Acta Pharm. 57 (2007) 269-285

10.2478/v10007-007-0022-8

 

full paper

Original research paper

 

Design and statistical optimization of glipizide loaded lipospheres using response surface methodology

 

HAGALAVADI NANJAPPA SHIVAKUMAR, PRAGNESH BHARAT BHAI PATEL, BAPUSAHEB GANGADHAR DESAI, PURNIMA ASHOK AND SINNATHAMBI ARULMOZHI

shivakumarhn@yahoo.co.in

1Department of Pharmaceutical Technology

2Department of Pharmacology, K. L. E. S s College of Pharmacy, Bangalore-560010, India

Accepted March 21, 2007

 

A 32 factorial design was employed to produce glipizide lipospheres by the emulsification phase separation technique using paraffin wax and stearic acid as retardants. The effect of critical formulation variables, namely levels of paraffin wax (X1) and proportion of stearic acid in the wax (X2) on geometric mean diameter (dg), percent encapsulation efficiency (% EE), release at the end of 12 h (rel12) and time taken for 50% of drug release (t50), were evaluated using the F-test. Mathematical models containing only the significant terms were generated for each response parameter using the multiple linear regression analysis (MLRA) and analysis of variance (ANOVA). Both formulation variables studied exerted a significant influence (p < 0.05) on the response parameters. Numerical optimization using the desirability approach was employed to develop an optimized formulation by setting constraints on the dependent and independent variables. The experimental values of dg, % EE, rel12 and t50 values for the optimized formulation were found to be 57.54 1.38 m, 86.28 1.32%, 77.23 2.78% and 5.60 0.32 h, respectively, which were in close agreement with those predicted by the mathematical models. The drug release from lipospheres followed first-order kinetics and was characterized by the Higuchi diffusion model. The optimized liposphere formulation developed was found to produce sustained anti-diabetic activity following oral administration in rats.

 

Keywords: lipospheres, glipizide, factorial design, response surface methodology, optimization