Statistical comparison of dissolution curves

Enhancement of the Solubility of BS Class II Drugs with MOF and MOF/GO Composite Materials: Case Studies of Felodipine, Ketoprofen and Ibuprofen

In this research, a novel composite material composed of Metal-Organic Framework material (MOF) and graphite oxide was synthesized and evaluated as a possible drug-loading vehicle. HKUST-1, a MOF material originally designed by the Hong Kong University of Science and Technology, was used as a model porous material. The aim was to synthesize a drug delivery vehicle for modifying the release kinetics and solubility of poorly soluble drugs (BSC Class II drugs); these are drugs that are known to have poor bioavailability due to their low solubility. We used ketoprofen, ibuprofen, and felodipine as models for BSC Class II drugs. The drugs were loaded onto composite materials through adsorption. The adsorption of these three drugs into the matrix of HKUST-1/GO (graphite oxide), HKUST-1, and graphite oxide was compared. The loading efficiency of the drugs onto the carrier was dependent on the drug molecule and the composition of the drug carrier. The inclusion of graphite oxide in the drug carrier matrix improved the drug loading capacity and modified the drug release rate. The loading of the three drugs felodipine, ketoprofen, and ibuprofen onto HKUST-1 were 33.7, 58, and 79 mg/g respectively. The incorporation of GO into the HKUST-1 matrix resulted in an increase in the loading by 16 and 4 mg/g for the ketoprofen and ibuprofen drugs. When compared to the pure drugs, the solubility of all three drugs in the HKUST-1/GO matrix increased by at least 6 folds.

Comparison of free software platforms for the calculation of the 90% confidence interval of f2 similarity factor by bootstrap analysis

INTRODUCTION

The calculation of the 90% confidence interval of f₂ based on the bootstrap methodology has been proposed and accepted by the main regulatory authorities when the dissolution data shows excessive variability. Different free software platforms allow the calculation of the 90% CI of f₂ by means of bootstrapping. Their use in regulatory submissions is growing, but divergent results have been observed between the available software platforms. Therefore, the objective of this work is to analyze the characteristics of these software platforms and evaluate their results.

METHODS AND MATERIALS

Highly variable in vitro dissolution data from two products were selected. Three different similarity factors, f₂, E(f₂) and bc-f₂, and their corresponding 90% confidence intervals were calculated with three free software platforms, Pheq_bootstrap, Bootf2bca and DDSolver, and computed by four different approaches, normal approximation, bootstrap-t-CI, percentile CI, and bias corrected and accelerated CI.

RESULTS

All three platforms report the same f₂ value, 49.66 upon comparison of products 1 and 3 and 54.87 for products 2 and 3 (no truncation rule). Bootf2bca and Pheq_bootstrap provided the same f₂ and E(f₂) also under other truncation rules (EMA or FDA), which are not implemented in DDSolver. Pheq_bootstrap allowed the calculation of bc-f₂. The conclusion of similarity is based on a bootstrap percentile CI of E(f₂) and f₂ in Pheq_bootstrap and DDSolver, respectively. Bootf2bca provides all four 90% CI.

DISCUSSION

Bootf2bca or Pheq_bootstrap should be considered for the estimation of the 90% CI of the f₂ similarity factor when dissolution profiles show high variability.

DDSolver: an add-in program for modeling and comparison of drug dissolution profiles

In recent years, several mathematical models have been developed for analysis of drug dissolution data, and many different mathematical approaches have been proposed to assess the similarity between two drug dissolution profiles. However, until now, no computer program has been reported for simplifying the calculations involved in the modeling and comparison of dissolution profiles. The purposes of this article are: (1) to describe the development of a software program, called DDSolver, for facilitating the assessment of similarity between drug dissolution data; (2) to establish a model library for fitting dissolution data using a nonlinear optimization method; and (3) to provide a brief review of available approaches for comparing drug dissolution profiles. DDSolver is a freely available program which is capable of performing most existing techniques for comparing drug release data, including exploratory data analysis, univariate ANOVA, ratio test procedures, the difference factor f (1), the similarity factor f (2), the Rescigno indices, the 90% confidence interval (CI) of difference method, the multivariate statistical distance method, the model-dependent method, the bootstrap f (2) method, and Chow and Ki's time series method. Sample runs of the program demonstrated that the results were satisfactory, and DDSolver could be served as a useful tool for dissolution data analysis.

When is a metric not a metric? Remarks on direct curve comparison in bioequivalence studies

The majority of measures proposed to date for direct curve comparison in bioequivalence studies were investigated. These measures have often been called metrics, but in most cases this was incorrect in the mathematical sense. It was demonstrated, with a set of counter-examples, that the axioms of a metric are fulfilled only for the integral p-metric and some of its transforms. The Rescigno index and two other measures devised by Polli and McLean are the semi-metrics, lacking the triangle inequality, while others also lack symmetry. The use of the p-metric is therefore recommended, and statistical analysis is suggested as a point at which the scaling of differences might be carried out.

Nonlinear mixed-effects model for the dissolution assays of drugs

Sustained or controlled release delivery systems can achieve predictable and reproducible drug release rates. With the aim of optimizing the bioavailability of conventional drugs with minimum side effects, new drug delivery systems continue to attract much attention. This research has been addressed to investigate in vitro metoclopramide release behavior from inert polymeric matrix tablets. Model-dependent and model-independent approaches are commonly used to investigate the release process and a fitted model results for each experimental condition. The aim of this work was to study the release characterization of the whole batch, therefore, we have developed a population model, taking into account the differences between tablets, experimental conditions, heterogeneity, and autocorrelation of residuals. The study of dissolution profiles was carried out using a mixed-effect model, which has provided a satisfactory tool for this kind of data.

Challenges associated with the evaluation of veterinary product bioequivalence: an AAVPT perspective

The Generic Animal Drug Patent Term Restoration Act (GADPTRA) enacted in 1988 provided the same benefits to animal drug products that were granted to human generic products. It has been over 13 years since the GADPTRA was enacted, and veterinary drug sponsors and regulators have gained enormous insight and experience into some of the unique challenges associated with the determination of product bioequivalence for veterinary dosage forms. Moreover, advances in information and technology have opened both new issues that must be addressed and new mechanisms for demonstrating product bioequivalence. While many aspects of the existing Center for Veterinary Medicine Bioequivalence Guidance continue to provide invaluable guidance to the animal drug industry, there are also aspects of this guidance that are being called into question. Therefore, during the 2001 annual meeting of the American Academy of Veterinary Pharmacology and Therapeutics, participants were asked to address issues and concerns associated with the evaluation of veterinary product bioequivalence. This manuscript provides a summary of the concerns and discussions that transpired.

Bioequivalent or nonbioequivalent?

The current tests for bioequivalence are based on assumptions that are not valid in general; this paper shows why it is necessary to use a method that does not depend upon assumptions that cannot, and need not, be proved in general.