Optimization of Self-Microemulsifying Drug Delivery Systems (SMEDDS) Using a D-Optimal Design and the Desirability Function

Abstract

D-optimal design and the desirability function were applied to optimize a self-microemulsifying drug delivery system (SMEDDS). The optimized key parameters were the following: 1) particle size of the dispersed emulsion, 2) solubility of the drug in the vehicle, and 3) the vehicle compatibility with the hard gelatin capsule. Three formulation variables, PEG200, a surfactant mixture, and an oil mixture, were included in the experimental design. The results of the mathematical analysis of the data demonstrated significant interactions among the formulation variables, and the desirability function was demonstrated to be a powerful tool to predict the optimal formulation for the explored system.

Design and evaluation of famotidine mucoadhesive nanoparticles for aspirin induced ulcer treatment

“…The aim of the optimization of pharmaceutical formulations is generally to find the levels of the variable that affect the chosen responses and determine the levels of the variable from which a robust product with high quality characteristics may be produced. All the measured responses that may affect the quality of the product should be taken into consideration during the optimization procedure (Holm et al 2006;Gupta et al 2010). It was evident from the polynomial equation and contour plots (Figures 1-3) that a high and low level of X 1 could not target particle size close accordance with 500nm.…”

“…After the fitting of the mathematical model, the desirability function was used for the optimization. The application of the desirability function combined all the responses into one variable and left the possibility to predict the optimum levels for the independent variables (Holm et al 2006). …”

Development and optimization of a self-microemulsifying drug delivery system for atorvastatin calcium by using D-optimal mixture design

“…SMEDDS formulations form transparent microemulsions with a particle size of 100 nm, whereas self-emulsifying drug delivery system formulations typically form emulsions with particle sizes between 100 and 300 nm. 20,27 A transmittance value of 80% indicates good microemulsification, while a value in the range of 50%-80% indicates large droplet size and thus decreased microemulsification.…”

“…17 Optimal SMEDDS formulations can be obtained using a statistical optimization tool based on response surface methodology and experimental designs such as central composite, BoxBehnken, factorial, and mixture designs. [18][19][20] Statistical optimization allows simultaneous estimation of the main effects and interaction of all variables of a SMEDDS formulation. The d-optimal mixture design, a subtype of mixture design, is one of the most popular response surface methodologies for optimizing formulation of a SMEDDS.…”

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“…The technique requires minimum experimentation and time, thus proving to be far more effective and cost-effective than conventional methods of formulating SNEDDS. [7,8] As a type of quality by design, RSM is generally applied to experimental situations where several independent variables influence a response variable.…”