Comparison of free software platforms for the calculation of the 90% confidence interval of f2 similarity factor by bootstrap analysis
Graphical abstract
Introduction
Dissolution profile comparison is an essential tool for drug development and regulatory approval. Its use has been recognized by the main regulatory authorities (FDA and EMA) as a surrogate for bioequivalence in the case of minor variations of drug products (EMA, 2010; FDA, 2014), biowaivers based on the Biopharmaceutics Classification System (EMA, 2010; FDA, 2015), biowaivers for additional strengths (EMA, 2010; FDA, 2014), and as part of the evidence demonstrating therapeutic equivalence for certain dosage forms (EMA, 2018a; FDA, 1997, 2012).
The regulatory guidelines (EMA, 2010; FDA, 2017) describe different methodologies for the comparison of in vitro dissolution profiles, depending on the variability observed in the dissolution data. When a low coefficient of variation (CV) is observed in the amounts dissolved at the different sampling times (low variability: <20% up to 10 min and <10% in the rest of sampling times (EMA, 2010; FDA, 2017)), the dissolution profile comparison is generally based on the similarity factor (f2) equation, which assumes the mean dissolution profile of each product is an appropriate surrogate of the dissolution performance. The use of f2 has been extended worldwide due to its easy implementation. The f2 equation is based on the Euclidean distance, where similarity between test and reference products is concluded when f2 ≥50. This cut-off value of 50 has been defined since it is the value that corresponds to a difference of 10% between the average amounts dissolved at all sampling times.
On the contrary, dissolution comparison under high variability conditions has remained confusing until recently. Over the last few years, there have been a large number of publications where different methodologies were evaluated for the comparison of highly variable dissolution profiles (Cardot et al., 2017; Gomez-Mantilla et al., 2013; Mangas-Sanjuan et al., 2016; Martinez and Zhao, 2018; Ocaña et al., 2009; Paixao et al., 2017; Yoshida et al., 2017). Currently, the 90% confidence interval (CI) of f2 based on the bootstrap methodology has been proposed and accepted by the main regulatory authorities as a possible methodology for dissolution comparison under high variability conditions (Davit et al., 2013), or even as the preferred approach (EMA, 2018b). The value of f2 takes into account not only the differences in the dissolution of both products/formulations, but also its variance, which is negligible when low variability conditions are present. However, when two highly variable drug products/formulations are compared, the estimated value of f2 is clearly affected when the variance of both drug products/formulations is not considered. Some modifications of f2 have been proposed (Liu et al., 1997; Shah et al., 1998), but they have not been explored sufficiently and have no recognition by regulatory authorities.
Different free software platforms allow the calculation of the 90% CI of f2 by means of bootstrapping for the comparison of highly variable dissolution profiles, although none of them seems to have been adequately validated. Their use in regulatory submissions is growing, but divergent results have been observed between them. Therefore, the objective of this work is to analyze the characteristics of these software platforms and evaluate the results.
Section snippets
Experimental dataset
In order to investigate and compare the characteristics of the software platforms and their outputs, the calculation of the 90% CI of the f2 similarity factor was performed for the datasets displayed in Table 1. In vitro dissolution data were obtained from two modified-release products. Product 1 (30 mg) and product 2 (60 mg) were classified as test products (e.g. variation and additional strength, respectively), whereas product 3 (30 mg) was considered as reference. A sample of 12 tablets was
Experimental dataset
Fig. 1 shows the mean dissolution profiles of product 1, 2, and 3. Mean dissolved values of product 3 (reference) are always lower than those of product 1 (test), showing a slower dissolution rate. Compared to product 2, product 3 dissolves faster up to 145 min (25 min after the change of buffer to pH 6.8), but then, product 2 (test) shows a faster dissolution. Individual experimental values of each drug product are summarized in Table 1. The CV within each product exceeds the 20% at sampling
Discussion
DDsolver should not be used for the comparison of dissolution profiles with high variability because E(f2) and bc-f2 cannot be calculated with this software platform and the available 90% CI of f2 is biased due to high variability. Another major limitation of DDSolver is that this platform does not truncate the data sets to ensure that only one sampling point includes more than 85% dissolved for the first product to reach that value (EMA rule) or for both test and reference product (FDA rule).
Acknowledgments
None.
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