Global Regulatory Viewpoint.
Richard Poska, Coordinator
]
Corrective Action and Preventive
Action—The “StN” Boundary Line
of Effective Action
J. Ambrose Van Wert
“Global Regulatory Viewpoint” addresses various regulatory
and compliance topics including newly published regulations. This column discusses regulatory information from a
global perspective. The content in this column is useful to
those who deal with pharmaceutical development, expectations for CMC sections of regulatory dossiers, and guidances
for manufacturing, validation, and CGMPs. This issue of
“Global Regulatory Viewpoint” discusses a CAPA approach
to process data.
confirmed failure in the signal or the process owner
chooses to invest in more precise controls to achieve
more exacting control of the processes and resulting
product characteristics.
• Understanding the relationship of process signal-tonoise is key to performing a meaningful and successful
CAPA.
INTRODUCTION
Reader comments, questions, and suggestions are requested to help us fulfill our objective for this column.
Suggestions for future discussion topics are needed.
Readers are invited to submit manuscripts for publication in
this column. Please contact the coordinating editor at
[email protected] for further information.
KEY POINTS DISCUSSED
• A “signal-to-noise” (StN) approach is useful in addressing CAPA investigations of process excursions.
• This approach distinguishes between real process excursions and inherent process noise (variation).
• Process quality control points should reflect an understanding of the StN boundary and deal only with signals based on the process design.
• Key actions for successful CAPA action are proposed,
including containment of impacted product, evaluating design controls, action determination, changing system controls if warranted, and monitoring effectiveness.
• System controls should only be changed if there is a
For more Author
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Corrective action and preventive action (CAPA) is a wellestablished current good manufacturing practice (CGMP)
regulatory concept that focuses on investigating, understanding, and correcting discrepancies while attempting
to prevent their recurrence. The fundamentals of CAPA
have been extensively discussed within the Journal of Validation Technology in the past (1, 2).
The discussion in this column provides a useful approach to CAPA that overcomes a common problem regarding investigations of suspect failures—presuming that
every problem investigation has a unique root cause outside of the process itself.
CAPA is unique among the tools of quality for the simple reason that when a confirmed (real) or suspected (hypothetical) product failure has occurred, the manufacturer must eliminate or mitigate the resulting risk to the
customer. When the failure is real, the solution is conclusive with a well-defined root cause. When the failure
is suspected (hypothetical), however, the solution may
be inconclusive or elusive. The dividing line between real
solutions and statistical possibilities is the signal-to-noise
ABOUT THE AUTHOR
J. Ambrose Van Wert is a pharmaceutical industry consultant with experience in regulatory and GMP issues.
Richard Poska, RPh., the column coordinator, has 20 years of technical experience and can be contacted by
e-mail at [email protected].
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Global Regulatory Viewpoint.
(StN) boundary of a process. What does the StN mean
to the success of a CAPA investigation? Simply this,
successful CAPA investigations derive solutions from
the signal region of a process. This article describes,
from a practical perspective, the underpinnings and
design considerations of any CAPA process and suggests some general actions that can contribute to effective CAPA outcomes.
There are two basic quality control considerations
that underlie effective process management (and consequently the success of a CAPA intervention). The
first involves the use of the StN threshold, the second
is establishing a continual hypothesis test to affirm adequacy of process controls.
Process quality control points should deal only with
real signals above the StN threshold and should be based
upon the process design and supporting technology. Analytical method validation provides an excellent analogy
for conceptualizing the StN point. Those familiar with
analytical method validation recognize the concepts of
limit of detection (LOD) or the limit of quantitation
(LOQ) of analytical methods. The LOD of an analytical
method is the point at which the instrument’s detector is
able to distinguish the presence, but not the exact amount
of, the chemical of interest. The LOQ of an analytical
method is the point at which the detector is able to accurately measure the amount of chemical present. The LOQ
is often several times larger than LOD by design to establish data significance and method sensitivity. Values below
the LOD are unreal, given a specific technology platform,
while those values above the LOQ are real. This fundamental rule also applies to processes.
