Gaussian Process Modeling for Dissolution Curve Comparisons

Abstract

Abstract Dissolution studies are an integral part of pharmaceutical drug development, yet standard methods for analysing dissolution data are inadequate for capturing the true underlying shapes of the dissolution curves. Methods based on similarity factors, such as the f2 statistic, have been developed to demonstrate comparability of dissolution curves, however, this inability to capture the shapes of the dissolution curves can lead to substantial bias in comparability estimators. In this article, we propose two novel semi-parametric dissolution curve modeling strategies for establishing the comparability of dissolution curves. The first method relies upon hierarchical Gaussian process regression models to construct an f2 statistic based on continuous time modeling that results in significant bias reduction. The second method uses a Bayesian model selection approach for creating a framework that does not suffer from the limitations of the f2 statistic. Overall, these two methods are shown to be superior to their comparator methods and provide feasible alternatives for similarity assessment under practical limitations. Illustrations highlighting the success of our methods are provided for two motivating real dissolution data sets from the literature, as well as extensive simulation studies.