Transcript GMTP in the QC Laboratory (Good Manufacturing Testing

Statistical Tools, Performance Verification
Presented by: Karen S. Ginsbury
For: IFF
February 2011
Tools
• Statistics is a science pertaining to the
collection, analysis, interpretation or
explanation, and presentation of
valuable / useful data where the
decision regarding what is collected is
made up front.
• Process Validation uses statistics,
sampling and testing to predict process
variability / uncertainty
Definitions
• Statistics is a mathematical science
pertaining to the collection, analysis,
interpretation or explanation, and
presentation of data
• Statisticians improve the quality of data
with the design of experiments and survey
sampling
• Statistics provides tools for prediction and
forecasting using data and models
What is a confidence level
• Confidence Level is the likelihood - expressed as a
percentage - that the results of a test are real and
repeatable, and not just random
• The idea is based on the concept of the "normal
distribution curve," which shows that variation in
almost any data (such as the heights of all fourthgraders, or the amount of rainfall in January) tends to
be clustered around an average value, with relatively
few individual measurements at the extremes
• A confidence level of 50% means there is a 50:50
chance that your result is WRONG
• 75% means that one in four results will be WRONG
• In pharma industry we usually want a minimum
confidence level of 95% and that helps in selecting a
sampling plan
Probability
• Probability, or chance, is a way of
expressing knowledge or belief that an
event will occur or has occurred
• Statistics is a means of assessing or
predicting probability
• At the process validation stage of product
development we have a lot of uncertainty
and wish to increase the probability of
success through process understanding
Statistical Based Sampling
Plan: From the 2008 Guide
• Protocol should address the sampling plan
including sampling points, number of samples, and
the frequency of sampling for each unit operation
and attribute
• The number of samples should be adequate to
provide sufficient statistical confidence of quality
both within a batch and between batches
• The confidence level selected can be based on
risk analysis as it relates to the particular attribute
under examination
• Sampling during this stage should be more
extensive than is typical during routine production
Acceptance Criteria
• Criteria that provide for a rational
conclusion of whether the process
consistently produces quality products.
The criteria should include:
A description of the statistical methods
to be used in analyzing all collected
data (e.g., statistical metrics defining both
intra-batch and inter-batch variability)
Acceptance Criteria - Variability
• Critical Quality Attributes ?
(Product Specification)
• Critical Process Parameters
• Trends
• Inter and Intra-batch variability:
–
–
–
–
Paired/ unpaired t-test
Shewhart control charts
Upper and Lower control limits
Process capability
Process Performance Qualification
Typically will include:
– Commercial batches manufactured with the
qualified utilities, facilities, production equipment,
approved components, master production and
control record, and trained production personnel in
place.
– Usually run at target/nominal operating parameters
within proven acceptable range or design space.
– Extensively tested, i.e., combination of samples
analytically tested and increased process control
monitoring beyond typical routine QC levels.
