Abstract
The purpose of uniformity of dosage unit test is to determine the degree of uniformity in the amount of drug substance among dosage units in a batch. Recently, there are several nonparametric methods including the large sample counting approach proposed in European Pharmacopeia 8.1 (EU Option 2). All nonparametric methods specify a maximum number of tablets, of which the contents fall outside the interval (85%, 115%) of labeling claim (LC) for a given large sample size. The nonparametric method in European Pharmacopeia requires another maximum number of tablets, of which the contents fall outside the interval (75%, 125%) LC. We denote the nonparametric method as the counting test which will be used in the rest of the article. We focus on the comparison of the acceptance probabilities between EU Option 2 and the parametric two one-sided tolerance intervals (PTIT_matchUSP90) test. Obviously, a counting test is less efficient than a parametric test in general. Our simulation study clearly shows that the EU Option 2 is not sensitive to batches with a large variability in contents which follow a normal distribution with an off-target mean, a mixture of two normal distributions, or a mixture of a uniform distribution with small percent of extreme values. The EU Option 2 is not sensitive to the mean shift of the majority population (97%) from 100% LC to 90% LC. In addition, the EU Option 2 is not sensitive to low assay values (about 90% LC). The EU Option 2 is over-sensitive to one extreme case: 97% tablets with 100% LC and 3% tablets with 76% LC.
This article represents only the authors’ opinion and not necessarily the official position of the US Food and Drug Administration.
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The authors would like to thank two anonymous reviewers for their comments.
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Shen, M., Tsong, Y., Lostritto, R. (2019). Counting Test and Parametric Two One-Sided Tolerance Interval Test for Content Uniformity Using Large Sample Sizes. In: Liu, R., Tsong, Y. (eds) Pharmaceutical Statistics. MBSW 2016. Springer Proceedings in Mathematics & Statistics, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-319-67386-8_2
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