Risk-Based Thinking in Process Validation - Pharmaceutical Technology

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Risk-Based Thinking in Process Validation
The author describes why statistical significance would impose an unreasonable burden on manufacturers.

Pharmaceutical Technology
Volume 35, Issue 2, pp. 68-76

Validation as the scorekeeper

Personnel often blame the validation exercise when a process fails to meet its specified requirements. That blame is completely misplaced. Inadequacies in process-validation exercises are not associated with an inadequate number of batches as much as they are associated with inadequate science behind the process. Validation by itself is nothing more than an independent assessment of the inherent capability of the process. Just as one cannot test quality into a product, one cannot validate it in, either. FDA's draft guidance outlines a means for product quality and process reliability through reliance on sound science during process development. To the extent that Stage I is properly executed, process robustness is largely assured. The development activity seeks to gain knowledge about the product that will ensure its success in the clinic, and about the process that will ensure its suitability for that purpose. The later stages of the guidance outline means to transfer that knowledge initially into a commercial manufacturing environment and then support it throughout its time in the market. When firms fail to gain adequate knowledge initially and maintain it over time, they are likely to encounter quality difficulties. The validation approach outlined in the guidance is intended to remedy that problem by mandating increased process understanding. Applying rigorous validation acceptance criteria or defining success criteria without adequate knowledge of the process or product capability misses the point entirely.

The premise of this article (and FDA's draft) is that as the firm gains process knowledge and applies it appropriately, the level of risk is reduced. Although QbD activities could be construed to be required to determine the independent parameter–dependent attribute relationship, the QbD exercise does not start with elemental science. Individuals will draw upon their educational backgrounds, and firms will rely on their prior efforts as the foundation upon which the new process and product is built. When that knowledge is extensive, the amount of new work required in the QbD exercise, and later in the commercial demonstration, should be reduced. Similarly, when the core process is simple, such as in the preparation of a solution, the amount of QbD or commercial-scale redemonstration of it should also be executed with less effort. The more knowledge a firm possesses, regardless how it has been acquired, should reduce the amount of new effort necessary in QbD or commercial-scale manufacturing. A well understood underlying process can serve to reduce the QbD and commercial-scale activities. Greater knowledge should lead to reduced risk.

The intent of this effort is to foster a dialog between industry and regulators that results in a shared understanding of regulatory expectations. The adoption of any specific value is not the intent of this proposal: the goal is to initiate communication that results in common ground on this subject, basing it on a risk-based model.

Additional risk considerations

Table II: Risk categories for nonproduction processes.
Validation of processes extends well beyond the direct production processes used for drug substances and drug products. The application of risk-based thinking in those activities makes sense for much the same reasons as it does for production processes. Extending the performance qualification for these processes beyond what already appears to be fully validated processes, however, has little apparent merit. For example, increasing sterilization validation, which is clearly an essential and critical process, beyond the current three-study expectation would not provide much benefit. The absence of validation-related problems with respect to sterilization across the industry suggests that added studies are not required. This result is in large part due to the robustness of the science applied to sterilization and the certainty of the operational controls. Considering the spectrum of nonproduction processes that require validation, those with greater risk are those with substantial quality implications where the underlying science is limited or process controls are less effective. At the other end of the spectrum are processes with minimal impact or with well defined and robust process controls. Thus, nonproduction validated processes might fall into three major risk categories (see Table 2).*

*The categories of risk are associated with both patient safety considerations and process robustness (e,g., sterilization processes are high risk to the patient, but predominantly easily and reliably validated).

The number of validation studies, the validation approach (i.e., concurrent or prospective), and, perhaps most importantly, the number of supportive background controls should all be dictated by the level of risk associated with the particular process. The author's suggestions are intended to provoke interaction rather than serve as definitive positions on the subjects.

One further concern relative to nonproduction processes bears repeating. FDA's draft guidance made no distinction between the direct and indirect processes within our industry. Although process validation may have derived from sterilization issues in the 1970s, the thrust of the 2008 draft guidance is heavily skewed towards direct production processes, and the document scarcely mentions the supportive processes, however important they might be. FDA should take a definite stance on the inclusion or exclusion of these supportive processes and system with respect to their final guidance (8). These processes would benefit from the same type of risk analysis outlined in Table I. The diversity of processes, however, would make consensus examples impractical, given the uniqueness of the individual processes.


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