US FDA Process Validation Stage 3: Continued Process Verification

This presentation is compiled by “ Drug Regulations”
a non profit organization which provides free online
resource to the Pharmaceutical Professional.
Visit http://www.drugregulations.org for latest
information from the world of Pharmaceuticals.
8/6/2015 1

 This presentation is compiled from freely
available resource like the website of FDA, EMA
and publically available literature from websites
of PDA, ISPE and DIA.
 “Drug Regulations” is a non profit organization
which provides free online resource to the
Pharmaceutical Professional.
 Visit http://www.drugregulations.org for latest
8/6/2015 2
Drug Regulations : Online
Resource for Latest Information

 This presentation addresses following:
◦ How is Stage 3 monitoring and testing determined as a part
of Life cycle approach to Process Validation ( PV)
◦ What is the impact of the Life Cycle approach to monitoring
and testing of legacy products.
 More experience is required in implementing life
cycle approach to PV to arrive at a Industry
consensus.
8/6/2015 3

 FDA guidance on PV in January 2011.
◦ Process Validation: General Principles & Practices.
 EMA issued revision to the Guideline on Process
Validation.
 EMA guidance clarifies
◦ How to implement the principles of ICH Q8, Q9 and Q10.
◦ The possibility to use “continuous process verification”
◦ Different from “Continued Process Verification” in FDA
guidance.
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 PV Guidance of FDA is main focus of this
presentation.
 Life cycle approach to Process validation which links
◦ Product & process development
◦ Qualification of the commercial manufacturing process
◦ Maintenance of the process in a state of control during routine
commercial production.
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 Process validation Definition
◦ “Collection and evaluation of Data from the process
design stage through commercial production which
establishes scientific evidence that a process is
capable of continuously delivering quality product”
there by also assuring reliability of supply.
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 Process validation
 Emphasis that process validation should not be
seen as one off event.
 Goals of different stages of process validation are
given in subsequent slides.
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 Process validation Goals
 Stage 1 :
◦ Process Design
◦ Define & Design Process
◦ Build Knowledge & understanding generated
through development and scale up activities and
establish a strategy for process control
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 Stage 2 :
◦ Process Qualification
◦ Process design is evaluated to determine if the process
is capable of reproducible commercial manufacturing.
◦ Design of a facility and qualification of utilities and
equipment should be completed before commencing
this step.
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 Stage 2 :
◦ Process Qualification
◦ Involves manufacture of number of Process Performance Qualification
batches to
 Confirm the process design
 Demonstrate that the commercial manufacturing process performs as
expected in the commercial manufacturing facility.
 Level of sampling may be higher than routine manufacturing
 No. of samples should provide a statistical confidence of quality within and
between batches.
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 Stage 3 :
 Continued Process Verification
◦ Ongoing assurance that the process remains in a state of control
◦ Ongoing program
◦ Collect and analyze product and process data
◦ Assure state of control
◦ Verify impact of variability.
◦ Identify potential issues
◦ Determine whether action must be taken to correct , anticipate and prevent problems
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 Process Performance has been adopted by
some organizations to support continuous
improvement
 To bring this under PV is a significant change
 Different interpretations and approaches
possible
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 This presentation will
 Provide practical approaches to CPV
 Considerations for selection of appropriate
attributes and parameters for sampling in CPV
 Specific example developed by ISPE on how to
establish and apply stage 3 monitoring.
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 Definition of CPV
 “ Assuring that during routine production the process remains in a state of
control”
 Refers to cGMP section 211.180(e)
 Requires
◦ Collection and evaluation of information and data about the performance of the process
◦ Verification that the Quality attributes are being appropriately controlled throughout the
process
◦ Provide statistical confidence of Quality
◦ Evaluation of process stability and capability
◦ Identification of variability and/or potential process improvements.
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 CPV is an ongoing program and could be applied to
 New Products :
◦ Developed through FDA process validation stages 1 and 2
and entering routine commercial manufacturing.
◦ Implemented immediately following successful PPQ
◦ FDA guidance recommends
 “ Monitoring and sampling of process parameters and quality
attributes at the level established during PPQ stage until
sufficient data is available to estimate variability estimates”.
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 CPV is an ongoing program and could be applied to
◦ Existing or Legacy Products:
◦ Existing validated products in commercial manufacturing.
◦ A monitoring plan should be established or enhanced for
existing products.
◦ Define the scope of the CPV
◦ Identify parameters and attributes for CPV
◦ Conduct evaluation of Process performance based on the
historical manufacturing data/ routine manufacturing data
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 Implement
 A systematic approach to maintenance of the
facility, utilities and equipment
 Periodic review of the equipment and facility
qualification status.
 Can be referenced to Quality systems for CPV
monitoring plan.
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 CPV will also help to
 Identify improvement areas
◦ Adjustment to the control strategy , specifications
◦ Support evaluation of the proposed post appoval changes
◦ Facilitate investigations
◦ Support Technical Transfers
◦ Confirm that the changes / process improvements have had
the desired impact.
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 CPV concepts can be applied to
 Small & large molecules
 Sterile & Non-sterile products
 Finished products
 Active Pharmaceutical Ingredients
 Combination products
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Stage 1 : Process Development
Document Process understanding ,
control strategy , CQAs, CPPs
Stage 2: Process Performance
Qualification Verify Control Strategy
Stage 3 CPV
Establish monitoring & sampling plan for each
CQA

