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Implementation of Analytical
Quality by Design Concepts at Pfizer
James Morgado
On Behalf of the AQbD Alignment Team:
Melissa Hanna-Brown, Neil Clayton, Tim Graul,
Kimber Barnett, Loren Wrisley, Brent Harrington (Stats),
Chuck Melucci (GCMC)
1
Outline
• Introduction –
– What is Analytical QbD
– Why we are exploring it
• Elements of Analytical QbD (AQbD)
• Examples from Chromatographic Case Studies
• Refining our Approach
• Path Forward
2
Acknowledgements
• Jeff Harwood
• Dave Fortin
• Jian Wang
• Debbie Kraus
• Greg Steeno
• Steve Colgan
• Kyle Leeman
• Ke Wang
• Steve Chesnut
• Jason Ewers
• Dave De Antonis (VP Analytical R&D)
Core Team:
• Tim Graul
• Melissa Hanna-Brown
• Brent Harrington
• Kimber Barnett
• Jim Morgado
• Loren Wrisley
• Chuck Melucci
• Neil Clayton
3
Quality by Design Elements (synergies)
Processes Element Analytical Methods
Target Product Profile Establish Criteria Analytical Target Profile
(ATP)
Process Design Design supported by sound
science
Method Design
Risk Assessment Identify Risks Risk Assessment
Process Design Space Demonstrate Robustness Method Operable
Design Region (MODR)
Control Strategy Establish Appropriate
Controls
Control Strategy
Monitor Process
Performance
Monitor Performance Monitor Method
Performance
Continual Improvement Evaluate innovative
approaches
Continual Improvement
4
The same principles that are applied to Manufacturing Processes (ICH Q8-11)
can be applied to Analytical Methods
Why QbD for Analytical Methods?
5
Facilitates a framework to…
• Understand, identify, reduce and control, sources of analytical
method variability
• Enhance method understanding improves robustness and
ruggedness
• QbD concepts offer an opportunity to enhance current practices
The concepts are not new…
• Activities are better connected throughout the method lifecycle
• We’re applying the concepts in a rigorous, consistent, and
harmonized manner to provide accurate, precise, and reliable
results
AQbD Process Workflow
6
Define Objectives
(Method Design)
Method Selection
(Method Design)
Identify Quality
Attributes
Quality
Risk Assessment
Identify and Prioritize
MethodParameters
ID Experiments
Understand
CQA = f(CPP)
Risk Assessment
Prioritize
Experiments
Develop
MODR and
Control Strategy
(Risk Mitigation)
Perform
Experimental Strategy
Develop Method
Understanding
Verify MODR &
Establish Control
Strategy
Method
Understanding
Knowledge
Management
Analytical Target
Profile
Method Design: The Analytical Target Profile
• Describes the needed performance of the method in terms of sources of “Uncertainty”
– Expressed in terms of accuracy and precision jointly, through a probability statement of measurements meeting pre-defined criteria
• Can be structured so that the ATP is not linked to a specific technique
– Linked to the result generated
– The method is just a tool to generate a result(s)
– More than one technique may satisfy ATP requirements
• (e.g., HPLC, CE, IC, SFC, etc…)
7
Method Design: Analytical Target Profile
• ATP describes measurement requirements
(method performance criteria)
– based on a probability of reportable result being within a given
range from the “true value”
The procedure must be able to accurately quantify [drug] in film
coated tablets over the range of 70 – 130% of the nominal
concentration with accuracy and precision such that
measurements fall within ± 3.0% of the true value with 95%
probability
8
Method Design: Traditional Method (Precision/ Bias)
Validation Criteria Graphical Representation
9
Criteria
Bias (Accuracy): NMT 3.0%
Variability (Precision): NMT 2.0%
Low bias, high
variability
•At boundaries: no trade-off
between method bias and
precision
•It is possible to accept a
method with both high bias
(low accuracy) and high
variability (low precision)
•Does not consider TOTAL
method variability
Method Design: The ATP Equation defines a joint
(Precision/ Bias) Probability Contour
• A contour line is a
curve connecting
points of a function of
two variables, where
the function has the
same value.
10
ATP plot: measurement ± 3.0% of the true value with ≥95% probability
Traditional Criteria
ATP Criteria
Method Design: Interactive ATP Tool- Modeling Trade-off between Bias (Accuracy) and Variability (Precision)
11
API
Specification 98.0-102.0%
Process Mean = 100.0%
Process std dev = 0.2%
ATP criteria: ± 2.0 from the true
value with a 95% probability
If method bias = 0
then the method standard
deviation should be ≤ 1.0%
(area under the curve that is
OOS is ~ 5%)
• What specifications can my method support with its inherent
variability and bias…and proposed replicate strategy?
Develop Method Understanding: Risk Assessments: Quality Risk Management
12
Risk Identification
List method parameters that could potentially affect the
method
Examples:
Material Properties: Formulation composition, Solubility,
Tablet hardness
Extraction: Diluent, Extraction type, Shaker speed
Mobile Phase: Reagent purity, solvent grade
Injector: Strong/weak needle wash, Volume
Separation: Column temperature, Flow rate, Ionic
strength, pH
Detector: Wavelength, Data rate, Band width
Method
Understanding
Risk Analysis
Qualitative or quantitative process to estimate
risk associated with method parameters
QRM Tools: - Process Flow Diagrams
- Cause & Effect Matrix
- FMEA
- Ishikawa Diagram
Failure mode and effects analysis
Ishikawa Diagram
Process Flow Diagrams
13
Develop Method Understanding: Risk Assessments: Quality Risk Management
Method
Understanding
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Risk Evaluation - Weightings
Compare the identified and analyzed risk against
given risk criteria.
