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    New Publication on Uncovering Surprising Equivalences in Column Selectivities using 3D Analytical Design Spaces

    white-paperMolnár-Institute for applied chromatography
    April 25th 2024

    The latest research study involving scientists from the Berlin-based Molnár-Institute of Applied Chromatography builds on their earlier work in optimizing column selection and replacement by using 3D Design Space (DS) modelling in software to identify columns that will provide equivalent separation and selectivity results.

    The study ‘Revisiting column selectivity choices in ultra-high performance liquid chromatography–Using multidimensional analytical Design Spaces to identify column equivalency’ has now been peer reviewed and published in the Journal of Chromatography A.

    Employing structured approaches

    The paper is authored by Molnár-Institute senior scientists Arnold Zöldhegyi in collaboration with Krisztián Horváth from University of Pannonia and Róbert Kormány from Egis Pharmaceuticals.

    Kormány’s pioneering work laid the foundation for modeling concepts applying analytical Quality by Design (AQbD) principles in HPLC column comparison, as demonstrated in two seminal precursor studies [1, 2]. Building upon these principles, the team succeeded in developing a straightforward, AQbD-compliant modeling workflow to acquire and compare multidimensional DS models for an actual pharmaceutical application. This then enabled to objectively quantify and compare the chromatographic separations achievable on various USP L1-, as well as other L7-group phases.

    This paper represents a significant advancement in the field, offering valuable insights into HPLC column selection and method development through the application of AQbD principles and innovative modeling techniques.

    Increasing demand for powerful modeling tools

    Column selectivity plays a critical role in chromatography, influencing how effectively a chromatographic setup can differentiate between neighboring peaks. However, achieving the desired selectivity to separate sample constituents while meeting analytical goals can be challenging. This often results in lengthy method development, subpar performance, and inconsistent robustness.

    To address this, there is a need for more powerful modeling approaches. These approaches must consider and model the effects of all system components, including the stationary phase, mobile phase, column, and sample.

    In pharmaceutical laboratories, it’s a common dilemma to find two or more stationary phases with equivalent or comparable selectivity.  Using narrow-bore 50 × 2.1 mm UHPLC formats and a Waters Acquity Classic UPLC system, DS-models were generated with DryLab®4 (Ver. 4.5) modeling software package and their direct comparisons performed with the assistance of the recently introduced Design Space Comparison (DSC) module within the software. By analyzing acquired models of the selected stationary phases and comparing them with traditional methods like the Tanaka test and Snyder-Dolan HSM evaluation, the team revisited column selectivities from a Design Space perspective.

    Concerns with traditional methods

    Current guides and column selection system platforms can provide some helpful insights into selection of alternative phases. However, their practical reliability, as was found in this case, may be uncertain because they often lack detailed information on their actual application specifics. This can make it hard to compare different stationary phases effectively. In such situations, multivariate modeling tools are pivotal as they can deliver unbiased results.

    Surprising results

    Although the tested stationary phases exhibited a wide range of physicochemical properties, the DS models revealed some unexpected similarities between columns. For example, they uncovered shared method conditions—Method Operable Design Region (MODR)—resulting in established baseline separation and identical elution order for the two most distinct C18 phases (HSS C18 and HSS C18 SB).

    However, there were also cases where, despite the anticipated similarities in column data, the model Design Spaces and chromatograms revealed notable differences between the stationary phases.

    “This underscores the limitations of current column testing practices and affirms that identifying alternative phases and suitable conditions is feasible only through AQbD-based modeling methodologies,” the authors conclude.


    1. Róbert Kormány, Imre Molnár and Rieger, H.-J. (2013). Exploring better column selectivity choices in ultra-high performance liquid chromatography using Quality by Design principles. Journal of Pharmaceutical and Biomedical Analysis, 80, pp.79–88. doi:
    2. ‌ Kormány, R., Soós, B. and Horváth, K. (2024). Updating the European Pharmacopoeia impurity profiling method for cetirizine and suggesting alternative column, using design space comparison. Journal of Pharmaceutical and Biomedical Analysis, [online] 237, p.115776. doi:



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