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Generic R&D – CONFIDENTIAL
Inna Ben-Anat, Senior Engineer, Research and Development, Teva Pharmaceuticals USA
PQRI-FDA Workshop on Process DriftDecember 1-3, 2010
Process Drift Affects Specifications and Shelf-Life
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The Boiling Frog Story…
“The boiling frog story is a widespread anecdote describing a frog slowly being boiled alive. The
premise is that if a frog is placed in boiling water, it will jump out, but if
it is placed in cold water that is slowly heated, it will not perceive the danger and will be cooked to
death…”
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The Boiling Frog Story…
“The boiling frog story …”
“Process Drift” is the change of “normal process behavior” over time
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Presentation Outline
Process Drift: Potential Causes
Statistical Tools for Monitoring and Detecting Process Drift
Shelf-Life Re-Evaluation Illustration
Process Drift Affects Dissolution Dissolution Test Specifications
Potential root-causes for Dissolution change
Detecting and Monitoring tools and techniques
QbD, PAT and Specifications Setting
Summary and Conclusions
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Process Drift: What Is Causing It?
Raw materials: physical and chemical properties vendors change
Manufacturing equipment: upgraded/downgraded machinery equipment models/outputs
Operators: knowledge gain and lost recourses availability
Environment
Drift within measurement equipment
Analytical Methods...and many more factors potentially changing the process conditions…continuously
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Process Drift: How Can We Monitor and Detect It?
Control Charts
Data Modeling/DOE
Statistical Tools for Monitoring and Trending
Capability Analysis
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Process Drift: How Can We Monitor and Detect It?
Distribution ANOVA (Analysis Of Variance)
Regression Analysis
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Shelf Life Re-Evaluation
Shelf-Life of a product might be re-evaluated throughout product life-cycle
24 months Shelf-Life is commonly defined based on 12 weeks Accelerated Conditions stability data of the submission batch
A longer Shelf-Life might be considered based on actual obtained stability data
“Statistical design and analysis of stability
studies”
by Shein-Chung Chow
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Product Shelf-Life Determination
Definition of Shelf-Life (ICH Q1A): “The time period during which a drug product is expected to remain within the
approved Shelf-Life specification, provided that it is stored under the conditions defined on the container
label”
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Product Shelf-Life Prediction (ICH Q1A(R2))
Shelf-Life will be determined based on stability data evaluation of minimum three batches
The degree of variability of individual batches affects the confidence that a future production batch will remain within specification throughout its Shelf-Life
If the batch to batch variability is small, it is advantageous to combine the data into one overall estimate – slope and intercept equivalence test
If it is inappropriate to combine data from several batches, prediction isbased on the batch with shortest Shelf-Life
Shelf-Life will be determined based on the time at which the 95% confidence limit for the mean curve (CI) intersects the acceptance criteria
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Calculation example for Shelf-Life Re-evaluation: pooled lots data
All the batches can be pooled together for one
overall Shelf-Life estimate: slope and intercept
equivalence p-value is larger than 0.25
Poolability test: Slope and Intercept equivalence model
Intercept p-value
Slope p-value
All the data presented in this example is fictitious in sake of illustrating the calculation
procedure
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Calculation example for Shelf-Life Re-evaluation: pooled lots data
Shelf-Life determination based on 95% confidence limit for the mean curve
Recommended Shelf-Life is 34 months
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Calculation example for Shelf-Life Re-evaluation: based on the „worst‟ lot data
Shelf-Life determination based on one lot with shortest Shelf-Life(not required in this case)
Recommended Shelf-Life is 20 months based on the lot
with shortest Shelf-Life prediction
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Process Drift Affects Specifications:Dissolution Test Example
Dissolution/Drug Release Rate is one of the most critical
quality attributes in product development
Used as a quality control tool to ensure batch–to–batch consistency of predicted performance
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Dissolution Specifications Setting –Current Practice Deficiency
Specifications are set to fit the available, often limited data
May not be discriminative or sensitive enough to reflect the ‘true’ product quality and performance
Absolute Q values based on Mean and do not reflect Standard Deviation
Based on empirical approach – lack of product and process understanding
OOS results’ Root Cause is unknown
Variability source is unknown
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Process Drift May Affect Specifications:Dissolution Test Example
Dissolution is one of the most critical quality attributes that can be potentially affected by process drift
Possible root-causes:
Specifications setting deficiency (previous slide)
Excipients
API
Equipment
Processing parameters
Packaging
Analytical Method
Statistical tools and instrumentation can be utilized in order to monitor and detect process drift impact on dissolution
Blister overheating during packaging
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Process Drift May Affect Specifications:Dissolution Test Example
Statistical Tools
Control Charts
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Process Drift May Affect Specifications:Dissolution Test Example
Dissolution AVG: Grouped by manufacturing year
Dissolution Range (Max-Min): Grouped by manufacturing year
Year X Year Y Year Z
Year X Year YYear Z
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Process Drift May Affect Specifications:Dissolution Test Example
Dissolution AVG: Grouped by manufacturing year
Year X Year Y Year Z
Date Tested??
Lab Error??
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Process Drift May Affect Specifications:Dissolution Test Example
Statistical Tools
ANOVA
Is Year Y significantly different??
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Process Drift May Affect Specifications:Dissolution Test Example
Statistical Tools
Regression and Trend analysis
Is Year Z stability trend significantly different??
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Process Drift May Affect Specifications:Dissolution Test Example
The FT4 Powder Rheometer ( ) is a universal powder tester that can measure dynamic flow, shear and bulk
properties of powders and granules:
Instrumentation
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Process Drift May Affect Specifications:Dissolution Test Example
Lot C
Lot D
Lot B
FT4 Powder Rheometer Granulation TestingTota
l E
nerg
y (
mJ)
Lot A
FT-4 powder tester is discriminative enough to observe the granulation differences and correlate
it with dissolution values
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Specifications Setting –QbD Approach
Drug Development process understanding and control:
Design Specifications early in development-target product profile
Root Cause analysis
Gauge R&R to identify source of variability
Specifications as continuous "real time" assurance of product quality, should evolve during development as process knowledge and experience increases
Life Cycle Management – data collection and trending using statistical tools
Raw Materials Properties
Effect Of Formulation on manufacturing process
Effect Of Manufacturing Process on critical quality attributes
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In Process Testing – PAT
PAT – Process monitoring tools needed to analyze critical attributes: on-line monitoring (NIR, FBRM)
Detecting errors or process deviations and correcting them while the product is being made
Continuous Improvement and control of design space
Impact on Specifications Setting: Quality control testing will evolve from testing against a discrete specification (pass/fail) to real-time comparison of process/product signatures against a reference
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In Process Testing – PAT
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Summary
Don‟t become a "boiled frog": be proactive – look for trends, be a detective – explore your data
"Traditional" Specifications based on process experience; "QbD" specifications leverage process understanding: increased ability to control the process and eliminate the drift in the future
Specifications as continuous "real time" assurance of product quality: life cycle management, data collection and trending using statistical tools
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References
ICH Q6A – Specifications: Test Procedures and Acceptance Criteria for New Drug Product
ICH Q8 – Pharmaceutical Development
ICH Q9 – Quality Risk Management
ICH Q10 – Pharmaceutical Quality System
ICH Q1A (R2) – Stability Testing of New Drug Substances and Products
ICH Q1E – Evaluation of Stability Data – Comments
„Establishing Drug release/Dissolution Specifications – QBD Approach‟, Moheb M. Nasr, Ph.D., ACPS, 2005
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Questions?
Thank you!
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