Limitations of the quality range approach in analytical similarity assessment: Effect of mean shift and relative variability

Abstract

Recently in 2019, the United States Food and Drug Administration (FDA) circulated a new draft guidance for comparative analytical assessment. They suggest the use of quality range (QR) methods. In this article, selection of the k value, and the effect of mean shifts and relative variability are evaluated. These are expressed as a ratio between the two standard deviations of the tested product and the reference product, σT/σR. In a second step, the two modified versions of the QR method proposed by Son et al. (2020) are also analysed under several scenarios, through simulation studies using real data from a biotechnology company and our own data for bevacizumab. Results indicate that k has a great impact on the probability of passing similarity tests. Pass rates higher than 90 % can be achieved for small relative variabilities (σT/σR ≤ 0.6) and large mean shifts (≈4%) by using k = 3. The situation is totally different for k = 2: the pass rate is higher than 90 % for scenarios with small (<0.5 %) or no differences between the means of the two products, but this percentage decreases by up to 50 % for σT/σR = 1. Effectiveness in detecting the various scenarios was quantified by calculating the probability curves of passing the similarity test, as a function of the two variables for each k value. Alternative methods present the same limitations but with different magnitude in comparison with QR, this being most pronounced in the plausibility-interval QR method.