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Role of Excipient Variability in
QbD Drug Product Development
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Dr. Joseph Kushner, IV
Pfizer, Inc.
1
Co-Contributors
Beth Langdon
Jon Hiller
Glenn Carlson
Fasheng Li
Daniel Song
Gautam Ranade
–
–
–
–
Ian Hicks
Kam Agarwal
Lalji Kathiria
Anil Kane
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–
–
–
–
–
–
• Patheon
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• Pfizer
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Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
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• Closing thoughts: Interaction with regulatory
bodies
3
Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
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• Closing thoughts: Interaction with regulatory
bodies
4
Quality-by-Design Paradigm
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• Change from single set of process conditions to a
design space (range of process conditions) to achieve
acceptable product output.
• Simple example:
Pre-QbD
Time: 15 min
Speed: 12 rpm
QbD
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Blending
Time: 10-20 min
Speed: 6-18 rpm
Compression: 10 kN
Tableting
Compression: 5-15 kN
Spray Rate: 150 g/min
Pan Load: 70%
Film Coating
Spray Rate: 100-200 g/min
Pan Load: 50-90%
5
Pictorial of Design Space
Unknown Space
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Knowledge Space
Tested, but fails quality
specifications
Design Space
Demonstrated quality
product
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Normal Operating
Domain
Note: Developing a design space is optional
6
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ICH Q8(R2)
Pharmaceutical Development
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Developing Robust Products
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API – Target
Attribute Profile
API
Properties
Excipients –
Vendor Specs
Excipient
Properties
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Process
Parameters
Drug Product Quality Target Product Profile
(ensures consistent delivery of safe and efficacious drug products to the patient)
How can we incorporate excipient understanding into robust drug product design?
8
Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
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• Closing thoughts: Interaction with regulatory
bodies
9
One Approach to Incorporating Excipient Variability into QbD
Drug Product Development
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Control
Strategy
Goal: Ensure Product Robustness
Confirm Criticalities (if necessary)
Work with suppliers for materials
Goal: Identify Potential CMAs
Prior product knowledge
Literature review
Goal: Manage variability to consistently
supply safe, efficacious products
to patients and providers
DoEs on
Critical Attributes
Risk
Assessment
Gain
Development
Experience
Goal: Reduce Potential CMAs
Increase experience domain
Confirm excipient suitability
Rational lot selection
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Understand Sources of Variability in Excipients
Goal: Select Excipients and Composition
Understand specification range vs. actual variability
Evaluate site-to-site, annual/seasonal, and raw material sources of variability
10
Case Study #1 Utilizing Vendor
Generated Data
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• QbD filings: Determine domain where proven acceptable product has
been manufactured.
– Applies to both process conditions and raw material variability.
• Need to understand raw material (excipient) variability.
– Site differences, year-to-year variation, seasonal variation
• Following examples utilize quantitative datasets (ex: Certificate of Analysis
data).
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– Readily available from vendor.
– Requires no additional, in-house material characterization.
– May leverage multivariate methods (i.e. PCA) to simplify data analysis.
Joseph Kushner IV. (2013) Utilizing quantitative certificate of analysis data to assess the amount of excipient
lot-to-lot variability sampled during drug product development. Pharm Dev Tech, 18(2):333-342.
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Example Certificates of Analysis
Utilize quantitative material physico-chemical properties.
12
Normal Variability vs. Specs
35
Mag Stearate - Stearic Acid % (Spec not less than 40%)
Mag Stearate - Tapped Density (Spec: 0.18-0.33 g/cc)
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80
30
70
Typical Property Variability
Distribution of values
observed in 154 lots from
2004-2012.
40
30
20
20
15
10
5
10
0
0
40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Bin
Normal lot-to-lot variability is small
compared to specification range.
Bin
Normal lot-to-lot variability is comparable to
specification range.
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Frequency
50
Frequency
25
60
Knowledge of extent of normal variability compared to specification range can aid
future planning to ensure drug product robustness.
