Comparison of Dissolution Profiles: A Statistician’s Perspective
Dissolution profile comparisons are used in the context of postapproval changes where the manufacturer has to demonstrate that the quality of the product is not affected by the change. Around this topic, basic statistical principles are in conflict with widely used interpretations of current guideli...
Gespeichert in:
Veröffentlicht in: | Therapeutic innovation & regulatory science 2018-07, Vol.52 (4), p.423-429 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Dissolution profile comparisons are used in the context of postapproval changes where the manufacturer has to demonstrate that the quality of the product is not affected by the change. Around this topic, basic statistical principles are in conflict with widely used interpretations of current guidelines, resulting in time-intensive discussions in pharmaceutical practice. From a statistician’s perspective, the following suggestions could improve the situation regarding statistical analysis, inference, and interpretation: (1) A clear definition of the variability criterion for the similarity factor, such as that found in the EMA guideline, would be helpful. (2) Sample size recommendations should be interpreted as minimum, not as maximum, requirements. (3) In case of several batches per reference or test group, pooled comparisons should be performed instead of multiple batch-to-batch comparisons. (4) FDA Guideline recommendations concerning multivariate equivalence procedures for highly variable dissolution profiles are based on the state of statistical knowledge in 1997 and need to be updated. (5) The T
2 test for equivalence is an appropriate method for comparing highly variable dissolution profiles. Application of the T
2 test for equivalence enables reliable equivalence decisions and satisfies the intention of reaching scientific evidence in decision making. Software implementations of this test are available in R and SAS. The article is a summary of the poster of the same name presented at the DIA FDA Statistics Forum 2016. The poster took the third place in the poster award of the conference. |
---|---|
ISSN: | 2168-4790 2168-4804 |
DOI: | 10.1177/2168479017749230 |