7, 8 Model-dependent methods have some limitations. Commonly used model-dependent methods to fit the dissolution profiles include Gompertz, 4 Logistic, 5 Weibull, 6 probit and sigmoid models. These confidence intervals are then compared with the specified similarity region. The model is then fit to the data and confidence intervals for the model parameters are constructed. In a model-dependent approach, an appropriate mathematical model is selected to describe the dissolution profiles of the two drugs. In order to assess drug dissolution profiles, both model-dependent and model-independent methods are used. Despite the post-approval changes, two drugs are similar with respect to their dissolution rates if the test (post-approval) has the same (equivalent) dissolution performance as the reference (pre-change). Such changes include change of manufacturing sites, change in formulations, and change in component and composition. The United States Food and Drug Administration (FDA) requires similarity tests for the dissolution profiles of two drugs under consideration when there are post drug-approval changes. 1‒3 Assessment of dissolution profiles for two drugs, in vitro, provides the waiver for in-vivo assessment. The purpose of dissolution testing is to develop a new formulation, to ensure quality control, and to assess stability and reproducibility of the immediately released solid oral drug. In pharmaceutical studies for solid and oral drugs, it is important to compare a test drug to a reference drug using average dissolution rates over time.
Keywords: dissolution profiles, bootstrapping, confidence interval, bias-corrected and accelerated bootstrap percentile confidence interval Introduction The Bias corrected (BC) and accelerated bootstrap percentile (BCa) confidence interval method produce more precise two-sided confidence intervals for f 2 to other methods Non-parametric bootstrap confidence intervals for f 2 better than those obtained from parametric methods. The bootstrap sampling distributions of f ^ 2 both schemes are found to be approximately symmetrical with a non-zero excess of kurtosis. A number of bootstrap confidence interval (CI) construction techniques are used to determine a 90 % CI for the true value of f 2 both parametric and non-parametric schemes. We estimate characteristics of the sampling distribution of f ^ 2 under these methods with various bootstrap sample sizes using Monte Carlo simulation. Parametric and non-parametric bootstrap methods are used to investigate the statistical properties of the dissolution similarity factor f 2 The main objective of this study is to compare the results obtained by these two methods.