 CPV: Establish monitoring & sampling plan for each CQA
 Assess need for enhanced understanding based on
◦ Criticality level of Quality Attribute
◦ Unit Operation & parameter variability impact to the Quality
attribute & current capability.
◦ Range of experience of material variability impact on the
quality attribute
◦ Robustness of any predictive models.
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Data Collection
Data Analysis and Review
Determination whether CAPA needed
Continuous Improvement & Change
Management
Update Process understanding

 Plan for CPV should address
◦ Input and out parameters/attributes
◦ Data collection methods
◦ Statistical methods to be used
◦ Frequency at which data will be evaluated
◦ Plan could be product specific covering all unit operations,
raw materials, equipment settings
◦ Products could also be grouped
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 Plan for CPV should address
◦ Roles & Responsibilities
◦ Management reviews
◦ Mechanisms to trigger changes requiring redesign &
re-qualification.
◦ Leveraging existing data of the Quality System
including APR
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◦ Determine based on the current understanding of the
manufacturing process and systems in relation to quality
attributes
◦ This can be based on
◦ First principles
◦ Prior knowledge
◦ Statistical Models based on DOE
◦ Empirical Models
◦ Understanding based on manufacturing experience
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◦ Generate significant estimates of variability
◦ Adjustments to the control strategy may be warranted
◦ Potential changes in process/ raw material attribute or
parameter criticality may be warranted
◦ Initial monitoring may
 Involve grater number of parameters
 Heightened sampling and testing
◦ Some CQA’s may require separate validation studies ( Viral
Clearance)
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◦ Number of parameters and/or extent of testing may
increase or decrease as
 Knowledge is gained
 Specific residual risks are reduced or
 New risks identified
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◦ Decision to discontinue testing should be based on
 Confidence achieved in the stage 1 and stage 2 and
throughout stage 3
 Robustness of the control strategy
 Ability of the control strategy to manage variation
 Rationale for change or discontinuance should be
documented
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 Developed according to QbD principles
 Control Strategy identified during development
 Based on process understanding & QRM
 Consider parameter & enhanced sampling of stage 2 for CPV
 Consider Intra batch & inter batch variability for establishing
monitoring plan
 If this has been done during PPQ then sampling and testing can
be reduced to routine levels.
 Justify routine level scientifically and statistically
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 Complete establishing the capability of the process
 Look at process performance across many batches
 Build overall picture of inter batch variability
 More batches may be needed if the variability of the input affects final
product quality
 Establish statistically based control limits to evaluate atypical variation of
inputs
 Process capability analysis can be performed with groups of samples.
 Process capability can be calculated by considering batches in all three
stages of validation
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 Determine impact of each process variable/material
attribute on CQA by risk/criticality analysis
 Criticality analysis may have been done in Stage 2 but
cause and effect relation between inputs and outputs may
not have been established.
 Where variability has not fully assessed
◦ Evaluate potential sources of variability
◦ Determine their impact on CQA or process performance
◦ Confirm / establish input vs VQA correlations.
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 Products are developed by traditional process
 Critical quality attributes and process parameters are not defined
 Perform criticality and risk assessment
 Include raw and in process material attributes & manufacturing systems
that may affect the safety, efficacy and product quality.
 Perform this assessment based on
◦ Current process understanding
◦ Experience of the commercial manufacturing process
◦ Quality system events like deviations and COC
◦ Product performance on stability
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 Use data of existing routine monitoring of the defined
critical attributes and parameters.
 Initial evaluation could be retrospective review of existing
process performance data.
 As data is collected additional aspects may need
evaluation
 Monitor additional parameters across all batches
 Implement intensive sampling of an output within a batch
until desired level of process understanding is achieved.
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 This monitoring could
◦ Provide data to develop correlations
◦ Establish cause effect relations
◦ Identify inputs requiring better control
◦ Help reduce out put variability
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 Review the plan periodically during the commercial phase of the lifecycle