Process Performance qualification (PPQ)
• A series of tests which confirm that the
system or process does perform consistently
and predictably and results meet
predetermined specifications
• PQ documents that:
– processes operate as required at the normal
operating limits of critical parameters
– systems operate consistently and reliably
– appropriate challenges are employed
The Guide:
Continued Process Verification
• An ongoing program to collect and analyze
product and process data that relate to product
quality must be established (§ 211.180(e)
• Data collected should include relevant process
trends and quality of incoming materials or
components, in-process material, and finished
products
• The data should be statistically trended and
reviewed by trained personnel
• The information collected should verify that the
critical quality attributes are being controlled
throughout the process
The Guide:
Continued Process Verification
• We recommend that a statistician or
person with adequate training in
statistical process control techniques
develop the data collection plan and
statistical methods and procedures used in
measuring and evaluating process stability
and process capability
The Guide:
Continued Process Verification
• Procedures should describe how trending and
calculations are to be performed
• Procedures should guard against overreaction to
individual events as well as against failure to
detect process drift
• Production data should be collected to evaluate
process stability and capability
• The quality unit should review this information. If
done properly, these efforts can identify variability
in the process and/or product; this information can
be used to alert the manufacturer that the process
should be improved
The Guide:
Continued Process Verification
• Good process design and development should
anticipate significant sources of variability and
establish appropriate detection, control, and/or
mitigation strategies, alert and action limits
• However, a process is likely to encounter sources of
variation that were not previously detected or to which
the process was not previously exposed
• Many tools and techniques, some statistical and
others more qualitative, can be used to detect
variation, characterize it, and determine the root cause
• We recommend that the manufacturer use
quantitative, statistical methods whenever
feasible
The Guide:
Continued Process Verification
• We recommend that it scrutinize intra-batch as
well as inter-batch variation as part of a
comprehensive continued process verification
program
• We recommend continued monitoring and/or
sampling at the level established during the
process qualification stage until sufficient data is
available to generate significant variability
estimates
• Sampling and/or monitoring should be adjusted to
a statistically appropriate and representative level
with process variability periodically assessed
Practical Implications and
Applications in Process Validation
• process average and process variability
estimates used for determination of
appropriate specifications
• Average:
– how many batches
– Moving average or once determined and
that’s it?
Practical Implications and
Applications in Process Validation
• Suitable Statistical Methods:
– Sample size (how many units from a total
population) needs to be tied in with
confidence level
– Representative sample: what do we mean:
• beginning / middle / end?
• n +1
• MIL STD
Preparing for PPQ
• Activities and studies resulting in product
understanding should be documented
• Documentation should reflect the basis for
decisions made about the process
– e.g. manufacturers should document the
variables studied for a unit operation and the
rationale for (the controls exercised over)
those variables identified as significant
• This information can be used during PQ
Process Qualification questions
• Does running three batches of a product or
three processes mean that that process is
valid? …Does it mean the process is
effective?
• Can you explain why what you do provides
assurance that the process will produce the
same result each time it is run? …or that
the process is under control?
Process qualification questions
• What are the process variables? …the
things that will cause the process
outcome to vary.
• Are these variables understood and
adequately controlled?
Transition: PQ to Ongoing
Verification
• Prepare a summary report for PQ
• Report is basis for ongoing protocol
• Risk assessment focuses on
“uncertainty”
from stages 1 and 2
Transition: PQ to Ongoing
Verification
• Select CQAs and CPPs for increased
scrutiny:
– CQA’s = tests
– CPP’s = data analysis
• Statistician:
– # of runs
– # of samples
– Confidence level
Why is Continued
Process Verification Needed?
How much do we know after we
have completed the Performance
Qualification lots?
– Answer: Only a fraction of what we
will know over the course of time.
Continued Process Verification(CPV)
• On-going monitoring of the commercial
process to demonstrate that it remains in
a state of control
• Systems for detecting unplanned
departures from the process are
essential to accomplish this goal
CPV approach
• Develop a rationalized continued
process verification strategy
• The extent of verification and the extent
of documentation should be based on
risk to product quality and patient
safety, as well as the complexity and
novelty of the manufacturing system
Variation Detection
Sources of feedback
Goal: Improve and Optimize the Process
• Complaints
• Out-of-specification reports
• Process deviation reports
• Process trending
Variation Detection
Sources of feedback
•
•
•
•
Batch records
Incoming raw material records
Equipment and utility monitoring
Production line operators/Quality staff
interviews
• Operator error trending
FDA Guidance on Sampling
• Continued monitoring and/or sampling at the
level established during the process
qualification stage until sufficient data is
available to generate significant variability
estimates.
• Once the variability is known, sampling and/or
monitoring should be adjusted to a statistically
appropriate and representative level.
• Process variability should be periodically
assessed and sampling and/or monitoring
adjusted accordingly.
Maintaining Equipment Qualification
• Once established, equipment qualification
status must be maintained through routine
monitoring, maintenance, and calibration
procedures and schedules (21 CFR part 211,
subparts C and D).
