The test system must be properly controlled to ensure reliable release-testing results. System suitability criteria should be established at the end of AMD, usually by running a set of control points. For each test, results are
considered valid if all control points are within established limits. A test system must be able to reproduce measurable results
of a homogeneous sample (control) to allow examination of differences among various product batches.
Sample suitability should be established during AMD and should ideally ensure that samples, controls, and standards are prepared identically
and run simultaneously. In addition, sample suitability should include a statistical analysis of the number of replicates
needed to generate significant release results. Single measurements may be acceptable if the production-process sampling can
deliver truly batch-representative samples and the precision of assay repeatability is high compared to the product specifications
(and therefore high compared to the batch-to-batch variation upon which these specifications are based).
Sometimes, data transformation (for example, logarithmic conversion) may lead to improved linearity for essentially non-linear assay responses. However,
many biological assay response curves are not linear even after transformation.12,13 These are particularly difficult and should only be handled by experienced statisticians. Just as different test methodologies
have different biases, changing statistical models may significantly change the final results. Some models may be inappropriate
or may not provide acceptable results over the entire assay range.
Robustness, the lack of a significant effect when small changes are deliberately introduced into the test system, should ideally be
addressed during method optimization and not as part of AMV. We should know a method's degree of robustness before starting
formal AMV. Critical test system parameters (for example, the acceptable range of diluting the test sample) must be identified
and controlled with appropriate operational limits. These limits should be described in the AMD report and documented in the
method SOP. The SOP contains operational limits within the context of the overall system suitability criteria. These limits
must be adhered to during validation. In addition, robustness should be tested in the AMD phase during or after method optimization
because significant differences in the AMV results may be difficult to explain.
AMV Protocol Acceptance
Why must we set protocol acceptance criteria? One answer is that we always must validate against limits or specifications,
just as release testing results are compared to specifications. Only when all validation results are within established limits
is an AMV considered acceptable (assuming that all limits were reasonably set). In addition, a validation document is a contract
that not only defines in detail the test parameters but also the exact conditions and contingencies (acceptance criteria).
All signing parties agree to the acceptance criteria before the protocol is executed. We must derive appropriate acceptance
criteria so validations that should fail do fail and vice versa. Acceptance criteria should demonstrate to regulatory authorities
(and ultimately patients) that quality systems and production processes are well designed and maintained to ensure consistent
product quality. We also must satisfy acceptance criteria to remain in compliance.14
Deriving reasonable AMV protocol acceptance criteria is one of the most difficult AMV tasks. In general, the most critical
AMV characteristic for the quantitation of excipients, product potency, and impurities in biopharmaceutical manufacturing
is intermediate precision. It provides very valuable information concerning the overall contribution of assay variability
to the observed process variability. Accuracy is often of lesser concern because we are mostly concerned with batch-to-batch
consistency and how well release batches match the purity and impurity levels of the clinical reference batches.6,7
When setting AMV intermediate precision acceptance criteria, one may only know the observed production-process variability.
The best approach is to derive acceptance criteria for intermediate precision from historical process data (observed or expected
batch-to-batch variability and content uniformity) and the product specifications (existing or target). The AMV protocol should
not permit test method variability to be so high that we could face not being able to release product batches with results
that should have fallen within specifications, or vice versa, release product batches with results outside of specifications.14 Establishing an efficient AMV execution matrix has been discussed elsewhere.6,7
In addition to satisfying regulatory requirements, AMV enables reasonable product specifications to be set and helps separate
actual process variability from test method and sampling variability. AMV does not improve a test method but merely provides
evidence for its suitability and the confidence in reported results. The quality of the development work determines the quality
of the test method and, therefore, the quality of the production process and the product.