 Review can be after a defined phase : after number of batches
 Review the plan if there is a process change which may affect the quality or
process performance
 Data in stage 3 focuses on material or process data
 Data from other quality systems could be used to review process performance
 Other systems which may provide information on product quality
◦ Performance on stability
◦ Periodic review
◦ Complaints
◦ Deviations
◦ Atypical events
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 Develop a process to collect, analyze, report and store
obtained data.
 Use appropriate technology tools
 Use statistical tools of various complexities
◦ Time series plot
◦ Histograms
◦ Box Plots
◦ Process capability
 Cp, Pp, CpK, Ppk
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 Consider distribution of the data : Normal vs. Non
normal
 Define how the results will be reported
◦ Process capability : Index or Defects per million
opportunities
 Cross refer applicable GMP documentation for
excursions outside approved procedures, formulas,
specifications, standards or parameters.
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 Set limits to provide an indication to indicate
change and attention.
 These limits represent voice of the process
◦ Common cause variation inherent in the process
 Different from specifications, proven or studied
boundaries or in process control limits.
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 SPC limits are determined in different ways.
 Control chart limits
◦ Establish temporary control limits
◦ Once sufficient data is available establish permanent / locked limits
 Time series run charts may be used for parameter attribute
assessment until a statistically significant data set is available.
 Alternatively initial limits can be set based on historical data or
other sources of process understanding.
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 Review control limits when intentional changes are
introduced
 If as part of an “out of control” assessment a special cause
is identified suspend control limits till more data is
obtained.
 Where special cause events these values should be
removed from calculations for the establishment of control
limits
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 Review control charts at predetermined frequency
 Identify in as real time as possible
◦ Process excursions : out of statistical control
◦ Data points which fall out side of locked statistical limits
◦ Shifts : Out of statistical trend events
 Assess statistical out of control events based on the quality concern of
the event.
 Clearly define what will constitute OOST event in monitoring plan
 Review the Eight Western Electric rules to establish guidelines for
identifying OOST events.
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 Capability analysis links
◦ Voice of the process : routine or common cause process variation
◦ With
◦ Voice of the customer : Attribute / parameter acceptance criteria
 Perform capability analysis
◦ Once the process is in statistical control
◦ When Data is normally distributed
 If desired level of process capability is not achieved implement
process improvement plans.
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 Indices measure how well the data fits into the
specification limits.
 Frequently used process capability indices include Cp
and Cpk.
 Cp is used to evaluate the variation of the process.
 Cpk is used to evaluate the centering of the process.
 Normally the Cp / Cpk values be targeted at 1.33 or
above

USL = Upper Specification Limit, LSL =
Lower Specification Limit.

 Cp and Cpk are statistical tools
 Ensures that a production process has met the
specification limits defined for a particular
process or products.
 Cp measures the process capability with respect
to its specification using Upper Specification
Limit (USL) and Lower Specification Limit (LSL)

 Cpk measures the process variation with respect to its sample
mean, which is also considered to be the process mean.
 Process capability is determined by taking periodic samples from
process under controlled conditions and calculating its standard
deviation and sample mean.
 Standard deviation determines how far a sample is from the
sample mean
 sample mean is the average of the samples taken under
consideration.

Cp < 1: The process output
exceeds specifications. The
process is incapable.
Cp = 1: The process barely
meets specifications. There is
a probability that at least 0.3%
defects will be produced and
even more if the process is
not centered.
Cp > 1: The process output falls within specifications, but, defects might
be produced if the process is not centered on the target value.
Cp = 2: Represents the short-term objective for process capability. Since
Zst = 3 x Cp, we achieve 6 sigma when Cp = 2.

Cpk = Cp: The process mean
is on target.
Cpk = 0: The process mean
falls on one of the
specification limits, therefore,
50% of the process output
falls beyond the specification
limits.
Cpk < -1: The process mean is completely out of the specification limits,
therefore, 100% of the process output is out of specification limits.

