Statistical Sequential Experimentation: Preliminary Mixed Factorial Design, I-Optimal Mixture Design Then Finally Novel Design Space Expansion for Optimization of Tazarotene Cubosomes.

Objective: This study aims to illustrate the potential of sequential experimentation for statistically scientific based optimization of Tazarotene (TAZA) cubosomes. Methods: Hot melt emulsification method was used for cubosomes preparation. A preliminary (3.2) mixed factorial design (MFD) was conducted to choose suitable types of stabilizer and surfactant that maximize entrapment efficiency (EE) and minimize particle size (PS). These chosen stabilizer and surfactant were to be used in the statistical design proposed for optimization of TAZA cubosomes (I-optimal mixture design) (IOMD). Glyceryl monooleate (GMO), stabilizer and surfactant amounts were the three mixture components (MixCs) studied in that design. Responses (EE, PS and drug percent released after 24 hours (Q24h)) were statistically analyzed. Numerical optimization using desirability function based on different responses' importance was used to find an IOMD-optimized formulation (IOMD-OF) with the predetermined characters. Then, a novel statistical methodology of design space expansion was adopted to enhance Q24h. Suitable models to express EE, PS and Q24h were elucidated over the expanded mixture design (EMD) space. Validity of derived models was verified via prediction intervals and percent deviations of actual values from predicted ones for all the EMD design points. EMD was then navigated to find EMD-OF. Results: Analysis of MFD showed that Pluronic-F68 and polyvinyl alcohol were the best stabilizer and surfactant to be used. First stage optimization after IOMD analysis led to a formulation with unsatisfactory Q24h of 58.8%. After design space expansion adoption, re-analysis and re-optimization, a satisfactory EMD-OF having EE of 82.1%, PS of 273.0 nm and Q24h of 68.8% was found. Conclusion: Statistical sequential experimentation with the novel design space expansion approach proved to be a successful paradigm for enhancing TAZA cubosomes optimization. Thus, this paradigm is expected to have promising future applications in various pharmaceutical formulations optimization.