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Background: Monoclonal antibodies (mAbs) undergo multiple post-translational modifications (PTMs) during production and storage, resulting for instance in charge and oxidized variants. PTMs need to be assessed as critical quality attributes to assure protein quality and safety. Capillary zone electrophoresis (CZE) enables efficient charge-based separation. The CZE method developed by He et al. (2011) is currently applied routinely in the pharmaceutical industry for profiling charge heterogeneity of mAbs. However, as the method relies on a non-volatile background electrolyte (BGE), it cannot be directly hyphenated with mass spectrometry (MS), hampering the identification of separated charge variants., Results: This study presents a CZE-UV/MS method using a neutral static capillary coating of hydroxypropyl methylcellulose combined with a volatile BGE at pH 5.0 to allow for MS-compatible mAb charge variant separations. The effect of several parameters, including pH and concentration of the BGE, applied voltage, and injected mAb concentrations on separation performance was investigated using a panel of commercially available mAbs. The optimized method was evaluated with IgG 1 and IgG 4 mAbs of varying pI (7.4-9.2) and degrees of heterogeneity. Basic and acidic variants were separated from the parent mAb using a BGE of 50 mM acetic acid adjusted to pH 5.0 with ammonium hydroxide. The relative abundances of charge variants determined with the new method showed a good correlation with the corresponding relative levels obtained with the method of He et al. CZE-MS coupling was accomplished using the nanoCEasy, a low-flow sheath liquid interface, which enabled the identification and quantitation of basic, acidic, and incomplete pyroglutamate variants, and glycoforms of the tested mAbs., Significance: This manuscript describes a new CZE-MS method that permits heterogeneity assessment of mAbs under MS-compatible conditions, providing charge variant separation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Polyesters are applied in high-end products in many industrial applications, including resins and powder-coating applications. The characterization of the chemical heterogeneities within a polyester is of utmost interest to develop new products or improve existing applications. Unfortunately, characterization is a difficult task, as polyesters may feature distributions in end-group functionality, molecular weight, chemical composition, and degree of branching. Currently, no analytical method can characterize all these interdependent distributions in a single analysis., Results: We report the use of comprehensive normal-phase liquid chromatography × size-exclusion chromatography hyphenated with ultraviolet-light spectroscopy and high-resolution mass spectrometry in parallel (NPLC × SEC-UV/HRMS) to characterize polyesters according to their end-group-functionality and molecular-weight distributions. The chemical composition can be measured with HRMS, while relative quantitation can be performed with UV detection. A supercharging agent was used during ionization allowing to extend the molecular-weight range of the detected chemical species., Significance: The presented platform allows characterization of polyesters with varying fractions of carboxyl or hydroxyl end-group functionalities and varying distributions of molecular weight, degree of branching, and chemical compositions. The number-average and weight-average molar masses are obtained in the same analysis. This information cannot be obtained by any one-dimensional technique. The developed NPLC × SEC-UV/HRMS platform is a valuable tool for characterizing polyesters in an industrial setting., Competing Interests: Declaration of competing interest “The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.”, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data., Result: To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data., Significance: Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Method development in comprehensive two-dimensional liquid chromatography (LC×LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, make trial-and-error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues. However, both approaches have their strengths and weaknesses. On the one hand, retention modeling, which approximates true retention behavior, depends on effective peak tracking and accurate retention time and width predictions, which are increasingly challenging for complex samples and advanced gradient assemblies. On the other hand, Bayesian optimization may require many experiments when dealing with many adjustable parameters, as in LC×LC. Therefore, in this work, we investigate the use of multi-task Bayesian optimization (MTBO), a method that can combine information from both retention modeling and experimental measurements. The algorithm was first tested and compared with BO using a synthetic retention modeling test case, where it was shown that MTBO finds better optima with fewer method-development iterations than conventional BO. Next, the algorithm was tested on the optimization of a method for a pesticide sample and we found that the algorithm was able to improve upon the initial scanning experiments. Multi-task Bayesian optimization is a promising technique in situations where modeling retention is challenging, and the high number of adjustable parameters and/or limited optimization budget makes traditional Bayesian optimization impractical., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Hydroxypropyl methyl cellulose (HPMC) is a type of cellulose derivative with properties that render it useful in e.g. food, cosmetics, and pharmaceutical industry. The substitution degree and composition of the β-glucose subunits of HPMC affect its physical and functional properties, but HPMC characterization is challenging due to its high structural heterogeneity, including many isomers. In this study, comprehensive two-dimensional liquid chromatography-mass spectrometry was used to examine substituted glucose monomers originating from complete acid hydrolysis of HPMC. Resolution between the different monomers was achieved using a C18 and cyano column in the first and second LC dimension, respectively. The data analysis process was structured to obtain fingerprints of the monomers of interest. The results revealed that isomers of the respective monomers