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◦ Cp and CpK measure Potential process capability
◦ Typically referred to as Short Term Process variability
indicators
 Within variability based on the control chart sigma value
◦ Pp and PpK are referred to as long term process
indicators
◦ Rely upon overall process variability
 Based on sigma for the n-1 points
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 For Data assessment smaller of the capability indices is typically
used as a conservative measure.
 Larger the Index more capable is the process of meeting the
specification limit
 Usually CpK / PpK of > 1.33 is desirable.
 If CpK < 1 variability of the process is greater than the
specification limits
 CpK > 1.33 corresponds to 4 sigma
◦ 99.99 % of the data will be within specifications
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 Capability analysis
◦ Performed on existing process to establish baseline
◦ To determine if an improvement had an impact
◦ Monitor or control process as in case of a stage 3 data
◦ Can be applied to a new process as part of Qualification
and approval process
◦ Reliability engineering metrics can be used as a means
of monitoring equipment wear and tear.
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◦ Outline actions to follow for investigation out of control/alert values and
occurrence of trends
◦ Extent of investigations should depend on
 Proximity of results to specifications
 Perceived risk of the process shifting & leading to future OOS
◦ Investigate Instances where the values are within control limits but control
limits lie out side specifications
 Try to identify root cause and implement corrective actions.
◦ For very low variability processes outliers lie very close to control limits
and a sufficient safety margin versus the specification.
 For such outliers conduct lesser degree of investigations
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◦ Statistical control limits
 are tighter than acceptance criteria
 Should to be considered acceptance criteria themselves
 Used to alert process SME’s and management to potentially
unacceptable process variability which may lead to future
batches not meeting specifications
◦ Out of statistical control limit value
 Should not impact batch release decision
 Consider impact prior to next manufacturing campaign
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 Desired State
◦ Potential issues are identified as soon as data are entered in
the process analysis tool.
 Could be direct from the process in real time
 Could be performed after the batch has been completed
◦ An automated alert system can be used to inform
responsible persons of issues as soon as they are detected
◦ When evaluation against control / alert limits is manual
establish appropriate interval for process analysis.
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 Management Review
◦ Cross functional team meetings
◦ Operational excellence / Continuous improvement
programme
◦ Conduct for all processes currently running at the
site
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 Ongoing review of process performance
increases process understanding for
◦ Attributes and parameters which are in control
◦ Special and common cause variation through
investigations.
 Use this information to update process
understanding and process design
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 Knowledge management system should support
◦ End to end : Raw materials to impact on finished product
◦ Site to site
 Knowledge management tools
◦ Documentation sharing software
◦ Global Product meetings
◦ Review metrics
 Periodic Product performance reviews should include all stake
holders to leverage increased process understanding for similar
products and process platforms.
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◦ Define responsibilities in the monitoring plan document
◦ Activities include
 Data collection , evaluation and analysis
 Escalation mechanisms
 Management reviews
◦ Personnel should have understanding of statistical tools
being used.
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◦ Actions
 Identify to Eliminate / control special cause variation
 Limit / remedy impact in the short term
 Timely
 Based on the understanding of the event
 Long term actions to prevent recurrence of the special
cause events
8/6/2015 63

◦ Special cause variations
 Outside approved procedures, formulas, acceptance criteria,
standards or parameters
 Requires investigations, CAPA through the site deviations
management process
◦ Common cause variation
 Requires a more fundamental approach to understand the
sources of variation
 Identification of ways to reduce variation
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 Improvement actions
 Evaluate for impact on
◦ Product Quality
◦ Compliance to regulations
◦ Prior regulatory submissions
◦ Technical feasibility
◦ Process efficiency
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 Confirm all CAPA through a monitoring plan
 Document in CAPA system
 Use change management system
 Agreed actions may require
◦ A change to process design ( stage 1 of FDA process) or
◦ Process Validation ( Stage of the FDA process)
8/6/2015 66

 When special cause variation : OOSC or OOST or Low process capability
is identified conduct root cause analysis.
 Use statistical techniques
◦ Hypothesis testing
◦ Principle component analysis
◦ Linear regression
◦ ANOVA / MANOVA
◦ Multiple regression tools
 Based on the outcome take measures
◦ Tighten parameters : Unit operation , environmental
◦ Tighten incoming material specifications
8/6/2015 67

 Example is based on
◦ A science and risk Based approach
◦ Selection of subset of CQA and Process Parameters for
for CPV
◦ Similar levels of monitoring and sampling as stage 2 ,
process qualification
 Heightened monitoring and sampling compared to the
eventual routine situation.
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1
•Assess criticality of Quality Attribute
•Assess each unit operation and parameter impact to the Quality Attribute
•Assess Material Variability Impact to the Quality Attribute
•Assess Process operations impact on the Quality Attribute
2
•Amend Monitoring plan for Stage 3 using Risk Based Approach
3
•Collect sufficient data to demonstrate control and capability and that all sources of variability is
understood.
4
•Move to routine monitoring programme.

