Strategies for Validation Extensions - It is essential to understand the critical elements of validation extensions to ensure accurate process or product quality measurements. - BioPharm International


Strategies for Validation Extensions
It is essential to understand the critical elements of validation extensions to ensure accurate process or product quality measurements.

BioPharm International

The successful completion of analytical method transfer (AMT) is a regulatory expectation for the extension of the validation status to other laboratories. The demonstration of equivalent test results and, therefore, an acceptable level of reproducibility when testing at a different location can limit the potential risk to the patient. Acceptable reproducibility also limits the risk of failing test results for the biopharmaceutical firm because established probabilities of passing specifications can be maintained. Similar, postvalidation changes in method components should be monitored and controlled to avoid significant (negative) changes for material or product release probabilities.

Analytical method validation (AMV) guidelines exist from several recognized sources.1–5 Detailed validation guidelines for alternative microbiological test methods also exist.6–7 In addition, a series of practical tips and discussions for AMV and related topics was recently published.8 However, some topics are currently not sufficiently covered in recognized sources. For example, how can we demonstrate method comparability for new methods, extend the validation status onto other laboratories or other test method components, and maintain this validation status over time? What are acceptable levels for differences in method performance and when is a method no longer suitable? This article discusses practical concepts on how to ensure successful validation extensions.

When replacing approved test methods with improved ones, analytical method comparability (AMC) data should be submitted together with the method description and validation results.8 Once a method is approved and in routine use, it should be maintained in an analytical method maintenance (AMM) program that can be administered through the validation master plan (VMP).8 If done well, this will ensure—like all postvalidation activities—consistent (accurate and precise) production process and product quality measurements.8 What exactly are the critical elements of good validations, validation extensions, or suitable validation maintenance? The answer lies mostly in the preset acceptance criteria for method performance and, of course, the actual validation results obtained. For example, if changes in analytical method components cause a change in test results and, therefore, in process or product quality measurements, we should capture when we will have exceeded method suitability limits. In other words, if the analytical method change will cause a predictable shift or spread of results with respect to specification(s), and therefore negatively impact the probability of releasing material, we should monitor this. To monitor and possibly compensate, we must first set reasonable suitability limits, then continuously control the overall method performance. Often, the most difficult part may be to estimate the associated risk of changed results with respect to both patient and firm, and from this, to set reasonable acceptance criteria. Once we truly understand why and when there will be a need for method improvement, we will likely know what should be done to compensate for the difference.

Each production process has an associated probability for the rate of rejections that can be readily calculated by relating specifications and production process performance. However, instead of having to deal with only two probabilities (pass or reject) that are "visible" and monitored by statistical process control (SPC), we should consider two additional possibilities for all reported results. Therefore, there is a total of four possible cases for releasing product or material, of which three should be avoided as often as practically possible. The four cases for reported test results are illustrated here.

Measured results are within established specifications.

  • Case 1A: results are true.
  • Case 1B: results are not true.

Measured results are outside established specifications (OOS).

  • Case 2A: results are true.
  • Case 2B: results are not true.

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