13
Understanding Excipient Variability
Site
CorkA
Site
Newark
B
4
2
0
-2
•
-4
-3
-2
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-5
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Principal Component 2
6
-1
0
1
2
3
4
5
6
7
Principal Component 1
Example: Avicel PH102
– PC1 shows site differences
– Development Strategy: Source from both sites throughout development to build in robustness
14
Understanding Excipient Variability
Principal Component 2
3
2
1
0
-1
-2
-3
-4
2002
2005
2008
2003
2006
2009
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2001
2004
2007
-3
-2
-1
0
1
2
3
4
Principal Component 1
SIMCA-P 11 - 4/13/2010 11:10:19 AM
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• Example: Mg St (HyQual)
– PC1 and PC2 show year-to-year differences (particle size, density, chemical
composition)
– Uncertainty if material used in development will be similar to future material used in
commercial mfg
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Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
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• Closing thoughts: Interaction with regulatory
bodies
16
Case Study #2 – Risk and Experimental
Approach
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• Dry granulated, immediate release tablet
formulation
• Formulation components:
API
Microcrystalline Cellulose (Avicel PH102)
Spray-dried Lactose (Fast Flo 316)
Sodium Starch Glycolate (Glycolys, Explotab)
Magnesium Stearate (HyQual)
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–
–
–
–
–
Joseph Kushner IV, Beth A. Langdon, Jon I. Hillier, Glenn T. Carson. (2011) Examining the impact of excipient
material property variation on drug product quality attributes: A quality-by-design study for a roller
compacted, immediate release tablet. J Pharm Sci, 100:2222-2239.
17
How Many “Knobs” Do Excipients Have?
Spray-Dried Lactose
Disintegrant
Loss on Drying
Loss on Drying
Loss on Drying
Loss on Drying
Loose Bulk Density
Bulk Density
Loose Bulk Density
Specific Surface Area
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Microcrystalline Cellulose
Magnesium Stearate
Tapped Density
Degree Substitution
Stearic Acid Content
Residue on Ignition
pH
Stearic + Palmitic Acid Content
Water Content
Settling Volume
Mg Assay
Residue on Ignition
Absorbance 210-220 nm
Ash
% through 325-mesh
Water Soluble Substances
Absorbance 270-300 nm
Water Soluble Substances
Bulk density
Ether Soluble Substances
Acid/Alkaline
Ether Soluble Substances
Tapped density
Specific Rotation
Sodium Glycolate
Acid Value
Wt% on 60-mesh
Sodium Chloride
D50
Wt% on 140-mesh
Purity
D90
Wt% on 200-mesh
Humidity
Degree of Polymerization
pH
Conductivity
Wt% on 60-mesh
Wt% on 200-mesh
D10
D50
D90
Wt% on 200-mesh
Wt% on 325-mesh
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D10
Vendor CoA data lists over 50 potential
factors!
D50
D90
18
Risk-based Analysis: Literature Review
Spray-Dried Lactose
Sodium Starch
Glycolate
Excipient Property
Affected Product Attribute
Landin et al.16
Lignin (%)
Dissolution
Suzuki and Nakagami 17
% Crystallinity, Particle Size
Moisture Content, Yield Pressure,
Tablet Hardness, Dissolution
Kothari et al. 18
% Crystallinity
Moisture Content
Vromans et al. 19
Particle size, % Crystallinity
Tablet Hardness
Sebhatu et al. 20
Moisture Content
Tablet Hardness
Rudnic et al. 21
Degree of Cross-linking,
Degree of Substitution
Water Uptake, Disintegration,
Dissolution
Bolhuis et al. 22
Degree of Substitution
Disintegration
Bolhuis et al. 23
Raw Material Source,
Degree of Cross-linking
Water Uptake, Disintegration
Dansereau and Peck 24
Particle Size, Specific Surface Area
Tablet Tensile Strength, Friability
Leinonen et al. 25
Specific Surface Area,
Moisture Content
Ejection Work
Barra and Somma 26
Particle Size
Tablet Hardness, Ejection Work
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Magnesium Stearate
Study
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Excipient
Microcrystalline
Cellulose
Rao et al. 27
Agglomerates, Particle Size,
Specific Surface Area, Polymorph
Particle size, specific surface area, and polymorph were identified as the highest risk
material properties for further study.