The quality control verification-points of a process establish a continual hypothesis test of the process design
by analyzing samples removed from the process stream.
Each time the evaluator is determining whether or not
the process is meeting the design target (typically the nominal dose strength among other factors) and that it is statistically stable. An important distinction is to be made
about process management practices. Stable and predictable process results will seldom be achieved when
quality control results are allowed to wander within the
specification range. Accepting “off target” test values,
which meet specification, hides the fact that the process
failed to meet the intended target. Overall quality is degraded for reason of increased variation as the mean is allowed to shift over the specification range. The success of
a CAPA is directly related to the understanding of the two
preceding axioms of process management.
To a great extent, regulator and industry discussions of
recent have become increasingly academic. The current
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approaches to quality overlooks the pragmatic lessons
learned since the 1920s, when statistical controls for economic process management were first introduced. Such
techniques as run charts, histograms, and other simple
tools have lost favor to the more extrapolative and predictive statistical tools. How does academic knowledge
versus pragmatic understanding relate to the application
of a CAPA program? In simple terms, academic knowledge without process understanding often blurs the StN
boundary through data extrapolation and results in imaginary process data signals. Solutions derived from data
below the LOD will surface again when the dataset is reanalyzed from a slightly different point of view. It is not
a question of whether or not the problem will resurface,
but rather when will the problem resurface.
So, what are the key actions that should be followed
to improve the quality of CAPA evaluations? The following five general concepts outline appropriate action:
• Contain the impacted product
• Evaluate the design controls
• Decide on the action
• Change the system controls, ONLY IF WARRANTED
• Monitor new system controls for effectiveness.
CONTAIN IMPACTED PRODUCT
IMMEDIATELY
Two situations define the extremes of CAPA activities. The
first is what could be termed internal capture problems
and the second external capture problems. Internal capture issues relate to problem product still within the manufacturer’s control, whereas external capture are problems
identified through patient experience. In either situation
the primary objective is to protect the patient from unnecessary risk. Beyond protecting the patient, a secondary issue is to develop meaningful data to assess the extent of the problem. The better of the two situations is
the internal capture where the organization is fortunate
enough to have control of the product before it is distributed to the market. Internal capture provides a luxury of
time to develop and perform a controlled analysis of the
problem using pre-defined statistically based sampling
plans. Internal capture is the most certain way to protect
the interests of the patient as the CAPA evolves. With
records at hand and a well-conceived sampling plan in
place, a highly credible action plan can be developed.
External capture is the more challenging of the two situations as product is in the market and being used. Gathering data is a challenge that should be discussed as a risk
contingency plan. Response must be rapid and decisive.
Sampling for meaningful data is limited to product that
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Global Regulatory Viewpoint.
has not been distributed or data that must be gathered
from product removed from the market. Withdrawal is
an effective but costly way to assure only safe and effective product remains in the market. In either an internal
or external capture issue the CAPA must derive reliable
data sets to perform analysis.
In either situation, the manufacturer must understand
with certainty why the quality control system failed to
identify the situation as it developed.
EVALUATE THE DESIGN CONTROLS
Data analysis during a CAPA evaluation should consider
boundaries of practical possibilities to define what the
problem is but equally important, what the problem is
not. This concept is the Kepner-Tregoe (3) technique for
problem solving. Bounding the problem helps to clarify
the StN point. The data must be evaluated objectively
and without bias, and the original process quality controls must be reconsidered. The investigation should focus
upon the inherent precision and accuracy assumptions
of the process quality controls rather than relying on finished product specification limits. Four areas of the
process, at a minimum, are key in design control analysis. The four areas of review include weighing and dispensing operations (pharmacy), processing and process
controls (production), assessment of process sampling,
and finally the examination of laboratory analytical results. The review should re-evaluate the cumulative impact of the precision and accuracy of the current systems
in an effort to determine if the CAPA solution lies in the
signal or noise region of the process. For example, if a
process dispenses 10 kilograms of active per lot using a
weigh scale with a +/- 0.5 kilograms precision, the process
has a theoretical 5% maximum error depending on the
size of the lot. If the CAPA seeks an answer with precision of 2% no meaningful CAPA outcome can be derived,
as the solution would be in the noise portion of the process
range. If the 5% error is not acceptable, as proven by clinical study and included in the initial process design, then
a new, more precise scale, such as +/- 0.02 kilograms could
be purchased to reduce the error and noise of dispensing.
