Statistical modeling approaches for the comparison of dissolution profiles

Abstract

Dissolution studies are a fundamental component of pharmaceutical drug development, yet many studies rely upon the $f_1$ and $f_2$ model-independent approach that is not capable of accounting for uncertainty in parameter estimation when comparing dissolution profiles. In this paper, we deal with the issue of uncertainty quantification by proposing several model-dependent approaches for assessing the similarity of two dissolution profiles. We take a statistical modeling approach and allow the dissolution data to be modeled using either a Dirichlet distribution, gamma process model, or Wiener process model. These parametric forms are shown to be reasonable assumptions that are capable of modeling dissolution data well. Furthermore, based on a given statistical model, we are able to use the $f_1$ difference factor and $f_2$ similarity factor to test the equivalency of two dissolution profiles via bootstrap confidence intervals. Illustrations highlighting the success of our methods are provided for both Monte Carlo simulation studies, and real dissolution data sets.

Keywords: Dirichlet distribution; Wiener process; difference factor; gamma process; similarity factor.