Acceptance Limits for the New ICH USP 29 Content-Uniformity Test - Pharmaceutical Technology

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Acceptance Limits for the New ICH USP 29 Content-Uniformity Test
Revisions to the United States Pharmacopeia's (USP) uniformity test require manufacturers to establish new acceptance limits. The authors present their method for calculating acceptance limits consistent with USP's revised content-uniformity test requirements.

Pharmaceutical Technology

Constructing acceptance limits

The LBOUND calculation derived in the previous section can be used to develop acceptance limits. This is done by first constructing a simultaneous confidence region for μ and σ from the data. If a 90% confidence region is constructed for μ and σ, and the entire region is below the 95% LBOUND, then at least 95% of the samples tested would pass the USP test with 90% confidence.

Construction of the confidence region depends on the sampling plan used to collect the samples. There are two sampling plans that are generally used when testing blends or final product. In the first plan (Sampling Plan 1), a single test result is obtained from each location sampled. For example, in a blending step, a single test result would be obtained from each of a number of different locations within the blender. In a drum, a single test result might be obtained from the different locations within the drum or from each of a number of different drums. For final tablets, a single tablet may be tested from various time points throughout the tableting run. In the second plan (Sampling Plan 2), more than one test result is obtained from each of the sampled locations. For example, during the tableting operation, if a cup is placed under the tablet press at specific time points during the tableting run, several of the tablets from each cup sample would be tested for content uniformity. Sampling Plan 2 allows for the estimation of between-location and within-location variability.

Figure 2. 95% lower bound with 95% simultaneous confidence region for μ and σ.
For Sampling Plan 1, the sample mean and sample standard deviation estimate the population parameters μ and σ. Lindgren gives a simultaneous confidence region for μ and σ (10). The region and the 95% LBOUND are visible in Figure 2, where ULS is the upper confidence limit for σ, and Z is a standard normal critical value.

Once the confidence region is constructed, it must fall completely below the specified LBOUND. One can generate an acceptance-limit table by finding the largest sample standard deviation for a fixed sample mean, such that the resulting confidence region remains below the prespecified LBOUND. Note that the only two points to evaluate on the triangle are the two points with the maximum value of σ.

Table II: Acceptance limits for content uniformity.
Table II provides an example of an acceptance-limit table. SAS program version 8.2 was written to generate the acceptance limits. The acceptance limit table corresponds to a target value of 100% label claim, a sample size of 30, a 95% confidence region, and a 95% lower bound.

Suppose that a random sample of 30 tablets is taken from a batch and tested for content uniformity. Suppose the sample mean is 98.4% label claim with a coefficient of variation (CV) of 3.01%. Since the acceptance limit for the CV is 3.85%, the sample passes. This means that with 95% confidence, any set of tablets taken from the batch has at least a 95% probability of passing the USP test.

For Sampling Plan 2, the variance of a single observation is the sum of the between-location and within-location variances. The standard deviation of a single observation, σ, is estimated by calculating the square root of the sum of the between- and within-location variance components. Graybill and Wang give a confidence region for σ (11).


MS L = mean square between locations from one-way analysis of variance (ANOVA)

MS E = mean square within locations from one-way ANOVA

L = number of locations


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