Universal ion chromatography method for anions in active pharmaceutical ingredients enabled by computer-assisted separation modeling.

Ion Chromatography (IC) is one of the most widely used methods for analyzing ionic species in pharmaceutical samples. A universal IC method that can separate a wide range of different analytes is highly desired as it can save a lot of time for method development and validation processes. Herein we report the development of a universal method for anions in active pharmaceutical ingredients (APIs) using computer-assisted chromatography modeling tools. We have screened three different IC columns (Dionex IonPac AS28-Fast 4 µm, AS19 4 µm and AS11-HC 4 µm) to determine the best suitable column for universal IC method development. A universal IC method was then developed using an AS11-HC 4 µm column to separate 31 most common anionic substances in 36 mins. This method was optimized using LC Simulator and a model which precisely predicts the retention behavior of 31 anions was established. This model demonstrated an excellent match between predicted and experimental analyte retention time (R2 =0.999). To validate this universal IC method, we have studied the stability of sulfite and sulfide analytes in ambient conditions. The method was then validated for a subset of 29 anions using water and organic solvent/water binary solvents as diluents for commercial APIs. This universal IC method provides an efficient and simple way to separate and analyze common anions in APIs. In addition, the method development process combined with LC simulator modeling can be effectively used as a starting point during method development for other ions beyond those investigated in this study.

Selectivity and Resolving Power of Hydrophobic Interaction Chromatography Targeting the Separation of Monoclonal Antibody Variants.

This study presents a comprehensive investigation of the mechanistic understanding of retention and selectivity in hydrophobic interaction chromatography. It provides valuable insights into crucial method-development parameters involved in achieving chromatographic resolution for profiling molecular variants of trastuzumab. Retention characteristics have been assessed for three column chemistries, i.e., butyl, alkylamide, and long-stranded multialkylamide ligands, while distinguishing column hydrophobicity and surface area. Salt type and specifically chloride ions proved to be the key driver for improving chromatographic selectivity, and this was attributed to the spatial distribution of ions at the protein surface, which is ion-specific. The effect was notably more pronounced on the multialkylamide column, as proteins intercalated between the multiamide polymer strands, enabling steric effects. Column coupling proved to be an effective approach for maximizing resolution between molecular variants present in the trastuzumab reference sample and trastuzumab variants induced by forced oxidation. Liquid chromatography-mass spectrometry (LC-MS)/MS peptide mapping experiments after fraction collection indicate that the presence of chloride in the mobile phase enables the selectivity of site-specific deamidation (N30) situated at the heavy chain. Moreover, site-specific oxidation of peptides (M255, W420, and M431) was observed for peptides situated at the Fc region close to the CH2-CH3 interface, previously reported to activate unfolding of trastuzumab, increasing the accessible surface area and hence resulting in an increase in chromatographic retention.

Hybrid grafted and hyperbranched anion exchangers for ion chromatography.

Novel grafted anion exchangers with covalently bonded hyperbranched functional layers were prepared and evaluated for the separation of monovalent standard inorganic anions and oxyhalides. Preparation of base coating included grafting highly polar N-vinylformamide to the ethylvinylbenzene-divinylbenzene (EVB-DVB) substrate surface in highly polar solvent (methanol) with subsequent hydrolysis of grafted amide polymer in basic media, which resulted in preparation of polymer chains with multiple primary amino groups. Those amino groups were used as attachment points for forming hyperbranched anion-exchange layers using 1,4-butanediol diglycidyl ether and primary mono- or diamine (methylamine or 1,3-diaminopropane, respectively). The effects of hyperbranching reaction cycles number on selectivity were evaluated which revealed that selectivity and capacity can be controlled independently for the covalently bonded stationary phases in contrast to electrostatically bonded phases. It was demonstrated that unlike for electrostatically bonded phases, the intentional increase of crosslink by using primary diamine instead of primary monoamine doesn't cause the shift of selectivity coefficients. It was also shown that crosslink distribution throughout the hyperbranched layer is an important factor determining selectivity of hyperbranched anion exchangers.

A Universal Eluent System for Method Scouting and Separation of Biotherapeutic Proteins by Ion-Exchange, Size-Exclusion, and Hydrophobic Interaction Chromatography.

