The impact of multiplicity effects in data-based decision making is well known. Some solutions have been devised in other
pharmaceutical disciplines such as the clinical and safety areas as well as genomics and microarray screening (9–11).
Eight fundamental principles for improved decision-making
Eight fundamental principles encourage appropriate planning and evaluation of data to reduce the effect of multiplicity and
to improve decision-making in the CMC environment.
Recognize that an observed result is only an estimate.
Key in-vitro batch parameters are, or should be, associated with clinical and safety properties or characteristics, including
potency and other important quality attributes (e.g., dissolution and preservative levels) as well as the uniformity of the
active ingredient among individual dosage units. The fact is that one can never measure the true value of a parameter without
error. This error, in conjunction with the multiplicity phenomenon, eventually will result in observing units or samples outside
limits. Understanding that the observed result is only an estimate of, and not exactly equal to, the true value is of utmost
importance.
Focus on the reliable estimation of batch parameters.
Adequately addressing today's dilemma requires a fundamental change in philosophy with respect to the goal of data generation.
From a statistical point of view, when fewer data are generated about a parameter, more uncertainty remains. By changing our
philosophy to one of gathering the appropriate amount of information, there is a strong incentive to increase data generation.
More data permit a more precise estimation of the true value of a parameter. The reduced uncertainty in the knowledge of the
underlying batch parameter allows a more informed decision about the acceptability of the level of the associated batch parameter.
When possible, the acceptable range of the underlying batch parameter should be determined on the basis of fitness-for-use.
Furthermore, the requirements on the results collected should be developed to ensure, with high likelihood, that the batch
parameter is within the defined acceptable range.
The acceptance question hereby changes from a simple "Is the observation a go/no-go decision?" to one of "Do I have sufficient
confidence that the true, unknown batch parameter is within acceptable limits?" Increased knowledge of a batch parameter obtained
from additional sampling and testing provides a clearer understanding of true batch quality and is no longer considered a
risk. The risk of failing an acceptance criterion in this new scenario would now be linked to the true batch quality and appropriately
tied to the sample size evaluated. This fundamental philosophical change is necessary to make the FDA vision of improved process
understanding a successful and sustainable reality for years to come.
Understand the role and function of various types of limits.
One must understand the differences among various limits, their appropriate use, and the risk for and consequences of mixing
different concepts. One concern is the growing use of data-driven 3σ control chart limits as specification acceptance criteria
rather than establishing specification limits that are based upon fitness-for-use, thereby ensuring proper performance of
the batch.
According to Tougas, "The approach to setting acceptance criteria for end-product tests is based on a perception of what the
process is capable of delivering, not on what limits are required to ensure performance (safety and efficacy). The net result
is a tendency toward excessive tests and limits that result in excessive producer's risk (i.e., failing test on a batch with
acceptable quality). This results in significant resources expended on activities that do not contribute to the quality of
pharmaceuticals" (12).
The distinction between 3σ limits and acceptance criteria is important, because the appropriate consequences of not meeting
the two types of limits are very different. The data-driven 3σ limits (also referred to as control chart limits) are tools to alert to a change or drift in a manufacturing process. Therefore, the appropriate action is to investigate
a potential special cause and, when appropriate, adjust the process back to its optimum or remove the special cause (13).
On the other hand, acceptance criteria, if correctly defined, are used to ensure batch suitability tied to fitness for use.
When regulatory agencies require a manufacturer to adopt control-chart limits as acceptance criteria, they force the interpretation
of alarm signals as criteria to determine batch disposition. Ultimately, this increases the risk of rejecting acceptable batches.
Manufacturers need a margin between alarm signals and acceptance criteria to institute a fully effective risk-based quality
system.
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