title: Formulation and optimization of piroxicam proniosomes by 3-factor, 3-level Box-Behnken design.
abstract: The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of piroxicam proniosomes by the slurry method. A 3-factor, 3-level Box-Behnken design was used to derive a second-order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio of Span 60:cholesterol (X(1)), surfactant loading (X(2)), and amount of drug (X(3)). Fifteen batches were prepared by the slurry method and evaluated for percentage drug entrapment (PDE) and vesicle size. The transformed values of the independent variables and the PDE (dependent variable) were subjected to multiple regression to establish a full-model second-order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full-model equation to derive a reduced-model polynomial equation to predict the PDE of proniosome-derived niosomes. Contour plots were constructed to show the effects of X(1), X(2) and X(3) on the PDE. A model was validated for accurate prediction of the PDE by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X(1), X(2), and X(3) (0, -0.158 and -0.158 respectively), for maximized response of PDE with constraints on vesicle size. The Box-Behnken design demonstrated the role of the derived equation and contour plots in predicting the values of dependent variables for the preparation and optimization of piroxicam proniosomes.
authors.affiliation: Department of Pharmaceutics and Pharmaceutical Technology, A. R. College of Pharmacy & G. H. Patel Institute of Pharmacy, Vallabh Vidyanagar 388 120, Gujarat, India. ajay_solanki76@yahoo.com
authors.forename: Ajay B
authors.initials: AB
authors.lastname: Solanki
authors.forename: Jolly R
authors.initials: JR
authors.lastname: Parikh
authors.forename: Rajesh H
authors.initials: RH
authors.lastname: Parikh
mesh_terms: D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000737:chemistry*; D002626:Chemistry, Pharmaceutical; D002784:Cholesterol / Q000737:chemistry*; D004339:Drug Compounding; D005163:Factor Analysis, Statistical; D006601:Hexoses / Q000737:chemistry*; D008081:Liposomes; D015233:Models, Statistical; D010316:Particle Size; D010894:Piroxicam / Q000737:chemistry*; D011134:Polysaccharides / Q000737:chemistry; D016062:Porosity; D015203:Reproducibility of Results; D013499:Surface Properties; D013501:Surface-Active Agents / Q000737:chemistry*; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal; D006601:Hexoses; D008081:Liposomes; D011134:Polysaccharides; D013501:Surface-Active Agents; D010894:Piroxicam; C008315:maltodextrin; D002784:Cholesterol; C009298:sorbitan monostearate