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Question:
What are the differences between the ICH guideline, Q2(R1) and the updated version, Q2(R2), that would be identified in a gap assessment of an analytical procedure validation study which was originally performed following version Q2(R1)?
Answer:
Requirements regarding the experiments associated with validation studies that are described in ICH Q2(R1) consist of numbers of concentration levels and numbers of replicates for the various performance characteristics, e.g., a minimum of 5 concentration levels is directed for the evaluation of linear relationships. These experimental requirements have supported validation studies from the original publication of the ICH Q2 guideline and are still considered to be sufficient. Therefore, there was a deliberate decision by the ICH working group not to change the experimental approach when the guideline was updated. This means that additional experiments should not usually be required on completion of a gap assessment.
However, it is still essential to assess whether the original validation was performed correctly, in terms of the experiments that were carried out, to identify if there are any gaps. For example, it is clear in the guideline that: “Accuracy should be demonstrated under regular test conditions of the analytical procedure (e.g., in the presence of sample matrix and using described sample preparation steps).” Thus, the replicates prepared in these experiments should be of the full procedure from beginning to end, and not merely replicate measurements of the same sample test solution. Similarly, other characteristics should be assessed to ensure that they were addressed correctly in the original validation study.
Interestingly, unlike in Q2(R1), no minimum number of replicates is indicated for accuracy in Q2(R2), just: “Accuracy should be assessed using an appropriate number of determinations and concentration levels covering the reportable range (e.g., 3 concentrations/3 replicates each of the full analytical procedure).” The reason for this seeming relaxation of the requirements is related to the major change that would be identified during a gap assessment, the statistical expression of the results.
For both accuracy and precision, a new requirement in ICH Q2(R2) is that the results should be expressed in terms of confidence intervals, which are then compared to the acceptance criteria, rather than the ‘point estimates’ that were acceptable when following ICH Q2(R1). For accuracy, the confidence interval of the mean should be calculated and for precision, the confidence interval of the standard deviation (the upper confidence limit of the %RSD will be of most interest in terms of determining if the data is acceptable). In the calculation of a confidence interval the number of data points will influence the range of the interval and an increase in the number of data points will result in a smaller interval. Therefore, it is in the interests of the experimenter to make sure that a suitably large number of data points are generated.
It is undoubtedly a more scientific and statistically valid approach to use confidence intervals instead of point estimates, but the calculations may be unfamiliar for some validation practitioners and thus require some additional statistical knowledge to comply with this new requirement. Additionally, the training modules provided by ICH provide some useful guidance on what to do in situations where the point estimate meets the acceptance criteria, but the confidence interval does not.
It may be useful to note that in Annex 2 of ICH Q2(R2), illustrative examples of approaches to validation are provided for a selection of technologies. The examples are not intended to be mandatory but for some technologies there are some recommendations for minimum numbers of concentration levels and replicates, e.g., quantitative PCR and biological assays, that are in excess of the numbers provided in the main guideline and are more typical for these types of technologies.
The evaluation of linearity has been moved in the new guideline to be a sub-heading of Range, now entitled, ‘Linear Response’, where it is joined by sections on ‘Non-linear Response’ and ‘Multivariate Calibration’. This rearrangement is certainly an improvement but should not result in identification of any gaps when compared to ICH Q2(R1). However, it is perhaps open to interpretation whether a residuals plot is required: “An analysis of the deviation of the actual data points from the regression line is helpful for evaluating linearity (e.g., for a linear response, the impact of any non-random pattern in the residuals plot from the regression analysis should be assessed).” The phrase ‘is helpful’ does not appear to imply that it is mandatory, but in the previous guideline it was ‘may be helpful’. I would always advise that a residuals plot is generated to support that there is a linear relationship.
In conclusion, in most cases, a thorough validation study which complies with ICH Q2(R1) is unlikely to have gaps relating to experimental requirements when assessed against ICH Q2(R2) but it is very likely that additional statistical evaluation of the results will need to be performed, in particular the calculation of confidence intervals for both accuracy and precision.
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MTS offers a short course, ‘What’s New in ICH Q2(R2) and Q14?‘, which fully explores the updates to Q2 in terms of the additional information that has been introduced, and the expected benefits, the potential additional validation requirements and associated gap assessment, when compared to previous requirements, and also to provide an overview of the contents of Q14 and how this new guideline relates to Q2(R2).
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