Bayesian Analysis, Hadamard Matrices,

No abstract

Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F2 parameter, an extension of the commonly used f2 statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f2 and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f2 statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.

Many pharmacologically active molecules are formulated as solid dosage form drug products. Following oral administration, the diffusion of an active molecule from the gastrointestinal tract into systemic distribution requires the disintegration of the dosage form followed by the dissolution of the molecule in the stomach lumen. Its dissolution properties may have a direct impact on its bioavailability and subsequent therapeutic effect. Consequently, dissolution (or in vitro release) testing has been the subject of intense scientific and regulatory interest over the past several decades. Much interest has focused on models describing in vitro release profiles over a time scale, and a number of methods have been proposed for testing similarity of profiles. In this article, we review previously published work on dissolution profile similarity testing and provide a detailed critique of current methods in order to set the stage for a Bayesian approach. KEY WORDS:Dissolution profile similarity; in vitro release; f 2 statistic; Bayesian model.

The α-ASTREE e-Tongue instrument uses seven sensors to characterize taste signals associated with a liquid sample. The instrument was used to study eight test preparations (comprised of a blank, four preparations corresponding to four known tastes and Sodium Topiramate in three concentrations known to have a bitter taste) and eight washes. Serially balanced residual effects designs were used to order the samples to estimate residual and main effects. The design provided for eight repeated measurements per test preparation. The experimental results suggested the following: (1) The seven sensors can be separated into three groups according to the ability to discriminate test preparations, and three of the sensors contributed little or no information. Further investigation suggested the lack of differentiability might be due to the age of the sensors. (2) The sensors discriminated known tastes from blank. The residual effect due to test preparations might appear after repeated usage. (3) Exploratory principal component analysis of the data indicated that nearly 90% of the total variability across the seven sensors could be explained by a single principal component. (4) The four standard taste preparations did not correspond to orthogonal dimensions in the principal component axes. (5) The three Sodium Topiramate test preparations could neither be associated with the corresponding known bitter taste sample nor could the three doses be shown to follow a quantitative dose-response relationship on the e-Tongue measurement scale. The practical interpretation of the results of the statistical analysis indicates only poor discriminative ability of the e-Tongue to distinguish clearly between increasing concentrations of a known bitter compound such as Sodium Topiramate. No apparent linear relationship could be discerned over increasing concentrations that would allow the quantification of bitterness.