19
Benefits of Risk-Based Analysis
Spray-Dried Lactose
Disintegrant
Loss on Drying
Loss on Drying
Loss on Drying
Loss on Drying
Loose Bulk Density
Bulk Density
Loose Bulk Density
Specific Surface Area
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Microcrystalline Cellulose
Magnesium Stearate
Tapped Density
Degree Substitution
Stearic Acid Content
Residue on Ignition
pH
Stearic + Palmitic Acid Content
Water Content
Settling Volume
Mg Assay
Residue on Ignition
Absorbance 210-220 nm
Ash
% through 325-mesh
Water Soluble Substances
Absorbance 270-300 nm
Water Soluble Substances
Bulk density
Ether Soluble Substances
Acid/Alkaline
Ether Soluble Substances
Tapped density
Specific Rotation
Sodium Glycolate
Acid Value
Wt% on 60-mesh
Sodium Chloride
D50
Wt% on 140-mesh
Purity
D90
Wt% on 200-mesh
Humidity
Polymorph
Degree of Polymerization
pH
Conductivity
Wt% on 60-mesh
Wt% on 200-mesh
D10
D50
D90
Wt% on 200-mesh
Wt% on 325-mesh
D10
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52 factors down to 5
Omitted 40+ possible factors…
D50
D90
20
Executed Experimental Plan
Microcrystalline
Cellulose
Spray-Dried Lactose
Batch Number
Particle Size
Particle Size
Polymorph
Particle Size
Specific Surface Area
1
High
Low
Dihydrate
Low
Low
2
High
High
Monohydrate
High
Low
3
High
Low
Monohydrate
Low
High
4
High
Low
Monohydrate
High
Low
5
High
High
Monohydrate
Low
High
6
High
High
Dihydrate
Low
Low
7
High
Low
Dihydrate
High
High
8
High
High
Dihydrate
High
High
9 (Control 1)
High
Medium
Monohydrate
Medium
Medium
10
Low
Low
Monohydrate
High
Low
11
Low
High
Monohydrate
Low
High
K-1
Low
Low
Monohydrate
High
Low
K-2 (Control 2)
Medium
Medium
Monohydrate
Medium
Medium
K-3
High
High
Monohydrate
Low
High
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Magnesium Stearate Properties
Batches 1-9 were used in the initial screening analysis, Batches 4, 5, and 9-11 in the second screening analysis, and Batches K1 through K-3 were used in the kilo-scale confirmation run
Executed experimental plan a result of challenges in obtaining excipients with
suitable properties – not an issue for process parameter experiments.
21
Executed Experimental Plan
Spray-Dried Lactosea
d50 (m)
94
126
94
94
126
126
94
126
116
94
126
94
116
126
Magnesium Stearateb
Polymorph
Dihydrate
Monohydrate
Monohydrate
Monohydrate
Monohydrate
Dihydrate
Dihydrate
Dihydrate
Monohydrate
Monohydrate
Monohydrate
Monohydrate
Monohydrate
Monohydrate
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Batch Number
1
2
3
4
5
6
7
8
9 (control 1)
10
11
K-1
K-2 (control 2)
K-3
Microcrystalline
Cellulosea
d50 (m)
127
127
127
127
127
127
127
127
127
95
95
108
121
131
d50 (m)
12.8
24.0
4.9
24.0
4.9
12.8
15.6
15.6
10.6
24.0
4.9
24.0
10.6
4.9
SSA (m2/g)
4.4
6.3
10.7
6.3
10.7
4.4
6.0
6.0
6.2
6.3
10.7
6.3
6.2
10.7
a – Values obtained from laser diffraction measurements.
b – Values obtained from vendor’s Certificate of Analysis.
22
Results: Ribbon Attributes
7
Sampled excipient particle size
variation yield a 0.25 MPa over
the tensile strength vs. solid
fraction profiles.
open diamonds ( ) – high PS MCC; low PS
lactose; high PS, low SSA MgSt
open circles (○) – low PS MCC, highPS lactose;
low PS, high SSA MgSt
open triangles ( ) – high PS MCC and lactose;
low PS, high SSA MgSt
closed circles (●) – control batch (high PS MCC;
medium PS lactose; medium PS and SSA MgSt)
Tensile Strength (MPa)
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open squares (□) – low PS MCC and lactose;
high PS, low SSA MgSt
6
5
4
3
2
1
0
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0.55
0.60
0.65
0.70
0.75
0.80
0.85
Solid Fraction
Conclusion: Excipient variability is not a critical material attribute for ribbon
properties.
