Mixture-process variable approach to optimize a microemulsion electrokinetic chromatography method for the quality control of a nutraceutical based on coenzyme Q10 G. Piepel a , B. Pasquini b , S. Cooley a , A. Heredia-Langner a , S. Orlandini b , S. Furlanetto b,n a Applied Statistics and Computational Modeling, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington, USA b Department of Pharmaceutical Sciences, University of Florence, Via U. Schiff 6, 50019 Sesto Fiorentino, Florence, Italy article info Article history: Received 20 December 2011 Received in revised form 12 March 2012 Accepted 24 March 2012 Available online 25 April 2012 Keywords: Coenzyme Q10 MEEKC Mixture-process variable approach Nutraceutical abstract 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. & 2012 Elsevier B.V. All rights reserved. 1. Introduction Multivariate optimization is playing an increasing role in analy- tical method development. In the context of ‘‘quality by design’’ (in which quality is designed into products and methods), ICH guide- lines [1] recommend using statistical experimental design, model- ing, and optimization methods to identify a design space. The design space is the region of the experimental space where multidimen- sional combinations of mixture components (MCs) and process variables (PVs) have been demonstrated to provide assurance of quality. MCs are the ingredients in a mixture, typically expressed as proportions that sum to 1. PVs are factors in an experiment that do not form any portion of the mixture, but whose settings (when changed) can affect the responses. Using the data resulting from a mixture-process variable (MPV) experimental design, MPV models are developed to represent the relationship of system performance with MCs and PVs [2]. Such data-based models provide an in-depth understanding of the problem and the basis for developing the design space and optimiz- ing the quality of the analytical method. In this article, for the first time in the literature, a MPV approach was used to develop a microemulsion electrokinetic chromatography method (MEEKC) for the quality control of a nutraceutical based on coenzyme Q10 (Q10, CAS 303-98-0) and containing ascorbic acid (AA, CAS 50-81-7) and folic acid (FA, CAS 59-30-3). Several methods are described for the analysis of Q10 in pharmaceuticals and/or biological fluids [3–10]. However, to the best of our knowledge, no capillary electrophoresis method has been reported for the simultaneous quantitation of Q10, AA and FA in nutraceuticals. The goals of the MPV study were to identify the design space, an optimal subregion of the design space, and a desirable combination of MC proportions and PV settings within the optimal subregion. Sometimes a two-stage approach is used to address MPV optimization problems [11–14]. In the first stage, a mixture experiment is performed, mixture models for the responses are fit to the experimental data, and then the mixture models are used to develop an optimum mixture at fixed settings of the PVs (e.g., at standard or central values of PVs). In the second stage, the Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/talanta Talanta 0039-9140/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.talanta.2012.03.064 n Corresponding author. Tel.: þ39 55 4573717; fax: þ39 55 4573779. E-mail address: sandra.furlanetto@unifi.it (S. Furlanetto). Talanta 97 (2012) 73–82