OPTIMIZATION OF DICLOFENAC SODIUM DISSOLUTION FROM SUSTAINED-RELEASE FORMULATIONS USING AN ARTIFICIAL NEURAL-NETWORK

The application of a two-level back propagation type of neural network has been demonstrated for studying and optimization of diclofenac sod ium dissolution from sustained release matrix tablets. The effects of three formulation components (cetyl alcohol, polyvinylpyrrolidone K-30 and magnesium stearate) on the dissolution rate were analyzed using t his method. A non-linear relationship between the amount of cetyl alco hol and PVP K-30 and the amount of dissolved drug is described. The co nvenience of a formulation study by two- and three-dimensional respons e surface analysis is presented. Neural network technique can be parti cularly suitable in the pharmaceutical technology of sustained release dosage forms where systems are complex and nonlinear relationships be tween independent and dependent variables often exist.