Quality by Design in Pharmaceutical Analysis Using Computer Simulation with UHPLC

May 01, 2014
Volume 32, Issue 5

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 modeling software for robustness testing was systematically compared to experimental measurements and design-of-experiment–based predictions.

The separation of an active pharmaceutical ingredient (API) and its impurities is a necessary step in the control of drugs. In most cases, the structures of the impurities vary widely. Some impurities are very similar to the API and others are built very differently. Reversed-phase chromatography is mainly used for their separation. In reversed-phase chromatography several parameters influence the quality of the separation. One of them is the stationary phase, where several interactions determine the separation: Hydrophobic interactions are the most dominant in terms of retention, and silanol groups influence the "polar selectivity" (1). If their ratio changes, the separation might change also. Other parameters include the composition of the mobile phase (%B or time of gradient [tG]), pH, and temperature. Both selectivity and working parameters must be in strict control if we transfer the method from laboratory to laboratory, or from high performance liquid chromatography (HPLC) to ultrahigh-pressure liquid chromatography (UHPLC) or vice versa. In the early stage of HPLC this was realized and method development was based on scientific recognitions in the field. In 1976 Horváth, Melander, and Molnár established the fundamentals of reversed-phase chromatography based on a design-of-experiments (DoE) approach (2). In 2002, regulatory authorities in the pharmaceutical industry, such as the Food and Drug Administration (FDA), recognized that analytical chemistry, in our case HPLC, is an integral part of the production of an API. In their guidelines, they highlighted that every parameter that can affect the analytical results must be forecasted. Today, this approach is called analytical quality by design (QbD).

The aim of this work was the implementation of analytical QbD in LC using intelligent modeling software (3–6). The modeling 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).

In 2004, a new age started in LC with the introduction of a new instrument with low extracolumn peak broadening (13). To get high efficiency, sub-2-µm particles were introduced. The columns packed with these small particles needed higher pressure compared to conventional HPLC. Today, the pressure limit is above 1000 bar or 14,500 psi. This new type of LC technique is called UHPLC.

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 trial-and-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 modeling software with multifactorial optimization of three measured critical HPLC method parameters — gradient time (tG), temperature (T), and pH — as well as further calculation of three factors: the flow rate, %B start, and %B end using UHPLC.



The mobile phase used in this work was a mixture of acetonitrile and 5 mM citrate buffer. Acetonitrile (gradient grade), citric acid, sodium hydroxide, standard reference buffers (pH 2.00, 4.01, and 7.00) were purchased from Merck. For the measurements, water was prepared fresh using ELGA Purelab UHQ water (ELGA). The buffer was filtered before use on regenerated cellulose filter membrane with a pore size of 0.2 µm (Sartorius).

The sample used throughout the study was an API and its starting materials (Stm), intermediates (Int), and known and unknown impurities (Imp).

Equipment and Software

UHPLC was performed using a Waters Acquity UPLC system equipped with a binary solvent delivery pump, an autosampler, a photodiode-array detector, a 5-µL injection loop and 500-nL flow cell, and Empower software. The column was a 50 mm × 2.1 mm, 1.7-µm dp Acquity BEH C18 UPLC column, which was selected from a large database with more than 500 reversed-phase columns (1). The dwell volume of the system was measured to be 0.125 mL.

An MP 225 pH meter was purchased from Mettler-Toledo.

Modeling was carried out using DryLab v. software, and the quantitative robustness evaluation of generated models was performed with the latest DryLab Robustness module v.1.0. (Molnár-Institute).

MarvinSketch v. 6.0.2 software was purchased from ChemAxon.

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