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Results: Hardness-Compression Profiles
150 mgW Tablets
a 200
b 300
Hardness (N)
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250
160
120
80
40
Hardness 
0
MgSt PS
Filler PS
200
150
100
50
0
0
3
6
9
12
Compression (kN)
15
18
0
5
10
15
20
Compression (kN)
25
30
open squares (□) – low PS MCC and lactose; high PS, low SSA MgSt, open diamonds ( ) – high PS MCC; low PS lactose; high PS, low SSA MgSt,
open circles (○) – low PS MCC, highPS lactose; low PS, high SSA MgSt, open triangles ( ) – high PS MCC and lactose; low PS, high SSA MgSt,
closed circles (●) – control batch (high PS MCC; medium PS lactose; medium PS and SSA MgSt)
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Hardness (N)
300 mgW Tablets
Interaction between particle size of excipients appear to drive the observed minor
differences in the hardness-compression profiles.
Conclusion: Excipient variability does impact tablet strength, but not critically.
24
Example Results:
Compression Force vs Disintegration Time
480
420
 Disintegration is a potential CQA
 Excipient variability had no impact
on disintegration times
Disintegration Time (sec)
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Overall Conclusion
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Drug product formulation and
manufacturing process were robust
to variation in the excipient
properties studied
25
360
300
240
180
120
60
0
0
3
6
9
12
15
18
Compression (kN)
Legend
MCC
PSD
Lactose
PSD
Mg Stearate
PSD/SSA
Open squares (□)
Low
Low
High/Low
Open triangles ( )
High
High
Low/High
Control - Closed circles (●)
High
Medium
Medium
Managing and Evaluating Available Data
Spray-Dried Lactose
Loss on Drying
Loss on Drying
Loss on Drying
Loss on Drying
Loose Bulk Density
Bulk Density
Loose Bulk Density
Specific Surface Area
Degree of Polymerization
Disintegrant
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Microcrystalline Cellulose
Magnesium Stearate
Tapped Density
Degree Substitution
Stearic Acid Content
Residue on Ignition
pH
Stearic + Palmitic Acid Content
Water Content
Settling Volume
Mg Assay
Residue on Ignition
Absorbance 210-220 nm
Ash
% through 325-mesh
Water Soluble Substances
Absorbance 270-300 nm
Water Soluble Substances
Bulk density
Ether Soluble Substances
Acid/Alkaline
Ether Soluble Substances
Tapped density
Specific Rotation
Sodium Glycolate
Acid Value
Wt% on 60-mesh
Sodium Chloride
D50
Wt% on 140-mesh
Purity
D90
Wt% on 200-mesh
Humidity
Polymorph
pH
Conductivity
Wt% on 60-mesh
Wt% on 200-mesh
D10
D50
D90
Wt% on 200-mesh
Wt% on 325-mesh
D10
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52 factors down to 5
Omitted 40+ possible factors…
… but are they truly “unknowns”?
D50
D90
26
Quantifying Domain of Prior Experience
•
Rationale: Evaluate the amount and domain of excipient variability sampled during
drug product development.
Example: Avicel PH102
–
3
Drug product development
• Development, Process Understanding
• Ph3 Clinical batches
• ICH batches
– 15 lots used
• 4 Newark lots
• 11 Cork lots
2
P rincipal Component 2
•
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4
1
0
-1
-2
Domain of Prior Experience
–
–
–
–
-3
Evaluated with MATLAB scripts
-4
-3
-2
-1
0
1
2
Principal Component 1
All PCs: 58% of all lots
Only PCs 1 and 2: 79% of all lots
Communication tool with commercial sites and regulatory agencies
3
4
5
6
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•
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Univariate Approach
80
70
Frequency
60
50
40
30
20
10
0
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Mag Stearate - Stearic Acid % (Spec not less than 40%)
Domain of Prior
Experience
Prior demonstration of
quality product
Typical Property
Variability
Distribution of values
observed in 154 lots from
2004-2012.
40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
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Bin
Prior experience covers ~15% of the specification range, but 97% of the typical variability
of Stearic Acid % observed in Mag Stearate.
28
How representative are the batches
used in development? (Multivariate)
3.0
8
(Residue on Ignition)
4
2
0
-2
1.0
0.0
-1.0
-2.0
-3.0
-4.0
-4
-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
-6
-4
-2
0
2
4
Principal Component 1
6
8
10
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(Wt% on 200-mesh & 140-mesh, Bulk & Tapped Density)
82% of lot-to-lot variability captured for only
Principal Components 1 and 2 (physical
properties).