Characterization and quality control of biotherapeutic proteins commonly require the application of several orthogonal separation techniques in order to establish product identity and purity. Many of the techniques used rely on a buffered aqueous mobile phase system to maintain the native conformation of the protein and its variants. Optimal pH, buffer substance(s), and chromatography methods vary with each protein of interest and result in tedious method development for each new drug product. Linear controlled pH gradient systems from pH 5.6 to pH 10.2 has been shown to provide a global method for the separation of charge variants of monoclonal antibodies. This can be realized using two balanced zwitterionic buffer blends. The pH linearity of the resulting system, with a cation ion exchange column in place, can generate any pH value in this accessible pH range. This study expands the scope of this buffer system and demonstrates its application in conjunction with a quaternary HPLC pump for several analytical techniques: the pH optimization of salt gradient-based anion and cation exchange during method development, as well as performing pH gradient elution. In addition, the same universal buffers are used for hydrophobic interaction and size exclusion chromatography. This eluent system omits the need to prepare different buffers for each method and flushing of the HPLC system between method changes. The implementation of this concept is further demonstrated to allow an automated method scouting approach and selection of different methods that requires minimal manual intervention.

Preparation of ion exchange columns with longitudinal stationary phase gradients.

Preparation of columns using electrostatic attachment of anion exchange latex particles with charge density gradients is demonstrated. When such columns are oriented with the highest charge density at the column outlet, the chromatographic performance at low linear velocity is enhanced. When multiple successive charge density gradients are prepared along the length of the column with the highest capacity oriented at the inlet end of the column, significant improvement in chromatographic performance is observed during gradient elution chromatography.

New Insights into the Chromatography Mechanisms of Ion-Exchange Charge Variant Analysis: Dispelling Myths and Providing Guidance for Robust Method Optimization.

Charge variant analysis is a widely used analytical tool in characterization of monoclonal antibodies (mAbs). It depicts the heterogeneity of charge variant forms, some of which may differ by only minor modifications of a single amino acid. The analysis ensures product consistency with no unwanted changes to the protein. With increasing numbers of new mAb drug products emerging in the market, the need for a robust charge variant analysis has intensified. The charge variant profiles often display partially resolved peaks on shoulders of larger peaks. This puts considerably more pressure on the robustness of the method to maintain the suboptimum selectivity. New products and techniques have emerged to address these requirements, in addition to the pre-existing older methods that may not have been optimized correctly in the past. This has led to some confusion as to the best approach and strategies in optimization of charge variant analysis. We show studies from several different approaches using on-line pH monitoring to check the performance characteristics of the methods. This has led to new insights on the interactions between the protein, column, and buffer constituents. We dispel some inaccurate assumptions about the different ion-exchange elution mechanisms and suggest ways to develop high-throughput methods that remain robust and of high resolution. Streamlined automatable method development tools are presented that will result in more efficient method optimization. The mechanisms behind poor chromatography design have provided an alternative explanation behind some methods failing when in the QC laboratories.

Localised quantitative structure-retention relationship modelling for rapid method development in reversed-phase high performance liquid chromatography.

Quantitative structure-retention relationships (QSRR) predicting the values of solute "hydrophobicity" coefficient η' in the approximate hydrophobic subtraction model (HSM) can be used to predict retention times of compounds on numerous reversed-phase (RP) columns, provided that column parameters on the corresponding stationary phases are available. In the present study, we propose a new dual clustering-based localised QSRR approach, combining P-ratio clustering (where P is the octanol-water partition coefficient) with second dominant interaction (SDI)-based clustering, to produce predictive models with an acceptable level of prediction accuracy for in silico column scoping in RP method development. QSRR models for η' values were derived for 49 compounds out of 63 in a dataset extracted from the literature, where retention data were measured under one isocratic mobile phase condition (i.e., acetonitrile-water, 50:50 [v/v]). These models gave a predictive squared correlation coefficient Qext(F2)2 of 0.83 and a root mean square error of prediction (RMSEP) of 0.14. For the modelling, a genetic algorithm-partial least square regression (GA-PLS) approach was performed using the η' values and their relevant molecular descriptors. The corresponding retention times were predicted by applying the predicted η' values of the models and the stationary phase "hydrophobicity" parameter H values for the corresponding columns to the approximate HSM, resulting in excellent accuracy and predictability (Qext(F2)2 of 0.90 and RMSEP of 0.72 min). The established QSRR approach was experimentally verified for six Thermo Scientific columns (Acclaim™ 120 C18, Acclaim Polar Advantage, Acclaim Polar Advantage II, Accucore™ aQ, Accucore Phenyl-X, and Hypersil Gold C18 columns) using two types of datasets. The first dataset consisted of eight model compounds extracted from the original dataset and retention time predictions for those compounds were then evaluated on the above columns. The result showed good agreement between predicted and observed retention times with an acceptable error in retention time predictions (slope of 0.97, Qext(F2)2 of 0.95, a mean absolute error (MAE) of 0.43 min and RMSEP of 0.61 min). The second dataset included eight test compounds not included in the original dataset, which were all classified into the η' cluster by applying a Tanimoto similarity (TS) threshold of 0.7. Similarly, predicted retention times of the test compounds were compared with their corresponding observed retention times, resulting in acceptable retention time predictions with the slope of 0.99, Qext(F2)2 of 0.93 and RMSEP of 0.52 min. Comparisons of resolution values between columns were utilised to select the most suitable columns for separations of the compounds in the respective test sets. Actual chromatograms obtained on the chosen columns showed the feasibility for effective column scoping without experimentation on numerous RP stationary phases available in the USP website, based on the predicted resolution values.

