[Application of an artificial neural network in the design of sustained-release dosage forms].
OBJECTIVE
To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets.


METHODS
The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output.


RESULTS
The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles.


CONCLUSIONS
The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.
Wei
X H
XH
College of Pharmacy, Zhejiang University, Hangzhou 310031, China.
Wu
J J
JJ
Liang
W Q
WQ
D003692:Delayed-Action Preparations; D003911:Dextrans; D004110:Diltiazem / Q000008:administration & dosage; D015195:Drug Design; D007538:Isoniazid / Q000008:administration & dosage; D007785:Lactose / Q000031:analogs & derivatives*; D008747:Methylcellulose / Q000031:analogs & derivatives*; D016571:Neural Networks, Computer; D010078:Oxazines; D011899:Ranitidine / Q000008:administration & dosage; D012995:Solubility; D013607:Tablets
D003692:Delayed-Action Preparations; D003911:Dextrans; D010078:Oxazines; D013607:Tablets; D011899:Ranitidine; D008747:Methylcellulose; C071191:MK 458; D004110:Diltiazem; D007785:Lactose; D007538:Isoniazid