Čes. slov. farm. 2016, 65(1):22-27 | DOI: 10.36290/csf.2016.004

Influence of the mesh of extrusion dies on the properties of cores intended for the preparation of pellets with controlled glucose release

Sylvie Pavloková¹, Aleš Franc^1,*, Jan Muselík¹, Dana Sabadková¹, Pavlína Kopecká¹, David Neumann²

¹ Ústav technologie léků, Farmaceutická fakulta, Veterinární a farmaceutická univerzita, Brno, Česká republika

² Dětská klinika 1. LF UK a FN, Hradec Králové, Česká republika

Diabetes mellitus and its compensation are accompanied by serious complications. One of them is hypoglycaemia, which occurs in patients treated with insulin and/or certain peroral antidiabetics. Hypoglycaemic episodes can be prevented by a dosage form with controlled release of glucose. The pellet cores of four compositions and three different sizes corresponding to the diameter of extrusion screen mesh (0.6, 0.8, and 1.0 mm) were prepared for this purpose. The cores contain 75-80% of glucose combined with one of the following osmotically or swellable active agents: croscarmellose sodium (ADS), a mixture of microcrystalline cellulose and carmellose sodium Avicel RC 591 (RC), polyethylene glycol 6000 (PEG), and carboxymethyl starch (CMS). The cores were prepared by extrusion-spheronization and are intended for subsequent coating by a semipermeable membrane based on ethylcellulose. The aim of the work was to statistically evaluate the results of the physical evaluation of the pellets which were prepared and evaluated in previously published papers. The results are processed in the form of a meta-analysis using principle component analysis. The physical characteristics of the individual pellet sizes were different among themselves. Although the same compositions and manufacturing methods were used for all the pellets sizes, the cores produced through a 0.6 and 0.8 mm mesh screens showed similar properties while the properties of cores produced through a 1.0 mm mesh screen were rather different.

Keywords: hypoglycemia; delayed release; glucose; principal component analysis PCA; meta-analysis of data

Received: December 13, 2015; Accepted: January 4, 2016; Published: January 1, 2016  Show citation