Christopher A. Pohl

Comparison of reversed-phase/cation-exchange/anion-exchange trimodal stationary phases and their use in active pharmaceutical ingredient and counterion determinations.

This study involved three commercial reversed-phase (RP)/anion-exchange (AEX)/cation-exchange (CEX) trimodal columns, namely Acclaim Trinity P1 (Thermo Fisher Scientific), Obelisc R (SIELC Technologies) and Scherzo SM-C18 (Imtakt). Their chromatographic properties were compared in details with respect to hydrophobicity, anion-exchange capacity, cation-exchange capacity, and selectivity, by studying retention behavior dependency on organic solvent, buffer concentration and pH. It was found that their remarkably different column chemistries resulted in distinctive chromatography properties. Trinity P1 exhibited strong anion-exchange and cation-exchange interactions but low RP retention while Scherzo SM-C18 showed strong reversed-phase retention with little cation-exchange and anion-exchange capacities. For Obelisc R, its reversed-phase capacity was weaker than Scherzo SM-C18 but slightly higher than Trinity P1, and its ion-exchange retentions were between Trinity P1 and Scherzo SM-C18. In addition, their difference in selectivity was demonstrated by examples of determining the active pharmaceutical ingredient (API) and counterion of drug products.

Prediction of Analyte Retention for Ion Chromatography Separations Performed Using Elution Profiles Comprising Multiple Isocratic and Gradient Steps

This study addresses the simulation of ion chromatographic (IC) separations performed under conditions where the elution profile consists of a sequence of isocratic and gradient elution steps (referred to as complex elution profiles). First, models for prediction of retention under gradient elution conditions in IC were evaluated using an extensive database of gradient elution retention data. It is shown that one such model is preferred on the basis that it can be used to predict gradient retention times on the basis of isocratic input data. A method is then proposed for using this model for complex elution profiles whereby each step of the elution profile is treated separately and analyte movement through the column is mapped. An empirically based algorithm for predicting peak width under complex elution conditions is also proposed. Evaluation of the suggested approaches was undertaken on a set of 24 analyte anions and 13 analyte cations on 5 different Dionex columns using a range of 5-step complex elution profiles that gave R2 values for correlations between predicted and observed retention times of 0.987 for anions and 0.997 for cations. The simulation of separations of anions and cations using a 3-step complex elution profile is demonstrated, with good correlation between observed and predicted chromatograms. The proposed approach is useful for the rapid development of separations when complex elution profiles are used in IC.

Disaccharide Analysis of Glycosaminoglycans Using Hydrophilic Interaction Chromatography and Mass Spectrometry

Heparan sulfate (HS) and chondroitin sulfate/dermatan sulfate (CS/DS) glycosaminoglycans (GAGs) participate in many important biological processes. Quantitative disaccharide analysis of HS and CS/DS is essential for the characterization of GAGs and enables modeling of the GAG domain structure. Methods involving enzymatic digestion and chemical depolymerization have been developed to determine the type and location of sulfation/acetylation modifications as well as uronic acid epimerization. Enzymatic digestion generates disaccharides with Δ-4,5-unsaturation at the nonreducing end. Chemical depolymerization with nitrous acid retains the uronic acid epimerization. This work shows the use of hydrophilic interaction liquid chromatography mass spectrometry (HILIC-MS) for quantification of both enzyme-derived and nitrous acid depolymerization products for structural analysis of HS and CS/DS. This method enables biomedical researchers to determine complete disaccharide profiles on GAG samples using a single LC-MS platform.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures

Quantitative structure-retention relationship (QSRR) models are developed to predict the retention times of analytes on five hydrophilic interaction liquid chromatography (HILIC) stationary phases (bare silica, amine, amide, diol and zwitterionic), with a view to selecting the most suitable stationary phase(s) for the separation of these analytes. The study was conducted using six β-adrenergic agonists as target analytes. Molecular descriptors were calculated based only on chemical structures optimized using density functional theory. A genetic algorithm (GA) was then used to select the most relevant molecular descriptors and these were used to build a retention model for each stationary phase using partial least squares (PLS) regression. This model was then used to predict the retention of the test set of target analytes. This process created an optimized descriptor set which enhanced the reliability of the developed QSRR models. Finally, the QSRR models developed in the work were utilized to provide some insight into the separation mechanisms operating in the HILIC mode. Three performance criteria - mean absolute error (MAE), root mean square error of prediction scaled to retention time (RMSEP), and the number of selected descriptors, were used to evaluate the developed models when applied to an external test set of six β-adrenergic agonists and showed highly predictive abilities. MAE values ranged from 13 to 25s on four of the stationary phases, with a somewhat higher error (50s) being observed for the zwitterionic phase. RMSEP values of 4.88-11.12% were recorded. Validation was performed through Y-randomization and chemical domain applicability, from which it was evident that the developed optimized GA-PLS models were robust. The high levels of accuracy, reliability and applicability of the models were to a large extent due to the optimization of the GA descriptor set and the presence of relevant structural and geometric molecular descriptors, together with descriptors based on important physicochemical properties, which establish a strong connection between retention time and meaningful chemical properties. The present strategy, while it is a pilot study, holds great promise for broader screening of HILIC stationary phases for desired separation, as well as for acquisition of information about molecular mechanisms of separation under chromatographic conditions.

