In vitro dissolution profile comparison using bootstrap bias corrected similarity factor, .

J Biopharm Stat

Division of Biometrics VI, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.

Published: January 2024

AI Article Synopsis

  • In vitro dissolution profiles are important for understanding drug absorption and are used to assess bioequivalence between test and reference products.
  • Various methods exist to compare dissolution profiles, with the similarity factor being the most commonly used metric despite its lack of strong statistical properties.
  • This paper evaluates different statistical methods for comparing dissolution profiles, finding that the recommended bootstrap bias corrected confidence interval method offers better control of type I error rates and improved power compared to other methods tested.

Article Abstract

In vitro dissolution profile has been shown to be correlated with the drug absorption and has often been considered as a metric for assessing in vitro bioequivalence between a test product and corresponding reference one. Various methods have been developed to assess the similarity between two dissolution profiles. In particular, similarity factor has been reviewed and discussed extensively in many statistical articles. Although the lacks inferential statistical properties, the estimation of and its various modified versions were the most widely used metric for comparing dissolution profiles. In this paper, we investigated performances of the naive estimate method, bootstrap confidence interval method and bias corrected-accelerated (BCa) bootstrap confidence interval method for comparing dissolution profiles. Our studies show that naive estimate method and BCa bootstrap confidence interval method are unable to control the type I error rate. The bootstrap confidence interval method can control the type I error rate under a specific level. However, it will cause great conservatism on the power of the test. To solve the potential issues of the previous methods, we recommended a bootstrap bias corrected (BC) confidence interval method in this paper. The type I error rate, power and sensitivity among different methods were compared based on simulations. The recommended bootstrap BC confidence interval method shows better control of type I error than the naive estimate method and BCa bootstrap confidence interval method. It also provides better power than the bootstrap confidence interval method.

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http://dx.doi.org/10.1080/10543406.2023.2171429DOI Listing

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