Demonstrating Comparability of Stability Profiles Using Statistical Equivalence Testing - The authors present an approach for testing statistical equivalence of two stability profiles. - BioPharm

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Demonstrating Comparability of Stability Profiles Using Statistical Equivalence Testing
The authors present an approach for testing statistical equivalence of two stability profiles.


BioPharm International
Volume 24, Issue 3, pp. 36-42

A TEST OF EQUIVALENCE WITH STABILITY DATA

The primary purpose of the equivalence test with stability data is to demonstrate that the difference in process slopes is less than the practically important threshold defined by the EAC. The steps employed in a test of average equivalence of slopes with stability data are the same steps used to demonstrate average equivalence of two process means. In particular, the three steps used to perform the average equivalence test of slopes are listed below.

1. Establish the EAC. This is the difference in process slopes thought to be of practical importance.

2. Determine the experimental design and required sample size to ensure acceptable type 1 and type 2 error (Refer to section "Step 2: Study design and determining sample size" for further discussion).

3. Perform the test of equivalence after the data are collected and interpret the results.

An example is now presented to demonstrate each of these three steps.

Step 1: Setting the equivalence acceptance criteria


Figure 2. Four stressed stability lots (A, B, C, and D) from the historical process.
The example considers a process in which four historical lots have been placed in a stressed temperature condition. In Figure 2, the response is measured over a specified length of time that is the same for each of the "typical" historical lots. The measured response is a purity measurement denoted as Y in percentage units on the vertical axis. The least squares slopes (in percentage of purity per month) were computed individually for each lot and are shown in Figure 2. Note that even for the same process, there is variation among the slopes across lots.

For stability data, an EAC represents the largest acceptable difference between the average slopes of the historical and new processes. It is recommended that the EAC for a specific attribute be derived from the following considerations:

  • Scientific knowledge of the critical quality attributes and the impact of degradation over time
  • Process understanding of stability data from material with clinical exposure
  • Variability among the lot slopes of the historical process (e.g., the range of the historical slopes in Figure 2 varies from –1.53% per month for lot A to –0.844% per month for lot D.)

In the present example, the EAC is established at 1% per month. To demonstrate equivalence, the true average slope of the new process cannot differ from the true average slope of the historical slope by more than 1% per month.


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