Quality by design: Understanding the formulation variables of a cyclosporine A self-nanoemulsified drug delivery systems by Box–Behnken design and desirability function

Quality by design (QBD) refers to the achievement of certain predictable quality with desired and predetermined specifications. A very useful component of the QBD is the understanding of factors and their interaction effects by a desired set of experiments. The present project deals with a case stud...

Full description

Saved in:
Bibliographic Details
Published in:International journal of pharmaceutics 2007-03, Vol.332 (1), p.55-63
Main Authors: Zidan, Ahmed S., Sammour, Omaima A., Hammad, Mohammed A., Megrab, Nagia A., Habib, Muhammad J., Khan, Mansoor A.
Format: Article
Language:English
Subjects:
Analysis of Variance
Biological and medical sciences
Box–Behnken
Caprylates - chemistry
Chemistry, Pharmaceutical
Cyclosporine
Cyclosporine - administration & dosage
Cyclosporine - chemistry
Drug Compounding
Drug Delivery Systems - standards
Emulsions
Factor Analysis, Statistical
General pharmacology
Glycerides - chemistry
Immunosuppressive Agents - administration & dosage
Immunosuppressive Agents - chemistry
Linear Models
Medical sciences
Nanoparticles
Nanotechnology - standards
Nephelometry and Turbidimetry
Optimization
Pharmaceutical technology. Pharmaceutical industry
Pharmacology. Drug treatments
Plant Oils - chemistry
Polyethylene Glycols - chemistry
Predictive Value of Tests
Quality by design
Quality Control
Reproducibility of Results
Self-nanoemulsification
Solubility
Solvents - chemistry
Surface-Active Agents - chemistry
Technology, Pharmaceutical - standards
Time Factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Quality by design (QBD) refers to the achievement of certain predictable quality with desired and predetermined specifications. A very useful component of the QBD is the understanding of factors and their interaction effects by a desired set of experiments. The present project deals with a case study to understand the effect of formulation variables of nanoemulsified particles of a model drug, cyclosporine A (CyA). A three-factor, three-level design of experiment (DOE) with response surface methodology (RSM) was run to evaluate the main and interaction effect of several independent formulation variables that included amounts of Emulphor El-620 ( X 1), Capmul MCM-C8 ( X 2) and 20% (w/w) CyA in sweet orange oil ( X 3). The dependent variables included nanodroplets size ( Y 1), nanoemulsions turbidity ( Y 2), amounts released after 5 and 10 min ( Y 3, Y 4), emulsification rate ( Y 5) and lag time ( Y 6). A desirability function was used to minimize lag time and to maximize the other dependent variables. A mathematical relationship, Y 5 = 9.09 − 0.37 X 1 + 0.37 X 2 − 0.45 X 3 + 0.732 X 1 X 2 − 0.62 X 1 X 3 + 0.3 X 2 X 3 + 0.02 X 1 2 − 0.28 X 2 2 + 0.471 X 3 2 ( r 2 = 0.92), was obtained to explain the effect of all factors and their colinearities on the emulsification rate. The optimized nanodroplets were predicted to yield Y 1, Y 2, Y 3, Y 4, Y 5 and Y 6 values of 42.1 nm, 50.6 NTU, 56.7, 107.2, 9.3%/min and 3.5 min, respectively, when X 1, X 2, and X 3 values were 36.4, 70 and 10 mg, respectively. A new batch was prepared with these levels of the independent variables to yield Y 1– Y 6 values that were remarkably close to the predicted values. In conclusion, this investigation demonstrated the potential of QBD in understanding the effect of the formulation variables on the quality of CyA self-nanoemulsified formulations.
ISSN:0378-5173
1873-3476
DOI:10.1016/j.ijpharm.2006.09.060