In vitro dissolution profile comparison using bootstrap bias corrected similarity factor, f₂

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 $f_2$ has been reviewed and discussed extensively in many statistical articles. Although the $f_2$ lacks inferential statistical properties, the estimation of $f_2$ and its various modified versions were the most widely used metric for comparing dissolution profiles. In this paper, we investigated performances of the naive $f_2$ estimate method, bootstrap $f_2$ confidence interval method and bias corrected-accelerated (BCa) bootstrap $f_2$ confidence interval method for comparing dissolution profiles. Our studies show that naive $f_2$ estimate method and BCa bootstrap $f_2$ confidence interval method are unable to control the type I error rate. The bootstrap $f_2$ 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) $f_2$ confidence interval method in this paper. The type I error rate, power and sensitivity among different $f_2$ methods were compared based on simulations. The recommended bootstrap BC $f_2$ confidence interval method shows better control of type I error than the naive $f_2$ estimate method and BCa bootstrap $f_2$ confidence interval method. It also provides better power than the bootstrap $f_2$ confidence interval method.

KEYWORDS:

• Dissolution profile comparison
• similarity factor $f_2$
• bootstrap confidence interval
• bias correction

Acknowledgement

The authors would like to thank Dr. Lei Nie for very helpful comments leading to improvements of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Disclaimer

Yi Tsong, this article reflects the views of the authors and should not be construed to represent FDA’s views or policies.

Additional information

Funding

The authors reported there is no funding associated with the work featured in this article.