Ultra-High-Pressure Ion Chromatography with Suppressed Conductivity Detection at 70 MPa Using Columns Packed with 2.5 µm Anion-Exchange Particles.

The use of ultrahigh pressures in combination with columns packed with 2.5 µm microporous and supermacroporous (perfusive) stationary phase particles coated with nanobeads has been successfully explored in ion chromatography with online eluent generation and suppressed conductivity detection. Isocratic separations of inorganic anions and organic acids yielding reduced plate heights as low as 2.1 were achieved, corresponding to efficiencies up to 190000 plates/m, using an optimized system configuration with respect to injection parameters, considering volume and mass loadability, and extra-column dispersion. Viscous-heating effects have been assessed for PEEK-lined stainless steel columns operated at 70 MPa, and effects of thermal gradients on separation efficiency and retention are demonstrated. Whereas the PEEK-lined column hardware acts to some extent as an insulator, a 10% increase in plate number could be obtained when applying a still-air column oven configuration. In the forced-air mode, an increase in retention was observed for polyvalent ions. Finally, the kinetic performance limits of ultrahigh-pressure ion chromatography applying 2.5 µm particle-packed columns operated at 70 MPa were compared to conventional ion-chromatography technology using columns packed with 4 µm particles operated at a maximum pressure of 35 MPa. Downscaling the particle size and increasing the operating pressure led to a maximum time gain with a factor of 3.4, without compromising separation efficiency (N = 10000).

Retention prediction using quantitative structure-retention relationships combined with the hydrophobic subtraction model in reversed-phase liquid chromatography.

The hydrophobic subtraction model (HSM) combined with quantitative structure-retention relationships (QSRR) methodology was utilized to predict retention times in reversed-phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never been used in the QSRR modeling process were used to verify the proposed approach. This work is designed to facilitate early prediction of co-elution of analytes in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR models were constructed through partial least squares regression combined with a genetic algorithm (GA-PLS) which was employed as a feature selection method to choose the most informative molecular descriptors calculated using VolSurf+ software. The analyte hydrophobicity coefficient of the HSM was predicted for subsequent calculation of retention. Clustering approaches based on the local compound type and the local second dominant interaction were investigated to select the most appropriate training set of analytes from a larger database. Predicted retention times of five new compounds on five new RPLC C18 columns were compared with their measured retention times with percentage root-mean-square errors of 15.4 and 24.7 for the local compound type and local second dominant interaction clustering methods, respectively.

A new suppressor design for low noise performance with carbonate eluents for Ion Chromatography.

Carbonate and bicarbonate based eluents have been applied for ion analysis from the inception of ion chromatography. The product of suppression with carbonate and/or bicarbonate eluent is carbonic acid which is weakly dissociated and tends to outgas. While the act of suppression enhanced the signal for fully dissociated ions and lowered the background to a weakly dissociated level, the overall noise performance, however, varied depending on the suppression mechanism. Chemical suppression with a membrane suppressor yielded low noise performance with carbonate and/or bicarbonate eluents. Electrolytic suppression, on the other hand, resulted in a relatively higher noise with carbonate based eluents when compared to chemical suppression. In this work, we investigated the root cause of noise with electrolytic suppressors and carbonate based eluents. Further, a new electrolytic suppressor design based on a three-electrode design is discussed in this paper and provided low noise performance with carbonate and/or bicarbonate eluents.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Ion exchange column capacities. Predicting retention behavior of open tubular columns coated with the same phase.

