title: Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach.
abstract: Multivariate nature of drug loaded nanospheres manufacturing in term of multiplicity of involved factors makes it a time consuming and expensive process. In this study genetic algorithm (GA) and artificial neural network (ANN), two tools inspired by natural process, were employed to optimize and simulate the manufacturing process of agar nanospheres. The efficiency of GA was evaluated against the response surface methodology (RSM). The studied responses included particle size, poly dispersity index, zeta potential, drug loading and release efficiency. GA predicted greater extremum values for response factors compared to RSM. However, real values showed some deviations from predicted data. Appropriate agreement was found between ANN model predicted and real values for all five response factors with high correlation coefficients. GA was more successful than RSM in optimization and along with ANN were efficient tools in optimizing and modeling the fabrication process of drug loaded in agar nanospheres.
authors.affiliation: Department of Pharmaceutics, Novel Drug Delivery Systems Research Centre, School of Pharmacy and Pharmaceutical Science, Isfahan University of Medical Science, Isfahan, Iran; Exir Pharmaceutical Company, Boroojerd, Iran. Electronic address: zaki@pharm.mui.ac.ir.
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Zaki
authors.affiliation: Department of Pharmaceutics, Novel Drug Delivery Systems Research Centre, School of Pharmacy and Pharmaceutical Science, Isfahan University of Medical Science, Isfahan, Iran. Electronic address: varshosaz@pharm.mui.ac.ir.
authors.forename: Jaleh
authors.initials: J
authors.lastname: Varshosaz
authors.affiliation: Department of Food Science and Technology, Faculty of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address: mfathi@cc.iut.ac.ir.
authors.forename: Milad
authors.initials: M
authors.lastname: Fathi
mesh_terms: D000362:Agar / Q000737:chemistry*; D000465:Algorithms; D016642:Bupropion / Q000378:metabolism*; D065546:Drug Liberation; D054118:Nanospheres / Q000737:chemistry*; D016571:Neural Networks, Computer; D013499:Surface Properties
chemical_list: D016642:Bupropion; D000362:Agar
keywords: Agar nanospheres; Artificial Neural network; Bupropion; Genetic algorithm; Response surface methodology