Possibilities and Limitations of Computer-Assisted Method Development in HILIC: A Case Study

Tyteca, Eva; Bieber, Stefan; Letzel, Thomas; Desmet, Gert

doi:10.1007/s10337-016-3127-8

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…. was applied :

w = 4 \cdot \frac{t_{0}}{\sqrt{N_{gradient}}} \cdot (1 + k_{elut}) \cdot f,

where N gradient represents the plate number corrected for the retention factor at the moment of elution ( k elution ) :

N_{gradient} = N_{observed} \cdot \frac{{((), 1 + k_{elution})}^{2}}{{((), 1 + k_{observed})}^{2}},

where N observed and k observed are plate number and retention factor values that can be obtained directly from a chromatogram recorded in gradient mode . The corresponding value for N gradient was determined from the scouting runs to be 1100.…”

Section: Resultsmentioning

confidence: 99%

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Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography

Tyteca

Vos

Tassi

et al. 2017

J of Separation Science

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We describe a liquid chromatography method development approach for the separation of intact proteins using hydrophobic interaction chromatography. First, protein retention was determined as function of the salt concentration by isocratic measurements and modeled using linear regression. The error between measured and predicted retention factors was studied while varying gradient time (between 15 and 120 min) and gradient starting conditions, and ranged between 2 and 15%. To reduce the time needed to develop optimized gradient methods for hydrophobic interaction chromatography separations, retention-time estimations were also assessed based on two gradient scouting runs, resulting in significantly improved retention-time predictions (average error < 2.5%) when varying gradient time. When starting the scouting gradient at lower salt concentrations (stronger eluent), retention time prediction became inaccurate in contrast to predictions based on isocratic runs. Application of three scouting runs and a nonlinear model, incorporating the effects of gradient duration and mobile-phase composition at the start of the gradient, provides accurate results (improved fitting compared to the linear solvent-strength model) with an average error of 1.0% and maximum deviation of -8.3%. Finally, gradient scouting runs and retention-time modeling have been applied for the optimization of a critical-pair protein isoform separation encountered in a biotechnological sample.

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“…. was applied :

w = 4 \cdot \frac{t_{0}}{\sqrt{N_{gradient}}} \cdot (1 + k_{elut}) \cdot f,

where N gradient represents the plate number corrected for the retention factor at the moment of elution ( k elution ) :

N_{gradient} = N_{observed} \cdot \frac{{((), 1 + k_{elution})}^{2}}{{((), 1 + k_{observed})}^{2}},

Section: Resultsmentioning

confidence: 99%

“…In the last part of the study, we employed the validated two‐parameter retention modeling to optimize the separation of a biotechnological fermentation sample. To this end, we complemented the retention modeling with peak width predictions to predict the chromatographic resolution, using a previously published approach .…”

Section: Introductionmentioning

confidence: 99%

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography

Tyteca

Vos

Tassi

et al. 2017

J of Separation Science

Self Cite

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“…These strategies have been developed over the years with increasing knowledge of the parameters influencing retention. An example of this is the development of the Drylab, ChromSword, and PEWS 2 method development software [18,19,42,69–71]. Many of these papers are limited to RPLC, but there are three software packages that can be used for liquid chromatography in general.…”

Section: Methodsmentioning

confidence: 99%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

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Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

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“…New pharmaceutical and biomedical modalities are generating increasingly complex multicomponent mixtures of closely related compounds at a higher throughput. − In many cases, it is extremely difficult to optimize the separation by a traditional manual intervention. In this regard, computer-assisted chromatographic modeling has recently become a powerful labor-saving analytical tool to streamline the development of robust and reliable separation methods in both academic and industrial sectors. − In this approach, a multifactorial experimental design (temperature, mobile gradient, buffer concentration, etc.) is typically conducted, with the results being input into a software (ACD Laboratories/LC Simulator, DryLabs, etc.)…”

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confidence: 99%

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling

et al. 2020

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Modern pharmaceutical processes can often lead to multicomponent mixtures of closely related species that are difficult to resolve under chromatographic conditions, and even worse in preparative scale settings. Despite recent improvements in column technology and instrumentation, there remains an urgent need for creating innovative approaches that address challenging coelutions of critical pair and poor chromatographic productivity of purification methods. Herein, we overcome these challenges by introducing a simple and practical technique named multifactorial peak crossover (MPC) via computer-assisted chromatographic modeling. The approach outlined here focuses on mapping the separation landscape of pharmaceutical mixtures to quickly identify spaces of peak coelution crossings which enables one to conveniently switch the elution order of target analytes. Diverse examples of MPC diagrams as a function of column temperature, mobile phase gradient or a multifactorial combination in reversed phase and ion exchange chromatography (RPLC and IEC) modes are generated using ACD Laboratories/LC Simulator software and corroborated with experimental data match (overall retention time differences of less than 1%). This powerful MPC technique allows us to gain massive productivity increases (shorter cycle time and higher sample loading) for purification of pharmaceuticals by selectively switching the elution order of target components away from undesired tailing peaks and coelution spaces. MPC chromatography dramatically reduces the time spent developing productive analytical and preparative scale separations. In addition, we illustrate how this new MPC concept can be used to gain substantial improvements of the signal-to-noise ratio, enabling straightforward ppb detection of low-level target components with direct impact in the quantitation of metabolites and potential genotoxic impurities (PGIs). These innovations are of paramount importance in order to facilitate efficient isolation, characterization, and quantitation of drug substances in the development of new medicines.

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Possibilities and Limitations of Computer-Assisted Method Development in HILIC: A Case Study

Cited by 9 publications

References 28 publications

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography

Recent applications of retention modelling in liquid chromatography

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling

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