[Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology].
OBJECTIVE
To investigate the preparation and optimization of calcium alginate floating microspheres loading aspirin.


METHODS
A model was used to predict the in vitro release of aspirin and optimize the formulation by artificial neural networks (ANNs) and response surface methodology (RSM). The amounts of the material in the formulation were used as inputs, while the release and floating rate of the microspheres were used as outputs. The performances of ANNs and RSM were compared.


RESULTS
ANNs were more accurate in prediction. There was no significant difference between ANNs and RSM in optimization. Approximately 90% of the optimized microspheres could float on the artificial gastric juice over 4 hours. 42.12% of aspirin was released in 60 min, 60.97% in 120 min and 78.56% in 240 min. The release of the drug from the microspheres complied with Higuchi equation.


CONCLUSIONS
The aspirin floating microspheres with satisfying in vitro release were prepared successfully by the methods of ANNs and RSM.
Zhang
An-yang
AY
Department of Pharmaceutics, Peking University School of Pharmaceutical Sciences, Beijing 100191, China.
Fan
Tian-yuan
TY
D000464:Alginates / Q000008:administration & dosage*; D001241:Aspirin / Q000008:administration & dosage*; D003692:Delayed-Action Preparations; D004337:Drug Carriers / Q000008:administration & dosage; D041981:Gastrointestinal Tract / Q000378:metabolism; D020723:Glucuronic Acid / Q000008:administration & dosage; D006603:Hexuronic Acids / Q000008:administration & dosage; D008863:Microspheres; D008956:Models, Chemical; D016571:Neural Networks, Computer
D000464:Alginates; D003692:Delayed-Action Preparations; D004337:Drug Carriers; D006603:Hexuronic Acids; D020723:Glucuronic Acid; D001241:Aspirin