title: Response surface methodology in the optimization of chitosan-Ca pectinate bead formulations.
abstract: This study describes the application of a multi-varied experimental design methodology to the optimization of a bead formulation based on a mixed network of Ca pectinate and chitosan. The effect of varying the relative percent of the three components used for the bead production, i.e. pectin, chitosan and CaCl(2), has been systematically investigated with the aim of identifying their best levels to optimize drug encapsulation efficiency (considered as the experimental response to be maximized), as well as to highlight possible interactions among the components. The study was applied to two different drugs, i.e. prednisone and theophylline, selected, respectively, as model insoluble and relatively soluble drugs, in order to evaluate the influence of this parameter as well. Different bead batches were prepared according to Doehlert and D-optimal design and randomly evaluated for drug encapsulation efficiency. Analysis of response surface plots allowed identification of the best combination of the three bead components in order to maximize drug encapsulation efficiency. The most effective compositions were chitosan 3% (w/v), pectin 9% (w/v), CaCl(2) 4% (w/v) for the theophylline-loaded beads and chitosan 0.75% (w/v), pectin 6% (w/v), CaCl(2) 7.9% (w/v) for the prednisone-loaded ones. The good correspondence between calculated and experimental values indicated in both cases the validity of the generated statistical models for the prediction of microsphere encapsulation efficiency. The different results obtained for the two drugs indicated the importance of the greater or lesser drug lipophilicity in determining the optimal bead composition with the highest encapsulation efficiency.
authors.affiliation: Department of Pharmaceutical Sciences, University of Florence, Sesto Fiorentino, Italy.
authors.forename: N
authors.initials: N
authors.lastname: Mennini
authors.forename: S
authors.initials: S
authors.lastname: Furlanetto
authors.forename: F
authors.initials: F
authors.lastname: Maestrelli
authors.forename: S
authors.initials: S
authors.lastname: Pinzauti
authors.forename: P
authors.initials: P
authors.lastname: Mura
mesh_terms: D000893:Anti-Inflammatory Agents / Q000008:administration & dosage / Q000737:chemistry; D001993:Bronchodilator Agents / Q000008:administration & dosage / Q000737:chemistry; D002122:Calcium Chloride / Q000737:chemistry; D002626:Chemistry, Pharmaceutical; D048271:Chitosan / Q000737:chemistry*; D004339:Drug Compounding; D008055:Lipids / Q000737:chemistry; D008863:Microspheres; D015233:Models, Statistical; D010368:Pectins / Q000737:chemistry*; D011239:Prednisolone / Q000008:administration & dosage / Q000737:chemistry; D012044:Regression Analysis; D013806:Theophylline / Q000008:administration & dosage / Q000737:chemistry
chemical_list: D000893:Anti-Inflammatory Agents; D001993:Bronchodilator Agents; D008055:Lipids; D010368:Pectins; D048271:Chitosan; D011239:Prednisolone; D013806:Theophylline; D002122:Calcium Chloride