Several gaps in current regulatory guidelines that govern the analytical method life cycle for the testing of biopharmaceuticals
are identified. Strategic guidance on how to monitor and control the life cycle of an analytical test method is provided in
this article. Analytical method transfer, analytical method component equivalency, and analytical method comparability protocols
are discussed in light of risk-based strategies for validation extensions. The use of an analytical method maintenance program
is suggested to control over time the predictable risk to patients and firm.
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 (hence the regulatory expectation). Acceptable
reproducibility also limits the risk of failing test results for the biopharmaceutical firm as 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.1–6 Detailed validation guidelines for alternative microbiological test methods also exist.7–8 In addition, a series of practical tips and discussions for AMV and related topics was recently published.9–15 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
This two-part series focuses on all postvalidation work that may be required to ensure process and product quality over time.
This article discusses practical concepts on how to ensure successful validation extensions. Part II, to be published in the
October issue, will include practical tools to ensure a validation continuum (maintenance) for validated methods. The second
part will also include case studies for deriving meaningful and risk-based acceptance criteria for validation extensions and
validation maintenance and will, furthermore, include a case study on how to reduce analytical variability in validated systems.
When replacing approved test methods with improved ones, analytical method comparability (AMC) data should be submitted together
with the method description and validation results.13 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).14 If done well, this will ensure—like all postvalidation activities—consistent (accurate and precise) production process and
product quality measurements.14 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.