Modern trends in drug analysis: visualizing design space in the quality control of pharmaceuticals by capillary electrophoresis

In the pharmaceutical field, Quality by Design (QbD) has been recently introduced [1] as a fundamental quality model with the aim of demonstrating both understanding and control of pharmaceutical processes to deliver high quality pharmaceutical products. The analytical procedures are inseparable components of the global pharmaceutical process, and as such are required to answer quality demands postulated by the regulatory documents. Therefore, the concept of QbD should also be implemented in analytical method development, because these methods are intended to be used for quality control of both the active pharmaceutical ingredients and drug products. The key of QbD approach is the definition of the design space (DS), which corresponds to the multidimensional region of knowledge space where satisfactory values of all defined critical quality attributes (CQAs) are computed with a desired probability level. In this study, QbD workflow [2] has been applied for the set up of a capillary electrophoresis (CE) method for the quality control and impurity profiling of the antimigraine drug zolmitriptan in its pharmaceutical product. In CE several chemical, physical and instrumental parameters should be controlled in order to obtain good analysis performances in terms of minimum analysis time and high resolution, efficiency and sensitivity; moreover, these parameters may be often interacting in nature. Thus, it was essential to implement and strengthen the CE method development by means of a systematic strategy based on QbD principles. Preliminary scouting experiments led to select Capillary Zone Electrophoresis based on phosphate buffer as operative mode. Afterwards, in a screening phase the effect of critical process parameters (CPPs), both instrumental and related to the background electrolyte, on CQAs (critical resolution values, analysis time and peak efficiency) was evaluated by a symmetric screening matrix. Response surface methodology was then carried out by a Box-Behnken design and contour plots were drawn highlighting significant interactions between some of the CPPs. Probability surfaces were calculated by employing Monte-Carlo simulations, making it possible to consider the propagation of the predictive errors of the model. By setting a risk of error equal to 1% (π≥99%), the probability maps reported in Fig. 1 were obtained, where the DS is visualized in green. Additional verification points at the edges of DS were selected by a Plackett-Burman matrix and then tested to verify the requirements for CQAs to be fulfilled. A control strategy was finally implemented based on robustness test and system suitability limits, which corresponded to the lower and the higher CQAs values observed during system repeatability studies. The developed method was validated and applied to a real sample of zolmitriptan tablets.