 Example
◦ Utilize the assessment in previous slide for each
CQA of the out put drug product or drug substance
◦ This process will be utilized for Oral Solid Dosage
form CQA for Content Uniformity of Dosage Units
and dissolution.
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 Step 1 : Assessment of Drug Product CQA’s to monitor.
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Critical Quality
Attribute
Criticality
Rating
Rationale & Comments
Assay High Over dose : Side effects; Under dose : Lack of efficacy;
however dose response curve is not precipitous
Uniformity of
Dosage Units
High Variability in plasma levels may lead to side effects or poor
clinical response; however, dose response curve is not
precipitous.
Dissolution High Poorly soluble ( BCS class 2) drug substance- dissolution
characteristics important for bioavailability; linkage of
bioavailability to dissolution is not known.
Water Content High Moisture may affect degradation of dosage form
performance, i.e. dissolution. AP stable; however in API in
drug product requires further investigation. Could also
impact microbiology.

 Step 1 : Assessment of Drug Product CQA’s to
monitor.
◦ Product : Oral Tablet
◦ Dose : daily 30 mg
◦ Example focused on 2 CQA’s that are assigned high
severity
 Uniformity of dosage units
 Dissolution
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 Step 2 : Assessment of each unit operation
and parameter impact to the CQA’s
◦ The cause and effect matrix after formulation
development and before process development is
given in next table.
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 Step 2 : Assessment of each unit operation and parameter impact to the CQA’s
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Drug Product
Dispense Blend Lubricate Compress Coat Package
Appearance
M
Identity
M M
Assay
M M M
Impurities
UDU
H M M
Dissolution
H H
Micro

 Step 2 : Assessment of each unit operation and parameter impact to
the CQA’s
 Selection of UDU and dissolution
 Highest risk quality attributes before process development
 Development PV stage 1 studies lead to an understanding of Assay CQA
 Extensive studies result in introduction of important elements of control
strategy.
◦ In-line NIR testing for blend homogeneity
◦ At line testing of uniformity of dosage units
◦ Algorithm as an important element of the dissolution control strategy.
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 Step 2 Cause and effect matrix after process development studies and
application of control strategy.
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Drug Product
Dispense Blend Lubricate Compress Coat Package
Appearance
C C C C C C
Identity
C C+E C C C C
Assay
C C C C+E C C
Impurities
C C+E C C C C
UDU
C C C+E C+E C C
Dissolution
C C C+E C+E C C
Micro
C C C C C C

 Step 2 : Assessment of each unit operation and parameter
impact to the CQA’s
 Types of control strategy applied
 C = Conventional strategy elements including cGMP
 C+E = Conventional strategy elements including cGMP +
Elements of enhance / QbD approaches.
 Using the risk assessment criteria applied during development
shows that the impact on all drug product CQAs is low risk when
a comprehensive control strategy is applied.
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 Step 2 : Assessment of each unit operation and
parameter impact to the CQA’s
◦ Evaluate the residual risk using a different risk assessment tool.
◦ Evaluate the residual risk strategy
◦ Theoretically all of the unit operations have been mitigated to
predicted green for each unit operations impact on each CQA.
◦ However variability across wide range of input material attribute
variability in combination with operational conditions may not
have been demonstrated.
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 Step 2 : Assessment of each unit operation and
parameter impact to the CQA’s
◦ For the purpose of this example the focus will be on the
impact of compression on dissolution and content
uniformity.
◦ A summary of the control strategy risk assessment for
UDU and dissolution after stage 2 process validation is
seen subsequent table leading to proposed PV stage 3.
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 Control Strategy Assessment Summary after Stage 2 Process Qualification
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Uniformity of Dosage Units
Acceptance criteria applied to drug substance particle size
Extensive studies performed to understand homogeneity during
blending and lubrication operations.
In-Line NIR applied to blending and lubrication unit operations
At line NIR individual tablet assay UDU provides high level of
confidence using stratified sampling.
Each tablet indirectly evaluated for weight ( pressure monitoring)
Control strategy established and confirmed

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Uniformity of Dosage Units
Considerations for Stage 3 Process Validation CPV based on step 2
Consideration of reduced testing compared to Stage 2 for routine
monitoring based on control strategy.
Reduced frequency of at-line testing of individual tablet for assay.

