.434
Bates, R., Kenett R., Steinberg D.
and Wynn, H. (2004), Robust Design
using Computer Experiments, The
13-th Conference on Mathematics for
Industry 21-25 June 2004 Eindhoven,
The Netherlands.
2500
2000
4
1500
1000
500
0
0
67
© 2013 KPA Ltd., All rights reserved
5
10
15
20
25
30
35
min
HPLC
Simulation Experiments Analysis
68
#
Mobile Phase
1
800ml n-Hexane
200ml EtOH
2ml
Diethylamine(DEA)
2
850ml n-Hexane
150ml EtOH
0.5ml
Diethylamine(DEA)
3
850ml n-Hexane
150ml EtOH
2ml
Diethylamine(DEA)
4
800ml n-Hexane
200ml EtOH
0.5ml
Diethylamine(DEA)
© 2013 KPA Ltd., All rights reserved
T[°C]
RT[min]
Isomer #1
RT[min]
Isomer #2
15° C
RT =12.683
Tailing=3.0
Plates=2239
RT =16.892
Tailing=2.8
Plates=2758
15° C
RT =19.113
Tailing=3.6
Plates=2938
RT =26.122
Tailing=3.3
Plates=3709
40°C
RT =15.347
Tailing=3.0
Plates=2867
RT =20.963
Tailing=3.4
Plates=3576
40°C
RT =10.434
Tailing=3.3
Plates=2236
RT =13.803
Tailing=3.7
Plates=2459
Resolution
3.56
4.48
4.40
1.32
HPLC
Simulation Experiments Analysis
69
© 2013 KPA Ltd., All rights reserved
‫ב מ ג ר צ מ ס ש‬