Iontophoretic delivery of lisinopril: Optimization of process variables by Box-Behnken statistical design

• Published in: Pharmaceutical development and technology Vol. 15; no. 2; pp. 169 - 177
• Main Authors: Gannu, Ramesh, Yamsani, Vamshi Vishnu, Palem, Chinna Reddy, Yamsani, Shravan Kumar, Yamsani, Madhusudan Rao
• Format: Journal Article
• Language: English
• Published: New York, NY Informa UK Ltd 01-04-2010; Taylor & Francis; Informa Healthcare
• Subjects: Administration, Cutaneous; Angiotensin-Converting Enzyme Inhibitors - administration & dosage; Angiotensin-Converting Enzyme Inhibitors - pharmacokinetics; Animals; Biological and medical sciences; Box-Behnken design; factorial design; General pharmacology; Hydrogen-Ion Concentration; Iontophoresis; Lisinopril; Lisinopril - administration & dosage; Lisinopril - pharmacokinetics; Medical sciences; Models, Statistical; optimization; Permeability; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Rats; Regression Analysis; Skin Absorption; Sodium Chloride - chemistry; Time Factors; Lisinopril; Pharmaceutical technology; Enzyme; Enzyme inhibitor; Process; Route of administration; Optimization; Design; Peptidases; Iontophoresis; Peptidyl-dipeptidase A; Hydrolases; Peptidyl-dipeptidases; Antihypertensive agent; Factorial design; Box-Behnken design; ACE inhibitor
• ISSN: 1083-7450; 1097-9867
• DOI: 10.3109/10837450903085418

The objective of the investigation was to optimize the iontophoresis process parameters of lisinopril (LSP) by 3 × 3 factorial design, Box-Behnken statistical design. LSP is an ideal candidate for iontophoretic delivery to avoid the incomplete absorption problem associated after its oral administration. Independent variables selected were current (X1), salt (sodium chloride) concentration (X2) and medium/pH (X3). The dependent variables studied were amount of LSP permeated in 4 h (Y1: Q4), 24 h (Y2: Q24) and lag time (Y3). Mathematical equations and response surface plots were used to relate the dependent and independent variables. The regression equation generated for the iontophoretic permeation was Y1 = 1.98 + 1.23X1 − 0.49X2 + 0.025X3 − 0.49X1X2 + 0.040X1X3 − 0.010X2X3 + 0.58X12 − 0.17X22 − 0.18X32; Y2 = 7.28 + 3.32X1 − 1.52X2 + 0.22X3 − 1.30X1X2 + 0.49X1X3 − 0.090X2X3 + 0.79X12 − 0.62X22 − 0.33X32 and Y3 = 0.60 + 0.0038X1 + 0.12X2 − 0.011X3 + 0.005X1X2 − 0.018X1X3 − 0.015X2X3 − 0.00075X12 + 0.017X22 − 0.11X32. The statistical validity of the polynomials was established and optimized process parameters were selected by feasibility and grid search. Validation of the optimization study with 8 confirmatory runs indicated high degree of prognostic ability of response surface methodology. The use of Box-Behnken design approach helped in identifying the critical process parameters in the iontophoretic delivery of lisinopril.