Rapid method development in hydrophilic interaction liquid chromatography for pharmaceutical analysis using a combination of quantitative structure-retention relationships and design of experiments

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the models prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.

Capillary ion chromatography at high pressure and temperature.

The application of high pressure and temperature in ion chromatography (IC) can significantly improve the efficiency and reduce the analysis time. In this work, the kinetic-performance limits of capillary IC columns with inner diameters of 400 μm packed with 4 and 7 μm macroporous anion-exchange particles were investigated employing a capillary ion-exchange instrument allowing column pressures up to 34 MPa and column temperatures up to 80 °C. Plate heights below 10 μm could be realized using capillary columns packed with 4 μm particles. Compared to conventional IC using 7 μm particles and pressures up to 21 MPa, a 40% improvement in plate number could be achieved when working at the kinetic performance limits at 34 MPa and using columns packed with 4 μm particles. Using coupled columns with a total length of 400 mm, a mixture of seven anions was separated within 7.5 min while yielding 20,000 plates. Increasing the temperature improved the performance limits when operating in the C-term region (for fast IC separation using columns <75 cm). Temperature also affected the retention properties and hence the selectivity. At higher temperature, retention for monovalent ions was mainly governed by ion diameter. An increase in retention with temperature was observed for small ions, and there was a decrease for ions having a larger diameter. The retention factor for divalent and trivalent anions increased with temperature.

Determination of pharmaceutically related compounds by suppressed ion chromatography: IV. Interfacing ion chromatography with universal detectors

This work forms the final part of a study investigating gradient elution ion-exchange chromatography of pharmaceutically relevant compounds, aiming at achieving complementary selectivity to reversed-phase HPLC. In this study the coupling of three universal detectors (electro-spray ionisation mass spectrometer (ESI-MS); corona charged aerosol detector (CAD); and evaporative light scattering detector (ELSD)) to suppressed IC using complex elution profiles with potassium hydroxide eluents is demonstrated. The non-volatile ions were removed from the eluent by the suppressor prior to detection, thus allowing a stable detector response, especially with the prototype electrolytic suppressor. The detector response for ten weakly anionic pharmaceuticals followed the expected models and the limits of detection obtained were not compromised by the use of a suppressor, yielding values below 50 ng/mL with MS, low to sub μg/mL levels with CAD and 2-20 μg/mL with ELSD (25 μL injections). When coupled to MS and CAD, the prototype electrolytic suppressor showed percentage relative standard deviations (%RSDs) in peak areas of 0.4-2.5% on average, compared to the chemical suppressor which yielded 1.5-3 fold higher %RSD values for the test analytes. The prototype electrolytic suppressor also generally exhibited wider linear response ranges than the chemical suppressor.

Evaluation of desialylation during 2-amino benzamide labeling of asparagine-linked oligosaccharides

Labeling of released asparagine-linked (N-linked) oligosaccharides from glycoproteins is commonly performed to aid in the separation and detection of the oligosaccharide. Of the many available oligosaccharide labels, 2-amino benzamide (2-AB) is a popular choice for providing a fluorescent product. The derivatization conditions can potentially lead to oligosaccharide desialylation. This work evaluated the extent of sialic acid loss during 2-AB labeling of N-linked oligosaccharides released from bovine fetuin, polyclonal human serum immunoglobulin G (IgG), and human α1-acid glycoprotein (AGP) as well as of sialylated oligosaccharide reference standards and found that for more highly sialylated oligosaccharides the loss is greater than the <2% value commonly cited. Manufacturers of glycoprotein biotherapeutics need to produce products with a consistent state of sialylation and, therefore, require an accurate assessment of glycoprotein sialylation.

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (ηH) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30u202fs and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30u202fs in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.