29
Principal Component 6
2.0
6
Principal Component 2
(Wt% on 60-mesh, Water Content)
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Filled blue circles (●) represent the 13 batches of spray-dried lactose used in development of an IR tablet formulation;
earliest development batch from 2006 – all 13 batches made good quality tablets
12
Principal Component 4
SIMCA-P+ 12 - 2010-12-20 13:20:05 (UTC-5)
(Loss on Drying, Acidity/Alkalinity)
28% of lot-to-lot variability for all Principal Components
(all physico-chemical properties), due to poor Principal
Components 4 and 6 coverage
Expanding Domain of Experience
1 - Rational Lot Selection
Rationale: Select lots from vendor inventory that expand domain of prior experience during development
Example: Avicel PH102
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Principal Component 5
5
4
3
7545C
2 Domain of Prior
Experienc e
1
0
-1
-2
7143C
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7513C
-5
-4
-3
-2
-1
0
1
2
3
Principal Component 3
SIMCA-P 11 - 4 /14/2010 10:17: 54 AM
Lot 7143C most improves domain of prior experience.
30
Expanding Domain of Experience
2 - Alternate Vendors
Rationale: Evaluate alternative vendors of same excipient for similarities/ differences in CoA data
Example: Mg St - Mallinckrodt vs. 1 lot of Peter Greven Mg St
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5
3
2
1
0
-1
-2
-4
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Principal Component 5
4
-3
-2
-1
0
1
2
3
4
5
6
7
Principal Component 1
SIMCA-P 11 - 8/27/2010 11:54:15 AM
31
Expanding Prior Experience:
3 - Using Alternate Grades
4.0
3.0
2.0
0.0
-1.0
-2.0
-3.0
-4.0
-6
-5
-4
-3
-2
-1
0
1
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t[2]
1.0
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PH101
PH102
PH200
2
3
4
5
6
7
8
9
Domain of Prior Experience
Prior demonstration of
quality product
Normal Operating Range
Ideal to be able to use all
PH102 lots
10
t[1]
SIMCA-P+ 12 - 2011-06-28 04:29:51 (UTC-5)
Examination of smaller PS (PH101) and larger PS (PH200) in DP development broadens
knowledge space and may enable all lots of PH102 to be within design space for PS.
32
Impact on Other Components
3.0
2.0
0.0
-1.0
-2.0
-3.0
-4.0
-2.5 -2.0 -1.5 -1.0 -0.5
0.0
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PC 5
1.0
PH102
PH200
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PH101
0.5
1.0
1.5
2.0
2.5
3.0
Prior Experience
Prior demonstration of
quality product
3.5
PC 4
SIMCA-P+ 12 - 2011-06-28 04:39:56 (UTC-5)
Data for other CoA parameters is intermixed for other PCs. Only a fraction of PH102 lots
are within the domain of prior experience for other parameters.
33
Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
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• Closing thoughts: Interaction with regulatory
bodies
34
How Much Impact do Excipients Have?
API
Excipient
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C. Moreton, APR (2010)
API
Excipient
Excipient
API
Process
Process
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Excipients are minor
contribution
Control strategy focuses on API
and process parameters
Process
Excipients are primary
contribution
Control strategy focuses on
excipient properties
35
Quality by Design Evaluation for an
Immediate Release Tablet Platform
Study Objectives
•
•
Factors
• API Properties
• Type: Ibuprofen and Theophylline
• Loading: 1, 5, and 25%
• Particle Size: 8 – 114 microns
• Excipients
•
•
•
•
2 parts Microcrystalline cellulose
1 part Spray-dried lactose
1% Magnesium stearate
3% Croscarmellose Sodium
Varied diluent:lube particle size ratio (3.4 – 41.6) based
on results of prior dry granulated IR tablet excipient
variability study.
• Manufacturing Method
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•
Investigate the impact of excipient lot-to-lot variability on drug product performance and
manufacturability, relative to the impact of API properties and manufacturing method
Evaluate robustness of an IR tablet platform.
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•
• Direct Compression (DC)
• Dry Granulation (DG)
Joseph Kushner IV, Beth A. Langdon, Ian Hicks, Daniel Song, Fasheng Li, Lalji Kathiria, Anil Kane, Gautam Ranade, Kam Agarwal.
(2014) A quality-by-design study for an immediate-release tablet platform: examining the relative impact of active
pharmaceutical ingredient properties, processing methods, and excipient variability on drug product quality attributes. J Pharm
36
Sci, 103:527-538.