We discuss the reported capacities of available packed ion exchange columns and the different methods used for their measurement. We outline basic considerations related to both packed and open tubular columns based on ion exchange latex particles. There is a large body of information covering the retention behavior of packed ion exchange columns based on ion exchange latex particles. We propose a parameter γiex, which is the ion exchange capacity of a column (packed or open tubular) per unit liquid volume present in the column (including accessible volume within pores) and show that the retention factor for any given ion is directly related to γiex. On this basis, if based on the same type of latex, the behavior of one type of column can be reasonably predicted from the known behavior of the other, even when the absolute capacities differ by more than 5 orders of magnitude.

Electrostatically attached highly branched anion-exchange phases derived from diamines and diepoxides.

A new synthesis approach for the preparation of highly branched anion exchange materials utilizing diamine and diepoxide reagents is described. Unlike previously reported condensation polymers prepared from primary amine and diepoxide reagents, anion exchange polymers prepared from diamines and diepoxide reagents exhibit exceptionally low affinity for polyvalent ions. Use of anion-exchange materials synthesized utilizing this new synthetic method for the analysis of common inorganic anions is demonstrated.

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 (η'H) 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 30 s 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 30 s 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.

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review.

With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.

Novel method for manipulation of anion-exchange selectivity by derivatizing hydroxyl groups in the proximity of quaternary nitrogen ion-exchange sites with glycidol.

A general approach for the manipulation of anion-exchange selectivity through derivatization of hydroxyl groups adjacent to quaternary anion-exchange sites with glycidol is described. Repetitive reactions with glycidol result in dramatic shifts in the retention of divalent anions. Unique selectivities are observed for specific divalent species resulting in shifts in elution order. Modification of anion-exchange materials with glycidol has a small effect on the selectivity of monovalent anions, but in some cases, significant shifts in selectivity are observed. Use of the synthetic approach for modification of commercially available ion-exchange materials is demonstrated.

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.

Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added.

Error measures in quantitative structure-retention relationships studies.

An analysis and comparison of the use of four commonly used error measures (mean absolute error, percentage mean absolute error, root mean square error, and percentage root mean square error) for evaluating the predictive ability of quantitative structure-retention relationships (QSRR) models is reported. These error measures are used for reporting errors in the prediction of retention time of external test analytes, that is, analytes not employed during model development. The error-based validation metrics were compared using a simple descriptive statistic, the sum of squared residuals (SSR) of outliers to the edge of an error window. The comparisons demonstrate that Percentage Root Mean Squared Error of Prediction (RMSEP) provides the best estimate of the predictive ability of a QSRR model, having the lowest SSR value of 20.43.

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography.

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid "scoping" method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation - that the method is impractical for retention prediction for unknown compounds - is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk=a - blog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and tR- values with excellent accuracy and predictability (Qext(F2)2 of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38min) were created successfully, and these models can be employed to speed up the "scoping" phase of method development in IC.

Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems.

The development of quantitative structure retention relationships (QSRR) having sufficient accuracy to support high performance liquid chromatography (HPLC) method development is still a major issue. To tackle this challenge, this study presents a novel QSRR methodology to select a training set of compounds for QSRR modelling (i.e. to filter the database to identify the most appropriate compounds for the training set). This selection is based on a dual filtering strategy which combines Tanimoto similarity (TS) searching as the primary filter and retention time (tR) similarity clustering as the secondary filter, using a database of pharmaceutical compound retention times collected over a wide range of hydrophilic interaction liquid chromatography (HILIC) systems. To employ tR similarity filtering, correlation to a molecular descriptor is used as a measure of retention time. For the retention time of a compound to be modelled a relationship between experimental chromatographic data and various molecular descriptors is calculated using a genetic algorithm-partial least squares (GA-PLS) regression. The proposed dual-filtering-based QSRR model significantly improves the retention time predictability compared to the diverse, global, and TS-based QSRR models, with an average root mean square error in prediction (RMSEP) of 11.01% over five different HILIC stationary phases. The average CPU time for implementing the proposed approach is less than 10min, which makes it quite favorable for rapid method development in HILIC. In addition, interpretation of the molecular descriptors selected by this novel approach provided some insight into the HILIC mechanism.