Mixture-process variable approach to optimize a microemulsion electrokinetic chromatography method for the quality control of a nutraceutical based on coenzyme Q10.

In recent years, multivariate optimization has played an increasing role in analytical method development. ICH guidelines recommend using statistical design of experiments to identify the design space, in which multivariate combinations of composition variables and process variables have been demonstrated to provide quality results. Considering a microemulsion electrokinetic chromatography method (MEEKC), the performance of the electrophoretic run depends on the proportions of mixture components (MCs) of the microemulsion and on the values of process variables (PVs). In the present work, for the first time in the literature, a mixture-process variable (MPV) approach was applied to optimize a MEEKC method for the analysis of coenzyme Q10 (Q10), ascorbic acid (AA), and folic acid (FA) contained in nutraceuticals. The MCs (buffer, surfactant-cosurfactant, oil) and the PVs (voltage, buffer concentration, buffer pH) were simultaneously changed according to a MPV experimental design. A 62-run MPV design was generated using the I-optimality criterion, assuming a 46-term MPV model allowing for special-cubic blending of the MCs, quadratic effects of the PVs, and some MC-PV interactions. The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs. Contour and perturbation plots were drawn for each of the responses. Finally, the MPV models and criteria for the peak efficiencies were used to develop the design space and an optimal subregion (i.e., the settings of the mixture MCs and PVs that satisfy the respective criteria), as well as a unique optimal combination of MCs and PVs.

Response surface methodology in the optimization of chitosan-Ca pectinate bead formulations.

This study describes the application of a multi-varied experimental design methodology to the optimization of a bead formulation based on a mixed network of Ca pectinate and chitosan. The effect of varying the relative percent of the three components used for the bead production, i.e. pectin, chitosan and CaCl(2), has been systematically investigated with the aim of identifying their best levels to optimize drug encapsulation efficiency (considered as the experimental response to be maximized), as well as to highlight possible interactions among the components. The study was applied to two different drugs, i.e. prednisone and theophylline, selected, respectively, as model insoluble and relatively soluble drugs, in order to evaluate the influence of this parameter as well. Different bead batches were prepared according to Doehlert and D-optimal design and randomly evaluated for drug encapsulation efficiency. Analysis of response surface plots allowed identification of the best combination of the three bead components in order to maximize drug encapsulation efficiency. The most effective compositions were chitosan 3% (w/v), pectin 9% (w/v), CaCl(2) 4% (w/v) for the theophylline-loaded beads and chitosan 0.75% (w/v), pectin 6% (w/v), CaCl(2) 7.9% (w/v) for the prednisone-loaded ones. The good correspondence between calculated and experimental values indicated in both cases the validity of the generated statistical models for the prediction of microsphere encapsulation efficiency. The different results obtained for the two drugs indicated the importance of the greater or lesser drug lipophilicity in determining the optimal bead composition with the highest encapsulation efficiency.