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Vaccines, Blood & Biologics

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Analysis of Rotarix Minimum Release Potency

Phil Krause & Lev Sirota

1. When we look at all the data together, there really aren't enough data points at -- months to understand the stability profile of the product through -- months. It appears that there's a ----- --- between -- and -- months, but the significance or error bounds on that -------- can't be determined. Thus, we don't think the data justify a ----------------- model through -- months, because that may actually ------------ the actual potency at -- months. However, the data through 24 months do justify a ------------- model. However, in analyzing the data, we decided to include the --- month time point, because we think it's biologically plausible that aggregation of virus particles could limit the ability of their potency assay to detect potentially clinically significant degradations in potency (if some proportion of the live virions in an aggregate were to lose their infectivity, this might not affect the result of the potency assay, but almost certainly would influence clinical potency). Thus, regardless of the quantitative analysis, we do not believe the data support a dating period in excess of 24 months.

2. It is clear that it is the manufacturer's responsibility to justify their dating period, and the relationship between release and end-expiry potencies, with appropriate analyses. They have not done so, and thus are leaving it up to us to perform these analyses.

3. Examination of the raw data provided yields a ------------------ log pfu/month, with a standard error of -------- log pfu/month.

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Over 24 months extrapolation, this would yield an expected potency ---- of --- logs, with a standard error of -----.

4. However, this analysis overestimates the expected potency ----, because the studies provided by the sponsor have many missing data points that can skew the ----- calculation. To understand this effect, we also calculated the ------------------ when the data are normalized to the mean potency for each vial (over the individual stability study). In this

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case, the ------------------- with a standard error of -------- (at 24 months, an estimated ----- of ---- logs, with a standard error of ---- logs. This standard estimate is not completely reliable, because it doesn't account for the data normalization (which may influence the degrees of freedom in the statistical calculation, but it is a close approximation-and the actual standard error does not exceed the ---- logs obtained with the raw data calculation).

5. We already know that the assay standard error for a ----replicate assay is ---------, where - is the assay standard deviation of ----. So the assay standard error is -------. Thus, the relationship between end-expiry potency and minimum release potency is:

Minimum release potency = ------------------------------------------------------- ----------------------------------------------

Where expected ---- is ----- logs, assay standard error is ----- logs, and ----- standard error is conservatively estimated at ----- logs.

Plugging these numbers in, minimum release potency ------------------------------------- yielding a difference between minimum release potency and end expiry potency of ---- logs, for a 24 month dating period.

6. This means that vaccine that tests in a ----replicate assay at ---- logs has a 95% chance of exceeding a potency of 6.0 logs at end expiry of 24 months, or that vaccine released at --- logs (the proposed minimum release criterion) has a 95% chance of exceeding an actual potency of ---- logs at end expiry.


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