Keywords: Dissolution Profile Comparison, Product Sameness, Decision Tree, R Shiny Tool
Impact of changes made to a product needs to be assessed to ensure product performance. Dissolution profile comparison based on similarity factor (f2) has been widely used in the pharmaceutical industry to assure product sameness under FDA, WHO, Japanese and European guidances. However, the f2 method, often regarded as a “gold standard”, is not suitable for products with more variable dissolution profiles and an alternative tool other than f2 may be required for the purpose of similarity assessment. Therefore, it is critical to identify a right tool/method in order to make meaningful assessment for product quality.
At AbbVie, we have tackled this important problem by considering the fundamentals in statistical methods, formulation design/processing/manufacturing, and dissolution methods. Several statistical methods have been explored and compared for their design and performance. Real case comparisons were performed to identify the suitable applications of each statistical method. In this presentation, a process decision tree is established and illustrated with the intent to guide method selection, data analysis, and conclusion for regulatory use. In particular, an R shiny tool based on such decision tree is presented, exemplifying typical analysis results from several suitable statistical methods.
The proposed decision tree considers several key factors. First, it has good statistical power with adequate type I error control that ensures product quality. Secondly, the two newly selected methods (i.e., f2 bootstrap and Tsong’s MSD methods) have a strong regulatory connection and are on the conservative end of all the methods evaluated. Thus, a passing result can provide high level of confidence. If a product fails both methods, it suggests a potential dissimilarity and more thorough assessment should be performed before reaching a final conclusion.