Overcoming Disincentives to Process Understanding in the Pharmaceutical CMC Environment - Pharmaceutical Technology

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Overcoming Disincentives to Process Understanding in the Pharmaceutical CMC Environment
Larger and strategic sampling and testing plans can improve process understanding and characterization.

Pharmaceutical Technology

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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