In this study, the quality-by-design principle is applied instead of trial-and-error in the development of a liquid chromatography method. With few measurements, the appropriate stationary phase and chromatographic conditions such as the composition of mobile phase, gradient time, temperature, and pH can be determined. A mixture of an active pharmaceutical ingredient and its 13 impurities was analyzed on a short narrow-bore column (50 mm × 2.1 mm, packed with sub-2-µm particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and design-of-experiment–based predictions.
The aim of this work was the implementation of analytical QbD in LC using intelligent modelling software (3–6). The modelling software, which was originally developed in 1986 in cooperation with Snyder and his colleagues (7–9) and Molnár, looking at one and two dimensions, has now reached a three-dimensional (3D) stage (3,4,10,11). This intelligent software is based on the solvophobic theory of Horváth and colleagues (2) and the gradient elution theory of Snyder and Dolan (12). Further additions such as an improved peak tracking tool, the cube, and a robustness device help decrease the number of experiments needed for the development of a method and establish QbD-compatible UHPLC methods. This is particularly true for the 3D design (3).
Some considerations should be mentioned about the interconnection of QbD and intelligent software packages. To get a robust method, we need to know the influence of parameters and their combined action on a separation. In QbD this means we have to establish the design space, a region where we have baseline separation. In a classical tria-land-error approach one needed thousands of experiments and the final result might have been robust or not. It was hard to predict the resolution outside of an investigated area. Using intelligent software approaches, the number of measurements can be reduced by two or three orders of magnitude down to 12 runs (14,15). The critical point is the robustness of the separation. In trial-and-error approaches it is impossible to run hundreds of experiments; therefore the robustness is always under debate. Using intelligent software, 12 measurements are enough to get a robust method and indicate its limits (4).
This article investigates the development of an HPLC method for a pharmaceutical drug and its impurities using modelling software with multifactorial optimization of three measured critical HPLC method parameters — gradient time (t G), temperature (T), and pH — as well as further calculation of three factors: the flow rate, %B start, and %B end using UHPLC.