A Note on the Trandermal Delivery of Clenbuterol

A modified diffusion/compartmental model has been used to simulate the transdermal uptake of clenbuterol from a matrix-type delivery device. The application of a fresh device every 7 days was found to produce a pseudo-steady state drug plasma profile after approx. three changes of device. Of the matrix properties, only drug loading had substantial effect on the drug plasma profile

The influence of endogenous surfactant on the structure and drug-release properties of Eudragit NE30D-matrixes

The influence of an endogenous surfactant present in Eudragit NE30D on the structure and drug release (clenbuterol) properties of thin matrices has been examined. Both drug-free and drug-loaded matrices were found to be non-isotropic in structure, the former having a marbled appearance under the polarising light microscope, and the latter showing numerous needle-shaped crystals. At loading above approx. 10% w/w clenbuterol it was also possible to observe aggregates of the drug. Differential scanning calorimetry enabled the identification of melting peaks at approx. 50°C for the needle-shaped crystals and approx. 80°C for the larger drug aggregates. The former are composed of a surfactant used by the manufacturer for the synthesis of Eudragit NE30D by emulsion polymerization. This surfactant undergoes a phase separation from the polymer on storage at room temperature. It could, however, be extracted from the polymer by refluxing in water to yield an isotropic system. The extract showed a melting peak at 50°C and also UV, IR, NMR, and mass spectra in accordance with an o-substituted nonyl phenol surfactant. Matrices prepared from the purified Eudragit NE30D showed drug release rates of only one third the magnitude of those found with matrices prepared from the raw polymer. Substantially reduced scatter in the release data was also found with the purified polymer

Determining the solubility and crystal form of clenbuterol in thin films of eudragit NE30D

The solubility of the drug clenbuterol in thin films of surfactant-free Eudragit NE30D has been measured. Light microscopy and differential scanning calorimetry were supplemented by a technique based on measurement of the rate of drug release from the films. The clenbuterol crystals had the form of a fractal, as could be shown by a computer simulation of diffusion-controlled aggregation

EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions

This paper presents an R package EMMIXcskew for the fitting of the canonical fundamental skew t-distribution (CFUST) and finite mixtures of this distribution (FM-CFUST) via maximum likelihood (ML). The CFUST distribution provides a flexible family of models to handle non-normal data, with parameters for capturing skewness and heavy-tails in the data. It formally encompasses the normal, t, and skew-normal distributions as special and/or limiting cases. A few other versions of the skew t-distributions are also nested within the CFUST distribution. In this paper, an Expectation-Maximization (EM) algorithm is described for computing the ML estimates of the parameters of the FM-CFUST model, and different strategies for initializing the algorithm are discussed and illustrated. The methodology is implemented in the EMMIXcskew package, and examples are presented using two real datasets. The EMMIXcskew package contains functions to fit the FM-CFUST model, including procedures for generating different initial values. Additional features include random sample generation and contour visualization in 2D and 3D

EMMIX-uskew: An R Package for Fitting Mixtures of Multivariate Skew t-distributions via the EM Algorithm

This paper describes an algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions. The package EMMIX-uskew implements a closed-form expectation-maximization (EM) algorithm for computing the maximum likelihood (ML) estimates of the parameters for the (unrestricted) FM-MST model in R. EMMIX-uskew also supports visualization of fitted contours in two and three dimensions, and random sample generation from a specified FM-uMST distribution. Finite mixtures of skew t-distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour, for example, datasets from flow cytometry. In recent years, various versions of mixtures with multivariate skew t (MST) distributions have been proposed. However, these models adopted some restricted characterizations of the component MST distributions so that the E-step of the EM algorithm can be evaluated in closed form. This paper focuses on mixtures with unrestricted MST components, and describes an iterative algorithm for the computation of the ML estimates of its model parameters. The usefulness of the proposed algorithm is demonstrated in three applications to real data sets. The first example illustrates the use of the main function fmmst in the package by fitting a MST distribution to a bivariate unimodal flow cytometric sample. The second example fits a mixture of MST distributions to the Australian Institute of Sport (AIS) data, and demonstrate that EMMIX-uskew can provide better clustering results than mixtures with restricted MST components. In the third example, EMMIX-uskew is applied to classify cells in a trivariate flow cytometric dataset. Comparisons with other available methods suggests that the EMMIX-uskew result achieved a lower misclassification rate with respect to the labels given by benchmark gating analysis