Róbert Kormány

High performance liquid chromatography (HPLC) is a widely used and well-established technique, routinely employed by thousands of analytical scientists worldwide. Nonetheless, certain challenges—arising from the complex interplay of multiple factors affecting peak retention and separation—persist. In this context, multidimensional modeling approaches can provide valuable support.

Serial coupling of different column types can provide several important benefits to the chromatographer, including fine-tuning selectivity to separate complex mixtures.

The aim of this article is to illustrate the current status of computer-assisted method development and retention modelling. This study focuses on the successful method development of typical small pharmaceutical compounds (impurity profiling) and large therapeutic proteins. By choosing appropriate initial conditions, the method development can be performed in less than one day. However, for small molecules possessing different physicochemical properties, the conditions can be multifarious, while for biopharmaceuticals (for example, monoclonal antibodies [mAbs], antibody–drug conjugates [ADCs]), a generic method can easily be developed. In addition to retention modelling and optimization, the potential of simulated robustness testing is also demonstrated. Depending on the applied retention model, the impact of any change among six experimental parameters (tG, T, pH, ternary composition, flow rate, and initial- and final mobile phase compositions) on the separation can be assessed using a 26 or 36 type virtual

In this study, the quality-by-design principle is applied instead of trial-and-error in the development of a liquid chromatography (LC) method. 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-?m 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.

In this study, the quality-by-design principle is applied instead of trial-and-error in the development of an LC method.