Created by W.Langdon from gp-bibliography.bib Revision:1.7852
Supervisor Prof. Peter Kleinebudde",
The super-disintegrants also showed different effects on the release behaviour of diprophylline containing extrudates. Croscarmellose sodium and sodium starch glycolate led to fast disintegration of the matrix and to full release within a few minutes. Crospovidone (PVP-CL) of two different mean particle sizes instead, did not cause disintegration of the lipid matrix. These two super disintegrants showed different behaviour. In the case of Kollidon CL-SF, that one with the smaller particle size, the matrix was still intact after dissolution and the drug was dissolved from pores, as it was in the pore former group. Kollidon CL containing extrudates exhibited a much higher release rate. Here, surface erosion was the case, but not disintegration.
Since all the above mentioned experiments were performed using the excipients as received and as a consequence of this, the particle size influence on the release rate was not considered, additional trials with sieved excipients were performed. The excipients were sieved to a particle size range from 0-80 micrometer and the experiments were repeated. A significant influence of the particle size of the excipients could not be detected.",
For the development of the mathematical model the physicochemical properties of the extrudates were analysed first. Based on these results, fickian diffusion could be identified as the main transport mechanism during dissolution. Fick's second law of diffusion for cylindrically shaped systems served as the basic equation of the model. As Fick's second law of diffusion is a partial differential equation, an analytical solution via Laplace transformation had to be derived. The result was an equation, which could directly be used to calculate the released drug amount.
Extrudates of the abovementioned composition with PEG 20.000 of 0.6, 1.0, 1.5, 2.7 and 3.5 mm diameter were produced by using different die plates and were physicochemically characterised. After dissolution testing, the data of these extrudates were compared to the calculated release data, obtained by inserting diameter and length of the extrudates into the model equation. The calculation of the similarity factor f2 proved the sameness of the dissolution curve pairs (theory and experiment), indicating the good quality of the mathematical model. In order to validate the predictability of the model further experiments, considering only the length of an extrudate were performed. Extrudates of 1.0 mm diameter and the abovementioned composition were cut to different lengths. Dissolution experiments were performed and again the model equation was used to predict the release behaviour of these extrudates. Experiment and theory showed good accordance again. In order to demonstrate the limits of the mathematical model, a disintegrating extrudate (containing Kolldion CL-SF) was used. Since the model does not consider disintegration, it was not able to correctly predict the release behaviour in this case.
As a comparison to the mechanistic model based on Fick's second law of diffusion, an empirical approach was applied to the same problem (extrudates of 0.6-3.5 mm diameter). Artificial neuronal networks (ANNs) are well known as empirical modelling tools, which are able to learn from a set of experimental data and to identify a pattern in these data. Three parameters, extrudate length, extrudate diameter and the dissolution time were determined as input units for the ANNs. The released drug fraction was chosen as the output unit, since this was the parameter of interest. The ANNs was able to identify the diameter and time as a crucial parameters determining the release rate. The number of input units could thus be reduced from three to two.",
Genetic Programming entries for Sinan Gures