The most popular way of comparing oral solid forms of drug formulations from different batches or manufacturers is through dissolution profile comparison. Usually, a similarity factor known as (f2) is employed; However, the level of confidence associated with this method is uncertain and its statistical power is low. In addition, f2 lacks the flexibility needed to perform in special scenarios. In this study two new statistical tests based on nonparametrical permutation test theory are described, the Permutation Test (PT), which is very restrictive to confer similarity, and the Tolerated Difference Test (TDT), which has flexible restrictedness to confer similarity, are described and compared to f2. The statistical power and robustness of the tests were analyzed by simulation using the Higuchi, Korsmayer, Peppas and Weibull dissolution models. Several batches of oral solid forms 1 were simulated while varying the velocity of dissolution ( from 30 mins to 300 mins to dissolve 85% of the total content) and the variability within each batch (CV 2% to 30%). For levels of variability below 10% the new tests exhibited better statistical power than f2 and equal or better robustness than f2. TDT can also be modified to distinguish different levels of similarity and can be employed to obtain customized comparisons for specific drugs. In conclusion, two new methods, more versatile and with a stronger statistical basis than f2, are described and proposed as viable alternatives to that method. Additionally, an optimized time sampling strategy and an experimental design-driven strategy for performing dissolution profile comparisons are described.

Abstract. Appropriate setting of dissolution specification of extended release (ER) formulations should include precise definition of a multidimensional space of complex definition and interpretation, including limits in dissolution parameters, lag time (t-lag), variability, and goodness of fit. This study aimed to set dissolution specifications of ER by developing drug-specific dissolution profile comparison tests (DPC tests) that are able to detect differences in release profiles between ER formulations that represent a lack of bioequivalence (BE). Dissolution profiles of test formulations were simulated using the Weibull and Hill models. Differential equations based in vivo-in vitro correlation (IVIVC) models were used to simulate plasma concentrations. BE trial simulations were employed to find the formulations likely to be declared bioequivalent and nonbioequivalent (BE space). Customization of DPC tests was made by adjusting the delta of a recently described tolerated difference test (TDT) or the limits of rejection of f2. Drug k a (especially if k a is small), formulation lag time (t-lag), the number of subjects included in the BE studies, and the number of sampled time points in the DPC test were the factors that affected the most these setups of dissolution specifications. Another recently described DPC test, permutation test (PT), showed excellent statistical power. All the formulations declared as similar with PT were also bioequivalent. Similar case-specific studies may support the biowaiving of ER drug formulations based on customized DPC tests.