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Dissolution
Understanding of process to produce an acceptance criteria applied to
drug substance particle size
• Prediction algorithm developed from DoE’s dependent on :
• Drug substance particle size
• Specific surface area of lubricant
• Lubrication mixing time
• Tablet crushing force
Model developed at small scale and limited conformation during stage
2, process qualification
Stratified sampling applied for testing of tablet cores

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Dissolution
Control strategy established and confirmed
Continue stratified sampling at same level as above for a pre-defined
number of batches
Measurement of material attributes of drug substance
• Particle size
• Specific surface area of the lubricant
• Tablet crushing force
• Process parameter of lubrication time
Maintain end product testing
Verify Multivariate model and demonstrate robustness
Provide comparative data between prediction and measurement to
support movement to RTRT

 Step 3 : Assess Material Variability Impact
◦ Summary out put of Risk Evaluation after Formulation Development and IVIVC studies
using FMEA
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CQA
(Severity)/F
ailure
Mode
App
Fri
Hard
Impurities Water CU Disso Micro
Magnesium
Stearate
Surface Area
Particle Size

 CU: Low level of risk remains for the API particle size.
 Disso: A high level of residual risk remains for Magnesium Stearate
specific surface area and API particle size.
 Two raw materials with the potential to have significant impact on dose
uniformity and dissolution.
 Based on prior knowledge API particle size could have significant impact
on content and dose uniformity.
 Due to the particle size of the API; however, tablet strength and testing
during development, this has been migrated to a low risk level.
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 Conversely , due to the feed forward control strategy model,
limited development data and the desire to eliminate routine
testing of dissolution as part of batch release testing, the CQA of
dissolution is considered an excellent candidate for heightened
testing as part of CPV plan.
 The variability of within batch results should be be considered in
determining the number of samples and locations which will be
tested for each batch.
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 Although the powder blend is designed to be free flowing,
variability is estimated during early phases of production
with different batches of excipients.
 Continued monitoring of dissolution and material
attributes and process parameters impacting dissolution is
justified during the first part of CPV to confirm that the
model and predictions are acceptable.
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 Step 4 : Assess Operational Practices Impact
 Following operations practices could impact content
uniformity or dissolution.
 Multiple Operators
 Multiple Shifts
 Multiple Equipment sets
 Process interruptions ( Breaks, lunches, weekends,
equipment malfunction etc.)
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 Step 4 : Assess Operational Practices Impact
 A review of the level of process understanding and the design of
the control strategy indicates that these factors are very low risk.
 UDU :Many control strategy elements which are operator
independent
◦ In-line monitoring of blend homogeneity linked to an automatic system to
stop blending
◦ An easy to read revolution counter.
 Homogeneity is confirmed after the lubrication unit operation,
therefore reduced testing is justified during Stage 3 CPV.
8/6/2015 89

 Decision Process : Risk assessment summary
 Dissolution is largely dependent on
◦ Drug substance particle size
◦ Magnesium stearate surface area
◦ Tablet crushing force
◦ Lubrication time
 Operators can have impact to the equipment set-up
and resulting process parameters and crushing force.
8/6/2015 90

 Decision Process : Risk assessment summary
 Although the powder blend is designed to be free flowing,
variability is estimated during early phases of production with
different batches of excipients and use of operational personnel.
 Continued monitoring of the dissolution, material attributes and
process parameters impacting dissolution is justified during the
first part of the CPV to confirm that the model and predictions
are acceptable.
8/6/2015 91

 Summary of PV stage 3, part 1 Evaluation
 Summary of science and risk based evaluation for the four step CPV risk assessment as applied
to content uniformity and dissolution of oral tablets.
8/6/2015 92
Quality Attribute Content
Uniformity
Dissolution
Risk
Patient
Unit Operations
Material
Operations
Practices
Residual Overall
risk

 Summary of PV stage 3, part 1 Evaluation
 CU
◦ Based on the analysis no additional monitoring is required post
PPQ
 Dissolution
◦ Based on the analysis further evaluation is needed
◦ Additional sampling and testing for dissolution should be
included for period of time until control and capability are
established.
8/6/2015 93