This is an economic consideration of the quality control
system, which is the sole responsibility of management.
DECIDE ON THE ACTION
The objective of this step is to assess the correlation of the
data and facts to the final decision and act first in favor
of patient safety. The decision is complicated in today’s
business climate for the risk of being taken to court. What-
ever the final outcome, the resulting decision must be
consistent with scientific facts generated throughout
the product’s lifecycle including non-clinical data, clinical data, and commercial products test results in addition to the special CAPA data associated with the
problem investigation.
CHANGE THE SYSTEM CONTROLS,
ONLY IF WARRANTED
The CAPA must consider the previous analyses to determine if a product defect is process noise or process signal; otherwise, the solution generated by the CAPA will
not resolve the problem. The system controls should only
be changed if there is a confirmed failure in the signal or
the process owner chooses to invest in more precise controls to achieve greater precision of the processes and products produced by the process. It should be pointed out
that effective quality control systems are simple, not complex. The process owner must keep the quality control
system simple, for example using “Yes or No” decisions
to quickly identify the point in time where the process
target has shifted. Action can be taken in a timely manner and will be proximate to the cause-and-effect point
(where the data is most reliable). Given current technology, every CAPA “problem” may not have an absolute solution. Care must be taken to avoid CAPA “problem solutions” that result in more noise and less signal as the
result of extrapolated process data sets. It is difficult at
best to achieve parts-per-million results using parts-perthousand controls.
VALIDATION AND MONITORING OF
NEW SYSTEM CONTROLS
Assuming that new controls are warranted as the result
of an effective CAPA, the loop must be closed by followup assessment at a meaningful and timely point in time.
The new controls must be integrated and monitored for
adequacy. The company should not overlook the CAPA
solution as an opportunity to simplify the controls or
even institute less glamorous quality tools such as a run
chart. A proper solution for a “noise” issue could involve
incorporation of long-term monitoring of current controls to overcome the limitations of short-term sampling
of individual lots. Long term monitoring is a simple, yet
effective way to visualize a trend before problems require
the use of a CAPA due to a process failure.
JOURNAL
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Global Regulatory Viewpoint.
CONCLUSION
REFERENCES
Understanding the relationship of process StN is key to
performing a meaningful and successful CAPA. Lot sampling for release often times does not have adequate precision to identify the long term trends and issues. Older
tools such as a run chart and histogram will overcome the
limitations of isolated, lot-based quality control sampling
of the process. Application of the five considerations listed
previously to problem definition and problem investigation will make CAPA more effective and proactive in identifying those attributes critical to consistent quality. As the
old adage holds, a problem well defined and properly
bounded is half solved. The challenge of all CAPA investigations is to recognize that statistically derived problems
at or below the LOD will have an infinite number of solutions. It is up to the CAPA investigation to sort out the
signal from the noise in order to effectively resolve a problem and improve the overall outgoing product quality.
1. Nold, L., “Be SMART with Your Corrective and Preventive
Actions (CAPA),” Journal of Validation Technology, Vol. 7, No.
3, April 2003.
2. Bodea, G., “What Companies Should Know and Consider
When Designing a CAPA System, Part I,” Journal of Validation
Technology, Vol. 11, No. 3, April 2007.
3. Kepner, C. and Tregoe, B., The Rationale Manager, A Systematic Approach to Problem Solving and Decision Making, Library
of Congress 65-21586, 1976. JVT
ARTICLE ACRONYM LISTING
CAPA
CGMP
LOD
LOQ
StN
Corrective Action and Preventive Action
Current Good Manufacturing Practice
Limit of Detection
Limit of Quantitation
Signal-to-Noise
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