Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices.
The aim of the present study was to develop a semi-empirical mathematical model, which is able to predict the release profiles of solid lipid extrudates of different dimensions. The development of the model was based on the application of ANNs and GP. ANNs' abilities to deal with multidimensional data were exploited. GP programming was used to determine the constants of the model function, a modified Weibull equation. Differently dimensioned extrudates consisting of diprophylline, tristearin and polyethylene glycol were produced by the use of a twin-screw extruder and their dissolution behaviour was studied. Experimentally obtained dissolution curves were compared to the calculated release profiles, derived from the semi-empirical mathematical model.
Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine University of Düsseldorf, Universitätsstr. 1, 40225 Duesseldorf, Germany.
Sinan
S
Güres
Aleksander
A
Mendyk
Renata
R
Jachowicz
Przemysław
P
Dorożyński
Peter
P
Kleinebudde
D004400:Dyphylline / Q000737:chemistry*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011092:Polyethylene Glycols / Q000737:chemistry*; D012995:Solubility; D014280:Triglycerides / Q000737:chemistry*
D014280:Triglycerides; D004400:Dyphylline; D011092:Polyethylene Glycols; C022618:tristearin