 PV stage 3 , art 1 , Monitoring plan
Summary and Criteria
 Pre–defined to determine what testing could
be reduced
◦ process capability
 e.g. CpK>1with 99 % confidence
8/6/2015 94

 Summary of a monitoring plan for PV stage 3 ,
continued process verification is given in subsequent
table with dissolution testing at heighted level
 This plan focuses on product CQA testing
 However plan can also include
◦ Heightened material CQA’s testing
◦ Process parameter monitoring.
◦ Frequent trending of any and all results
8/6/2015 95

 Monitoring summary for CPV
8/6/2015 96
CQA HMP Rationale Sampling Acceptance
Criteria
Target
min CpK
# of
batches
RATIONALE
Appearance No Routine sampling &
inspection uses AQL
acceptance sampling
Routine Meets
release
criteria
NA NA NA
Identity No Identity confirmed by
multiple tests ( Assay,
dissolution, UDU)
Routine Meets
release
criteria
NA NA NA
Impurities No Impurities controlled in
API. Formulation &
process produce stable
drug products
Routine Meets
release
criteria for
D P
> 1 NA NA
Friability No Adequate in process
control
Routine Meets in
process
criteria
NA NA NA
Hardness No Adequate in process
control
Routine Meets in
process
criteria
NA NA NA

8/6/2015 97
CQA HM
P
Rationale Sampling Acceptance
Criteria
Target
min CpK
# of
batc
hes
RATIONALE
ASSAY No In-process NIR provides
confidence in assay
Routine Meets release
criteria
> 1 NA NA
Uniformity
of Dosage
units
No At line NIR for individual tablet
assay and in pine weight control
data provides high level CU will
meet quality requirements
Routine Meets release
criteria
> 1 NA NA
Dissolution Yes Complex multivariate relationship
requires more data to assess
robustness.
60
tablets/batc
h using
stratified
sampling
Mean > 85 % .
MA’s for API
particle size,
magnesium
stearate surface
area and crushing
force and
lubrication time
monitored.
> 1 NA NA
Microbiolog
y
No Oral ingestion and low water
activity if the drug product
formulation mitigates microbial
quality issues.
Routine NA
Annual Verification
Routine Release
requirements
NA NA

◦ Acceptance criteria for dissolution follows ASTM E
2709 using 90 % confidence interval
◦ AQL : Limiting Quality Level
◦ CS : Control strategy
◦ MA : Material Attributes
◦ CAP : Capability Analysis
◦ HMP : Heightened Monitoring Plan
8/6/2015 98

 Proposed Routine Monitoring Programme
 When pre defined capability measures are achieved as part of Stage
3 , CPV , the levels of testing may be adjusted to statistically routine
levels, which in the case of dissolution involves real time release
testing.
 This level of testing is applied as part of application of statistical
evaluation e.g. capability.
 If RTRT is implemented, frequent monitoring of some of the direct
process measurements, such as blender revolutions, should be
considered.
8/6/2015 99

 Proposed Routine Monitoring Programme
 Pre-defined criteria could be set, such as a defined confidence interval of process
capability CpK > 1 with 99 % confidence which when achieved would drive a
review to decide of and what testing could be reduced to the routine monitoring
level, and what elements of the control strategy could be considered for a change.
 A summary of the monitoring plan for stage 3 Continued Process Verification is
given in previous table.
 This focuses on Product CQA’s testing
 Monitoring plan could also include heightened material CQA’s testing , process
parameter monitoring and frequent tending of any and all results.
8/6/2015 100

 Process under review
 Legacy product : Oral Dissolving tablets
 Process design phase did not include evaluations of the full
range of excipient attributes
 Their possible contribution to variability
 No detailed control strategy developed for the process
 Historical performance review
◦ Potential excipient variability
◦ Impact to assay and and stability
8/6/2015 101

 Initial Stage 3 Monitoring Plan
 Conduct expanded assessment of product and process input
 Identify potential process parameters and/or process and
material attributes for monitoring and and/or enhanced
testing.
 Use conventional risk assessment tools to identify those most
likely to impact process variability and performance.
8/6/2015 102

 Engage Technical, Quality, development, and
Manufacturing personnel
 Apply the plan to a manufacturing campaign
 Obtain statistically valid set of data which
includes process variability conditions and
compile.
8/6/2015 103