• The data should be assessed periodically to
determine whether re-qualification should be
performed and the extent of that requalification.
Process Changes
• Data gathered during continued process
verification might suggest ways to improve
and/or optimize the process by altering some
aspect of the process or product such as:
– the operating conditions (ranges and setpoints)
– process controls
– manufacturing instructions
– component, or in-process material
characteristics
Process Changes
• If so, document:
– A description of the planned change,
– a well-justified rationale for the change,
– an implementation plan, and
– quality unit approval before implementation
• Depending on the significance to product
quality, modifications may warrant performing
additional process design and process
qualification activities.
CPPs and CQAs and Statistics
• Analyze the data for CPPs for
(representative) batches and tie in with
data for CQAs
• It is about converting data into knowledge
• i.e. how do CPPs affect CQA’s (if at all)
How it works
• Very basic statistics:
• Average =
• Standard Deviation =
How it works
• Very basic statistics:
•
•
•
•
•
Specification vs Control
Upper Specification Limit (USL)
Lower Specification Limit (LSL)
Upper Control Limit (UCL)
Lower Control Limit (LCL)
Process Capability
• Can it work?
• Will it work?
• Will it always work?
Process capability compares the output of an incontrol process to the specification limits by using
capability indices. The comparison is made by
forming the ratio of the spread between the process
specifications (the specification "width") to the spread
of the process values, as measured by 6 process
standard deviation units (the process "width")
Process Capability
Process capability compares the output of
an in-control process to the specification
limits by using capability indices
The comparison is made by forming the
ratio of the spread between the process
specifications (the specification "width") to
the spread of the process values, as
measured by 6 process standard deviation
units (the process "width")
Process Capability
Process Capability
• Most capability indices estimates are valid
only if the sample size used is 'large
enough'. Large enough is generally
thought to be about 50 independent data
values
Process Capability
Process Capability
• The idea is to push your process closer to
the mean and to have the mean in the
middle of the USL and LSL i.e.
REDUCE VARIABILITY
Example – Comments ??
Selection of Methods
• A description of the statistical methods
to be used in analyzing all collected
data (e.g., statistical metrics defining both
intra-batch and inter-batch variability
• Various choices:
– T test (paired or unpaired)
– Shewart control charts
Now it is worth speaking to a statistician
Select method upfront and include in protocol
Using Statisticians
• FDA says there has always been a
requirement for the use of statistics in
pharmaceutical manufacturing and control
and especially in process validation
• We don’t necessarily have to go to levels
where we need statisticians
• Probably every company should have a
consultant statistician on – hand for tricky
questions
How Much Sampling and Statistics
• We recommend continued monitoring and /
or sampling at the level established during
the process qualification stage until sufficient
data is available to generate significant
variability estimates
• The Product Control Strategy should
establish appropriate sampling levels and
process validation should demonstrate that it
works. How much sampling is going to be
expected?
What About R&D Data
• Will data from pilot runs be acceptable as
constituting some of the process validation
data and would that mean that in some
cases, if adequate scientific evidence is
available – less than three commercial
batches might be acceptable or
concurrently released batches ?
Continuous Improvement: Implementing Change
to Minimize Unintended Consequences
• Every change has the potential to invalidate your
validation
• Every change has the potential to result in nonconforming product
• Therefore
sufficient initial validation to fully understand the
particular equipment item: the strengths and
weaknesses and those areas where particular
care is needed before, during and after making
change
new paradigm
Do and only do what is necessary…
…to assure that the process is under
control and will produce quality product
each time.
…validation is not an event, but a
continuous process
And in Conclusion
• The Process Validation Guide makes it clear that
industry can no longer sit back with “3 batches
and I’m done”
• The guidance has far-reaching implications for
industry particularly upstream (product
development)
• Drug product manufacturers would do well to
familiarize themselves with 21CFR 820 – QSR
for medical devices – sections on design
controls