Intermediate precision indicates how precise test results are on any given day. Intermediate precision should be demonstrated by generating a sufficiently
large data set that includes replicate measurements of 100% product (analyte) concentration. Data should be generated in a
well-designed matrix by several operators over several days using different instruments. If other critical assay elements
(such as different columns in HPLC) are expected to significantly contribute to assay variability, they also should be integrated
into the execution matrix. Different analyte concentrations over the entire assay range can be used to demonstrate intermediate
precision, but the results must be converted to percent recoveries before they can be statistically compared.
Assay specificity is usually ensured by demonstrating insignificant levels of matrix interference and analyte interference. The matrix may
interfere with assay results by increasing the background signal (noise), or matrix components may bind to the analyte of
interest, potentially changing the assay signal. Spiking the analyte into the liquid product and comparing the net assay response
increase versus the expected assay response provides information on potential interference. Other analytes that may be present
in the product matrix should be spiked in proportional concentrations into the matrix (keeping final analyte concentrations
constant). Results of unspiked versus spiked product should be compared.
Linearity of the assay response demonstrates proportionality of assay results to analyte concentration. Accuracy data may be used to
evaluate this parameter. Linearity should be evaluated through a linear regression analysis — plotting individual results
of either analyte concentration versus assay results or observed versus expected results. However, many biological assays
are not linear, even after data transformation (such as logarithmic conversion). The overall fit of the curve for biological
assays within the proposed assay range should not be evaluated by this validation characteristic.
A method's assay range must bracket the product specifications. By definition, the quantitation limit (QL) constitutes the lowest point of the assay
range and is the lowest analyte concentration that can be quantitated with accuracy and precision. In addition to establishing
the accuracy and precision of all analyte concentrations within the assay range, the assay response must also be linear (if
applicable) as indicated by the regression line coefficient.
An analyte's detection limit (DL) is the concentration that yields a response significantly different from a blank or background signal. ICH Q2B suggests
three different approaches for determining the DL. Other approaches may be acceptable if justified.
The QL is the lowest analyte concentration that can be quantitated with accuracy and precision. Since the QL constitutes the beginning
of the assay range, the assay range criteria for linearity (if applicable) must be passed for the particular analyte concentration
determined to be the QL.
It is important to remember that AMV provides the formal evidence that a test method is suitable for use under strictly controlled
QC-testing conditions. The AMV protocol should be setup to deliver this evidence through appropriate acceptance criteria by
varying sample batches, concentrations, operators, instruments, days, and other factors that are expected to vary during routine
testing — within established sample and system suitability conditions and operational limits.6,7
All analytical procedures are associated with bias. It is particularly true for biological assays that test for the purity, potency, and molecular interactions of biopharmaceuticals.
If the product is unique, appropriate reference standards may not be available. The evaluation of the assay's accuracy and
bias can be the most difficult part of the development and validation process. When replacing a method, comparing the results
of the new method to those of the old method is often meaningful only when assay bias is taken into consideration. If we can
compensate for the bias by modifying release specifications, we should be able to properly assess the quality of the process
and the product and remain in compliance. Whenever relative percentages of various analytes are estimated using a single assay,
the response factors must be established and integrated (normalized) into the calculations in order to consistently report
accurate purity and impurity levels.6,7
Samples, standards (secondary, in-house, or working), controls, and critical reagents should be evaluated for degradation during storage and potential freeze-thaw cycles. The negative effects of time on the bench
during actual testing (room temperature), repetitive freeze-thaw cycles, and long-term storage of all materials used to generate
test results should be evaluated and expiration times should be established.