Bootstrap Statistics and Its Application in Disintegration and Dissolution Data Analysis

Abstract

Disintegration time (DT) and rate of drug dissolution in different media are among the most widely studied crucial parameters for various types of drug products. In the ever-evolving landscape of generic formulation development, dissolution comparison of reference and test products is the major reliable in vitro method of establishing product similarity. This is one of the most widely accepted methods of proving pharma equivalency between two drug products. A well-studied match between the disintegration and dissolution profile of the test and reference formulations can ensure in vitro product similarity. Various statistical approaches have been employed to establish product performance similarity; among them, the similarity factor (f₂) calculation based approach is the most widely accepted and explored method to date. However, the f₂ statistics fail to predict the similarity of batches with unit-to-unit variability. Bootstrap statistical analysis of dissolution data between the test and reference products was introduced to overcome the problems associated with batches with unit variability. Bootstrap can also be applied to extract statistically significant results by treating a series of data from different batches, which can further help to understand the trend. The current review depicts different case study based approaches to show the applications of bootstrap statistics in disintegration and dissolution similarity evaluation for both conventional and additively manufactured solid dosage forms. It is concluded that bootstrap statistics can be a very promising and reliable data analytical tool for establishing in vitro product similarity for both conventional and additively manufactured formulations with a high level of intraunit variability.

Keywords: Additive formulation; Bootstrap; Disintegration; Dissolution; Generic formulation; Similarity; Variability.