MOLNÁR-INSTITUTE DryLab® applied in retention modeling in supercritical fluid chromatography

Berlin: – Academic researchers from Berlin’s Free University have successfully demonstrated the application of DryLab® analytical software in retention modeling of therapeutic peptides in supercritical fluid chromatography (SFC). The study exemplifies the use of representative linear and cyclic peptides – covering a broad range of physicochemical characteristics – to test and validate the feasibility of chromatographic modeling with DryLab®. By applying the software’s proprietary chromatographic first principles, it effectively modeled selectivity changes of peptides in SFC.

Extending the Scope of in silico Modeling to SFC

The research study titled “Retention Modeling of Therapeutic Peptides in Sub-/Supercritical Fluid Chromatography” is authored by Jonas Neumann, Sebastian Schmidtsdorff, and Professor Maria Parr, along with Chromicent’s founder and CEO, Alexander Schmidt. This work has recently been published in the separation science journal SSC Plus.

As demonstrated in earlier studies, in-silico chromatographic modeling using DryLab® or other empirical software packages has become a well-established tool for method optimization in reversed-phase (RP) applications, particularly through retention time (RT) prediction to improve separations. However, the application of RT modeling in SFC has been more limited, despite the growing interest in using SFC for separating complex biomolecules, such as peptides.

Three-Step Structured Approach

The study utilized DryLab to model the retention times (RTs) of peptides including bacitracin (Bac), colistin, tyrothricin, and insulin analogs. In the initial feasibility study, the team varied gradient time (tG), column temperature (T), and the composition of methanol/acetonitrile (ACN) as a ternary solvent (tC) with carbon dioxide in gradient elution, using both a neutral and an amino-derivatized aromatic stationary phase. In the second phase, they conducted an in silico chromatographic optimization by adjusting the gradient to optimize the separation of Bac’s fingerprint.

Finally, the researchers employed a gradient method using the Viridis BEH column, carbon dioxide, and a modifier composed of ACN, methanol, water, and methane sulfonic acid. This required manual volumetric proportioning, which introduced a potential source of inaccuracies. Nevertheless, the comparison between predicted and experimental RTs demonstrated high consistency, with an average error of ≤1.5% across both stationary phases.

Exploring Ternary Composition (MeOH vs ACN) Influences

The acquired models of the individual separation systems enabled a close examination of underlying changes in selectivity. Drastic peak shifts were observed on a Torus Diol column, particularly for Tyro compounds, as a result of varying proportions of the ternary solvent. While the addition of ACN did not enhance Tyro’s fingerprint on the diol phase, the researchers observed a shift in the elution order of several compounds. They hypothesize that the proportion of MeOH, which facilitates hydrogen bond interactions with a diol-type stationary phase (SP), is as important as the π-π interactions provided by ACN with an aromatic SP.

“These findings complement our previous results on ternary solvents for peptide SFC separations and reaffirm the usefulness of varying MeOH and ACN compositions during optimization steps,” they concluded.

Potential SFC method

In conclusion, the study demonstrated that a chromatography-based modeling approach allows for accurate prediction in SFC. This approach proved particularly useful in the second part of the study, effectively separating compounds within a single sample of complex peptides like Bac and Tyro, even when challenged by parameter settings not included in the input experiments.

“A sufficient level of prediction accuracy was achieved in terms of peak order and relative elution. For Bac, the resolution of the fingerprint was successfully optimized and experimentally confirmed. This work demonstrates the applicability of current modeling software for predicting high-modifier SFC separations, though some limitations exist in predicting peak widths and slight drifts in RTs,” the authors note.

About MOLNÁR-INSTITUTE

Founded in 1981, Molnár-Institute develops DryLab®4, a software for UHPLC modelling for a world-wide market. Its powerful modules gradient editor, peak tracking, automation, robustness and Design Space Comparison allow for the most sophisticated method development as required across modern pharma industries. Analytical scientists use DryLab®4 to understand chromatographic interactions, to reduce analysis time, to increase robustness, and to conform to Analytical Quality by Design (AQbD) principles, according to the recently published ICH Q14 regulatory framework.

The Molnár-Institute is a registered partner of the US-FDA, CDC and many other regulatory bodies. DryLab®4 pioneered AQbD long before regulatory agencies across the world encouraged such submissions. Widely implemented by thought leaders, the software contributes substantially to the paradigm shift towards a science and risk driven perspective on HPLC Quality Control and Assurance.

Further information at: http://www.molnar-institute.com/

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