Materials
(BASF, Ludwigshafen, Germany)
Theophylline API
(BASF, Ludwigshafen, Germany)
Microcrystalline cellulose
(FMC Biopolymer, Philadelphia, PA)
Lactose (spray dried)
(DMV Fontera, Goch, Germany)
Magnesium stearate
(Mallinckrodt, Hazelwood, MO)
Vendor Lot #
D[4,3] (µm)
Factor Level
25
38
50
Anhydrous powder
200M
PLV Micronized
Avicel 200
Avicel 102 - C5b
Avicel 102 - N3b
Avicel 102 - N4a
Avicel 101
Lactopress 250 (screened coarse)
Lactose SD11 (NZ)
Lactopress 260
Lactose SD11 (EU)
Lactopress 250 (screened fine)
Lactopress 250
Lactopress 250
Mg Stearate 434
Mg stearate KP 5712
Mg stearate KP 5712
Mg stearate KP 5712
Mg stearate VG 1726
IB1V0817
IB1V0311
IB1V1089
179921AX20
169321AX20
198721AX20
PN12824026
71138C5BC
P211823545
P212824256
P112824137
N/A
HV120027
600656
10648454
N/A
600722
600848
1207000026
1110000870
1005000629
1203000003
1203000005
29.1
42.6
55.3
8.0
37.6
113.5
244.9
136.2
126.2
101.9
62.9
159.4
148.3
137.6
125.9
77.6
124.1
122.9
19.9
12.1
11.7
10.6
5.2
LOW
MID
HIGH
LOW
MID
HIGH
1
2
3
4
5
1
2
3
4
5
N/A
N/A
1
2
3
4
5
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Ibuprofen API
Vendor Grade
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Material Name
(Vendor, location)
Examined effect of both intra-grade and inter-grade excipient particle size variability
on IR tablet product performance.
Experimental Plan
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Design: Resolution V 25-1 fractional factorial with axial and center runs
38
Methods
• Manufacturing Process
– Blending (5-kg batch)
Blend – 10 min, 12 rpm
Mill – 1000 rpm
Blend - 10 min, 12 rpm
Lube – 3 min, 12 rpm
– Granulation
• Roll force – 6 kN/cm
• Roll speed – 2 rpm
• Roll gap – 2 mm
– Tablets
– Blends
• Particle size
• Flow
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•
•
•
•
• Evaluated Attributes
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• 8-station Manesty
• 100 mg flat-faced tablets
• 50 rpm speed
– Granulation
• Ribbon solid fraction
• Particle Size
• Flow
– Tablets
• Weight, thickness, hardness,
friability
• Solid fraction, tensile strength
• Disintegration time
• Potency
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Sample Results: Blend Flow
Very poor flow (1.46-1.59) observed for high drug
load, low drug PS, and low diluent:lube PS ratio.
60% of observed variation due to API properties.
35% due to excipient particle size variation.
40
Al
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Sample Results: Granulation Flow
Granulation is satisfactory (0.7) to very good (0.95),
across all API loadings and particle sizes.
10% of observed variation due to API properties.
47% due to excipient particle size variation.
42% due to API and excipient PS interaction.
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Sample Results: Content Uniformity
Within-grade Particle Size Variation
Al
High acceptance values (AV>15) for low drug loading,
large drug PS, and high diluent:lube ratio.
Within grade variations meet AV criteria.
58% of observed variation due to API properties.
22% due to manufacturing method.
16% due to excipient particle size variation.
5% due to API load, excipient interaction.
42
Robustness Domain for IR Tablets
1
1.2
1.4
1.6
1.8
2
Granulation Flow (Carr)
0
10
20
30
40
50
Tablet Weight (mg)
Tablet Weight RSD (%)
Tablet Potency (%LC)
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Blend Flow (Hausner)
97
98
0
99
0.5
85
1
100
1.5
90
101
2
95
2.5
100
102
3
103
3.5
105
4
110
115
Acceptance Value
0
Tensile Strength (MPa)
0
1
2
3
4
5
Friability (%)
0
0.2
0.4
0.6
0.8
1
Solid Fraction
0
1
2
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Disintegration (min)
5
0.75
3
10
4
0.8
5
6
7
15
8
0.85
9
10
11
0.9
20
12
13
14
15
0.95
All batches satisfactory except for 25% loading of micronized 8 micron theophylline API.
