Abstract
Some drugs are intended for sequential storage under two different storage conditions. If the data for each condition are analyzed separately, predicting assay and other responses after T1 months at one condition followed by T2 months at the other condition is non-trivial for several reasons. First, the two analyses will give different intercept terms. What should one do about that? Second, how would one calculate the confidence limits for combined storage? Third, what if prior storage at one condition affects the slope at the other condition? This paper proposes a simple ANCOVA model containing two slope terms, one for each storage condition. When multiple batches and/or packages are involved, it is easily generalized to two sets of slope terms. Confidence limits are straightforward and can be calculated using existing commercial software. With properly designed data, one can test whether prior storage at one condition affects the slope at the other condition. If no such effect is significant, very useful extrapolations can be made. Temperature excursions, model reduction and curvilinear dependencies are discussed.
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Notes
“Worst” is defined as the combination with the highest upper confidence limit at that particular time point. This is slightly more conservative than choosing the upper confidence limit for the worst combination at the target shelf life, but the worst combination can vary with time point, and target shelf lives can change. When two-sided limits are involved, there are two worst combinations, one with the highest upper and one with the lowest lower confidence limit.
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ACKNOWLEDGEMENTS
James S. Bergum is thanked for the excellent mentorship during the first author’s tenure at Bristol-Myers Squibb. MannKind Corporation is thanked for permission to publish. The reviewers are thanked for the many valuable suggestions. One reviewer is particularly thanked for his “eagle eye.”
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Friedman, E.M., Shum, S.C. Stability Models for Sequential Storage. AAPS PharmSciTech 12, 96–103 (2011). https://doi.org/10.1208/s12249-010-9558-x
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DOI: https://doi.org/10.1208/s12249-010-9558-x