Submitting Advanced Bioanalytical Test Methods for Regulatory Approval - - BioPharm International


Submitting Advanced Bioanalytical Test Methods for Regulatory Approval

BioPharm International

Improvement Criteria

Table 1. Validation Characteristics Per ICH Q2A/B and Relevant Product Specifications
Although many new analytical methods provide faster, more accurate and precise results and somewhat support one of the principles of Process Analytical Technology (PAT) (i.e., immediate test results), current guidelines still lack detailed guidance on how to provide evidence that these new methods are equal to or better than classical or already licensed methods. To provide this evidence, we must define what "equivalent or better" means, and which aspect of all test method performance characteristics should be compared. Once we can clearly define this, we can then demonstrate test method equivalence or superiority. Per International Conference on Harmonization (ICH) Q2A/B guidelines,5-6 the five test method categories can be grouped into two greater categories — qualitative and quantitative test methods. A qualitative test method provides qualitative results (pass/fail, yes/no, or results reported as less than some action or specification level), whereas a quantitative test method provides results reported in real numbers ( Table 1). By definition, qualitative test methods must not be accurate or precise, but they must be specific for the analyte tested and often require the determination of the detection limit (DL). It is critical for qualitative methods to provide high percentages of positive results for positive samples, and high percentages of negative results for negative samples. For qualitative limit tests, a low DL is clearly desirable as it also increases the likelihood for observing positive results even at low analyte concentrations. Quantitative test methods require the generation of data for accuracy, precision, and several other criteria, depending on the type of release specifications and the relevant validation acceptance criteria to be met, in order to be valid and suitable for use (See Table 1).4

Qualitative Versus Quantitative Method Comparability Studies

Table 2. Suggested Statistics to Assess Method Comparability for Each Required Validation Characteristic
Table 2 identifies the validation characteristics and statistical tests a method comparability study should include and provides suggestions on which statistical tests may be appropriate to use for each validation characteristic. All qualitative tests should contain a comparison of hit-to-miss ratios (usually at low analyte concentrations) between both methods. This will somewhat describe the level of specificity of both methods. Both hit-to-miss ratios can be compared using Chi-squared statistics. If the DL is a required characteristic, then both limits should be compared. There is no easy approach to statistically compare DLs unless both DLs were established by the recommended ICH Q2B approach using linear regression statistics. If the candidate's method provides a lower DL, it could be stated in the submission. If the DL is higher, an explanation and justification with respect to release specifications and process control data should be provided.

For all quantitative methods, the method performance characteristics accuracy and precision (intermediate precision) should be compared. Assuming that both methods were properly validated individually, it is a regulatory concern whether results will be expected to change overall by drifting (change in "accuracy") or by a potential increase in day-to-day variance ("intermediate precision"). Depending on a pre-specified allowable difference, a drift in results may require a change in release specifications and would require regulatory pre-approval before the new method can be used for release testing. The demonstration of a method's accuracy will require an evaluation of equivalence between results obtained by both methods. A potential drift in release results can occur in lower or higher results. In most cases, both directions are not acceptable outcomes, and testing for equivalence between methods for accuracy is required.

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