(i.e., very poor flow led to high tablet weight RSD for 100 mg tablets)
Case Study #3 Conclusions
Excipient particle size variability had the most (~50%) impact on blend and
granulation particle size, and granulation flow.
•
API property variation dominated (~60%) blend flow, ribbon solid fraction and
tablet properties.
•
Acceptable product quality with immediate-release tablet platform achieved for
within-grade excipient particle size variations.
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•
– Some challenges observed for extreme DC formulation cases:
• Flow: High drug loading, lower API and excipients particle size
• Tablet weight uniformity: Low drug loading, larger API and excipients particle size
Control strategy implications:
–
Use of GMP procedures for formulation composition and within-grade excipients should provide
adequate product robustness for an immediate release tablet over a wide range of API loadings and
particles sizes.
Al
•
44
Outline
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• Background on QbD concepts
• Approaches for assessing excipient variability in
Drug Product Design
– Case Study 1 - using vendor CoA datasets to understand variability
– Case Study 2 – risk-based and experimental investigations of excipient
variability and drug product performance
– Case Study 3 - Quality by Design evaluation for robustness of an
immediate release tablet platform
Al
• Closing thoughts: Interaction with regulatory
bodies
45
Example Questions from Major Regulatory Agencies
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– Describe the effect of the physical properties (e.g., particle size,
density) of excipients on the particle size distribution of the
granulation, sieve cut potency, and tablet core content uniformity.
– How particle size of excipients (and other excipient properties)
affect compressibility?
Al
– Discuss if lot-to-lot variability in excipient properties (e.g. bulk
density, particle size, surface area) would have any adverse
impact on finished product quality. If there is an adverse impact,
describe your control strategy.
46
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Impact on Drug Product
CQAs (safety and
efficacy)
No Impact on Drug Product
CQAs (safety and efficacy)
Impact on Business
requirements (yield etc.)
Variability observed but:
No Impact on Drug Product
CQAs (safety and efficacy)
No Impact on Business
requirements (yield etc.)
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Excipient Variability
Excipient Variability – Regulatory and Business
47
Area for
Regulatory
Focus
Area for
Business
Focus
What is “Acceptable Risk Management” for
Excipient Variability?
Multivariate Analysis
PH101
PH102
PH200
8
4.0
3.0
2.0
1.0
-1.0
-2.0
1.0
t[2]
0.0
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Principal Component 2
4
2
0.0
-1.0
0
-2.0
-3.0
-3.0
-2
-4.0
-4.0
-6
-4
Principal Component 4
SIMCA-P+ 12 - 2010-12-20 13:20:05 (UTC-5)
Higher
Risk
35
30
20
15
10
5
0
Bin
Univariate Analysis
-4
Frequency
25
-6
-2
0
2
4
Principal Component 1
6
8
10
-5
-4
-3
12
35
35
30
30
25
25
Frequency
-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
20
15
-1
0
1
2
3
4
5
6
7
8
9
10
5
0
0
10
t[1]
SIMCA-P+ 12 - 2011-06-28 04:29:51 (UTC-5)
Minimal
Risk
15
5
Bin
-2
20
10
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Principal Component 6
2.0
Frequency
3.0
6
Bin
Future Question: What level of risk associated with excipient variability is acceptable for
patient safety and for drug product efficacy and supply?
48
Summary
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• Gather knowledge on excipients to reduce risks to patient safety
and drug product efficacy and supply.
– Work with material suppliers to better understand the variability
present in their materials.
– Leverage pre-existing data and models to support the knowledge and
design spaces.
– Multivariate analysis methods can help manage the large amounts of
excipient material property data.
Al
• Excipient understanding is as important as API and process
knowledge in the QbD world.
– Need to balance business needs with regulatory expectations.
49
Acknowledgements
• Pfizer
–
–
–
–
Stanley Wu
Wei Zhou
Ebenezer Otoo
Bo Wang
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Dan Gierer
Anthony Carella
Cindy Oksanen
Scott Herbig
Kim Vukovinsky
Kurt Speckhals
Angela Hausberger
Tom Garcia
Roger Weaver
Bruno Hancock
Craig Bentham
Graham Cook
Vince McCurdy
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–
–
–
–
–
–
–
–
–
–
–
–
–
• Patheon