 Conduct assessment of the
◦ Initial parameters/Attributes
◦ Level and frequency of testing
 Determine if parameters will be
◦ Maintained
◦ Removed
◦ Added
8/6/2015 104

 First step
◦ Review data from release attributes and known CQA’s over
a given period
◦ Determine current level of variability in the attributes
◦ Review if inputs for the attributes may benefit from
enhanced monitoring.
8/6/2015 105

 Product CQA’s
◦ Assay
◦ Disintegration
◦ Dissolution
◦ Impurities
◦ Content Uniformity
8/6/2015 106

 Conduct a statistical assessment of the the Product CQA’s
8/6/2015 107
Product CQA PpK’s
Assay 0.9
CU 2.66
Disintegration 2.16
Dissolution 2.34
Impurities 5.09

 Product CQA’s
◦ Identify threshold for Statistical Assessment to trigger
enhanced monitoring of additional inputs which may impact
the attribute/parameter.
◦ In this example
 assay data has a lower PpK
 Statistical control limit lies slightly below the product specification.
 Lower PpK indicates lower longer term capability.
 Suggests a need to reduce variability and center the process.
8/6/2015 111

 Product CQA’s
◦ This analysis justifies parameters like tablet
weight, excipient attributes, API particle size and
others to be included in the monitoring plan to
better understand their potential contributions to
assay variability.
8/6/2015 112

 Product CQA’s
◦ Develop a priority matrix. Helps in
◦ Selection of optimal parameters/attributes to
enhance the understanding of the process.
◦ Understanding the impact of input parameters
◦ Develop focus for higher risk elements.
8/6/2015 113

 Product CQA’s
◦ In this example
◦ Inputs were ranked for impact to process outputs
◦ Score used
 1 = Low
 3 = Marginal
 5 = High priority
8/6/2015 114

 Product CQA’s
◦ Similar priority risk assessments were performed for
◦ all process steps & unit operations
 Raw material input as unit operation
◦ Product Quality Drivers
 CQA , Release parameters
◦ Impact to the patient
 Complaints, mouth feel, appearance)
◦ Process Performance parameters
 Yield , Packaging Impact
8/6/2015 117

 Product CQA’s
◦ In this example the particle size, bulk density and
peroxide content of the excipient were selected
◦ These attributes correlated with product
dissolution/disintegration and impurity levels at
release and on stability, respectively.
8/6/2015 118

 Raw Materials
◦ An assessment of Raw Material attributes conducted.
◦ Based on process risk assessment
◦ Sources of variability were identified
◦ Following were identified for Excipient A
 Particle size
 Bulk Density
 Peroxide content
 Potential link to : Dissolution , Disintegration and Impurity
levels
8/6/2015 119

 Raw Materials
◦ A review of specifications was conducted
 “Use” value of peroxide content were significantly
below Internal Specifications
 Internal specifications were above CoA
specifications
 Incoming lots tested within specifications but well
outside of typical experience with the product
8/6/2015 120

 Raw Materials
◦ Monitoring action plans were developed to
 Review supplier capability
 Establish a revised internal specification for
peroxide content
8/6/2015 121

 In –process controls
◦ Review
 Tablet hardness identified for enhanced monitoring
 Linked to complaints : Broken tablets
 Review hardness retrospectively
 Tighten the control on the low end of the specification
 Reduce tablet breakage
 Maintain dissolution / disintegration properties
8/6/2015 122

 Quality System Elements
◦ Review
 Stability
 Warehousing/storage/sampling
 Customer complaints
 Equipment Preventive Maintenance/Calibration
 Deviations
◦ Tablet hardness was selected for enhanced monitoring as
mentioned earlier due complaints of broken tablets.
8/6/2015 123

 Process Performance Assessment
◦ Items identified for enhanced monitoring were evaluated via
statistical control charts
◦ Identify actions when established control chart values are
exceeded.
◦ At pre determined milestones conduct reviews to identify
additional actions
◦ Summary of monitoring plan is given in subsequent slides.
8/6/2015 124

8/6/2015 125

8/6/2015 126

8/6/2015 127

8/6/2015 128

 This presentation is compiled from freely
available resource like the website of FDA, EMA
and publically available literature from websites
of PDA, ISPE and DIA.
 “Drug Regulations” is a non profit organization
which provides free online resource to the
Pharmaceutical Professional.
 Visit http://www.drugregulations.org for latest
8/6/2015
12
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US FDA Process Validation Stage 3: Continued Process Verification

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