Drug impurity profiling: Method optimization on dissimilar chromatographic systems: Part I: pH optimization of the aqueous phase

Abstract

The use of dissimilar chromatographic systems in drug impurity profiling can be very advantageous. Screening a new-drug impurity mixture on those systems not only enhances the chance that all impurities are revealed, but also allows choosing a suited system for further method development. In this paper several strategies were evaluated to predict the optimal pH (of the buffer used in the mobile phase) from the screening results. Four or five dissimilar stationary phases were screened at four pH values (between 2.5 and 9.4), in order to obtain maximal information about the composition of the sample and to select one column for the subsequent optimization. Different linear models (straight lines, 2nd and 3rd degree polynomials) based on these experiments were tested for their ability to predict the retention times (t(R)) of the impurities at intermediate pH values. The predicted t(R) values were then used to calculate minimal resolutions and eventually to select an optimal pH at which the highest minimal resolution is predicted. None of the applied models is accurate enough to predict correctly which peaks are worst separated at the indicated optimal pH. However, the best strategy (applying a second degree polynomial describing the t(R) measured at 3 consecutive screening pH values) did succeed in indicating an optimal pH at which a good separation of the impurities is obtained. Unfortunately, the resulting separation quality is not or only slightly better than the best separation obtained during screening. Therefore, it can be concluded that the most (time-) efficient approach to develop an impurity profile of a new drug is to screen it on four or five dissimilar columns at four different pH values and to retain the best screening conditions (without making predictions for intermediate conditions) for further optimization of the organic modifier composition of the mobile phase, and occasionally the temperature and the gradient. This is at least the case when the profiles have a complexity similar to those studied.