Agree which method parameters need to be further
evaluated experimentally, or controlled through
specific method instructions (or other means). (e.g.,
Comprehensive Risk Assessment…below)
Develop Method Understanding: Risk Assessments: Quality Risk Management
Method
Understanding
Assessment of experimental results via DOE or One Off experiments
Risk Evaluation - DOE
Compare the identified and analyzed risk against given
risk criteria.
Agree which method factors need to be further
evaluated experimentally, or controlled through specific
method instructions (or other means). Response
results evaluated against the ATP…
Temp
Buffer
Strength
pH
-
+
- +-
+
Temp
pH
-
+
- +-
+
% Organic
+-
Buffer
Strength
Parameters (Factors): Units Low Target High
1 Initial Organic Content % 13 18 23
2 Initial Hold Time min 0.5 1.0 1.5
3 Final Organic Content % 52 57 62
4 Gradient Time min 7.5 8.0 8.5
5 Mobile Phase pH N/A 6.0 6.5 7.0
6 Column Temperature °C 27 32 37
7 Buffer Concentration mM 10
8 Flow Rate mL/min 0.4
15
Develop Method Understanding: Risk Assessments: DoE Implementation
Method
Understanding
16
EndB (%)
Sta
rtB
(%
)
low high
low
high
• Contour plots show
resolution of Peak Pair A
•Contour plots look similar
across the rows and down
the columns
•End B and Start B
had little effect
•The perchloric acid
concentration is most
dominant. Resolution
increases with increasing
perchloric acid
concentration.
• Arrow points in the
direction of increasing
resolution.
Develop Method Understanding: Develop MODR and Control Strategy (Risk Mitigation)
Design-Expert® SoftwareFactor Coding: ActualDesirability
1.000
0.000
X1 = D: Buffer pHX2 = E: Column Temp
Actual FactorsA: Gradient Hold Time = 1.00B: Gradient Organic T=1 = 3.00C: Gradient Organic T=2 = 72.00
5.80 5.90 6.00 6.10 6.20
45.00
46.00
47.00
48.00
49.00
50.00
51.00
Desirability
D: Buffer pH
E:
Co
lum
n T
em
p
0.100
0.200
0.200
0.300
0.400
0.500
Prediction 0.579
17
-2
-1
0
1
2
-2-1.5-1-0.500.511.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Score #2 (Explained variance=28%)
Score #1 (Explained variance=46%)
Score
#3 (
Expla
ined v
ariance=
9%
)
Minimum Res < 1.5
1.5 < Minimum Res < 2.0
2.0 < Minimum Res < 2.5
2.5 < Minimum ResOptimal
direction
Method Condition Min Res=1.46
Simultaneous multi-dimensional projection and optimization of response
data from DoE studies…using MLR and PLS analysis approaches…
Optimal
direction
(1) 3-D PCA: PLS-DA Simulation
Approach for all resolutions…
(2) 2-D Factorial Analysis
Desirability Approach
Develop Method Understanding: Develop MODR and Control Strategy (Risk Mitigation)
Verify MODR & Establish Control Strategy Method Evaluation and Verification Strategy
• Two phases:
– Screening DoEs – Evaluation of method performance across a range
of method parameters
• Identify/confirm regions of optimal performance
– Verification DoE – ATP verification
• A subset of method conditions with highest risk for method performance
as determined by screening DoEs
18
Screening
Sensitivity
Resolution
Accuracy
Accuracy
Precision
Verification
Experimental Verification (Example)
19
Factor Name Units Point 1 Point 2 (Nominal) Point 3
A Temperature °C 37 40 43
B Flow Rate mL/min 0.90 1.0 1.10
C Organic Modifier % B 62 64 66
Example of Parameters confirmed at boundary points of the MODR
•Precision (API and Degradants): (Injection Repeatability)
•Accuracy (API): (Accuracy Determined by Wet Spike in Placebo versus an Ext. Standard))
•Repeatability (API): (Accuracy Precision across the design space)
•Accuracy (Specified Degradant): (Accuracy at 0.05% and 0.2% Determined by Wet Spike Assay
in the presence of Placebo determined by Area % vs. Theoretical)
•Repeatability (Specified Degradant): (Accuracy Precision across the design space)
Multivariate method factor combinations identified as critical boundary points
Verify MODR & Establish Control Strategy Method Evaluation and Verification Strategy
20
Lab 1
Lab 2
Lab 3
Histogram of Assay
MODR Verification
Results Relative to the
Analytical Target Profile
Example: Analytical Target
Profile Probability Contour Plot
for Assay Measurements
(presented as a histogram for a method with no Bias)
Verify MODR & Establish Control Strategy Method Evaluation and Verification Strategy
21
Histogram of MODR Verification Results Relative to the Analytical Target
Profile Measurement for Degradants 0.15% (top) > 0.15% (bottom)
211815129630
RSD (%)
ATP Max
s
9.88.47.05.64.22.81.40.0
RSD (%)
ATP Max
s
Verify MODR & Establish Control Strategy Method Evaluation and Verification Strategy
Refining our Approach
• We’ve recently included intermediate precision factors in
verification DoE’s -- a work in progress
• multiple instruments
• multiple columns
• multiple analysts
• multiple days
• Provides an assessment of intermediate precision across the
Method Operable Design Region (MODR)
• Allows partitioning of total variability into individual sources
– Feeds control strategy, replicates and sampling strategies
22
Path Forward
• Our Focus is on method understanding!!!
• Continue to apply principles to the methods we
develop…
• Currently the process is leveraged for chromatographic
methods, but other high risk methods could benefit …
– Use data/knowledge to optimize methods
• The AQbD approach could be adapted rather easily to
CE methodologies
23