could be selectively separated based on the position of substituents. The examination of two industrial HPMC products revealed differences in overall monomer composition. While both products contained monomers with a similar degree of substitution, they exhibited distinct regioselectivity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Trapped ion mobility spectrometry-time-of-flight mass spectrometry (TIMS-TOFMS) has emerged as a tool to study protein conformational states. In TIMS, gas-phase ions are guided across the IM stages by applying direct current (DC) potentials (D1-6), which, however, might induce changes in protein structures through collisional activation. To define conditions for native protein analysis, we evaluated the influence of these DC potentials using the metalloenzyme bovine carbonic anhydrase (BCA) as primary test compound. The variation of DC potentials did not change BCA-ion charge and heme content but affected (relative) charge-state intensities and adduct retention. Constructed extracted-ion mobilograms and corresponding collisional cross-section (CCS) profiles gave useful insights in (alterations of) protein conformational state. For BCA, the D3 and D6 potential (which are applied between the deflection transfer and funnel 1 [F1] and the accumulation exit and the start of the ramp, respectively) had most profound effects, showing multimodal CCS distributions at higher potentials indicating gradual unfolding. The other DC potentials only marginally altered the CCS profiles of BCA. To allow for more general conclusions, five additional proteins of diverse molecular weight and conformational stability were analyzed, and for the main protein charge states, CCS profiles were constructed. Principal component analysis (PCA) of the obtained data showed that D1 and D3 exhibit the highest degree of correlation with the ratio of folded and unfolded protein (F/U) as extracted from the mobilograms obtained per set D potential. The correlation of D6 with F/U and protein charge were similar, and D2, D4, and D5 showed an inverse correlation with F/U but were correlated with protein charge. Although DC boundary values for induced conformational changes appeared protein dependent, a set of DC values could be determined, which assured native analysis of most proteins., (© 2024 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd.)

Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed. A workflow was designed to operate within a closed-loop that allowed direct interaction between the LC × LC-MS system and a data-processing computer which ran in an unsupervised and automated fashion. Obtaining accurate retention models in LC × LC is difficult due to the challenges associated with the exact determination of retention times, curve fitting because of the use of gradient elution, and gradient deformation. Thus, retention models were compared in terms of repeatability of determination. Additionally, the design of shifting gradients in the second dimension and the prediction of peak widths were investigated. The algorithm was tested on separations of a tryptic digest of a monoclonal antibody using an objective function that included the sum of resolutions and analysis time as quality descriptors. The algorithm was able to improve the separation relative to a generic starting method using these complex gradient profiles after only four method-development iterations (i.e., sets of chromatographic conditions). Further iterations improved retention time and peak width predictions and thus the accuracy in the separations predicted by the algorithm., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C 4 D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

New psychoactive substances (NPS) are synthetic derivatives of illicit drugs designed to mimic their psychoactive effects. NPS are typically not controlled under drug acts or their legal status depends on their molecular structure. Discriminating isomeric forms of NPS is therefore crucial for forensic laboratories. In this study, a trapped ion mobility spectrometry time-of-flight mass spectrometry (TIMS-TOFMS) approach was developed for the identification of ring-positional isomers of synthetic cathinones, a class of compounds representing two-third of all NPS seized in Europe in 2020. The optimized workflow features narrow ion-trapping regions, mobility calibration by internal reference, and a dedicated data-analysis tool, allowing for accurate relative ion-mobility assessment and high-confidence isomer identification. Ortho-, meta- and para-isomers of methylmethcathinone (MMC) and bicyclic ring isomers of methylone were assigned based on their specific ion mobilities within 5 min, including sample preparation and data analysis. The resolution of two distinct protomers per cathinone isomer added to the confidence in identification. The developed approach was successfully applied to the unambiguous assignment of MMC isomers in confiscated street samples. These findings demonstrate the potential of TIMS-TOFMS for forensic case work requiring fast and highly-confident assignment cathinone-drug isomers in confiscated samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Cellulose ethers (CEs) are semi-synthetic polymers produced by derivatization of natural cellulose, yielding highly substituted products such as ethyl hydroxyethyl cellulose (EHEC) or methyl ethyl hydroxyethyl cellulose (MEHEC). CEs are commonly applied as pharmaceutical excipients and thickening agents in paints and drymix mortars. CE properties, such as high viscosity in solution, solubility, and bio-stability are of high interest to achieve required product qualities, which may be strongly affected by the substitution pattern obtained after derivatization. The average and molar degree of substitution often cannot explain functional differences observed among CE batches, and more in-depth analysis is needed. In this work, a new method was developed for the comprehensive mapping of the substitution degree and composition of β-glucose monomers of CE samples. To this end, CEs were acid-hydrolyzed and then analyzed by gradient reversed-phase liquid chromatography-mass spectrometry (LC-MS) using an acid-stable LC column and time-of-flight (TOF) mass spectrometer. LC-MS provided monomer resolution based on ethylene oxide, hydroxyl, and terminating methyl/ethyl content, allowing the assignment of detailed compositional distributions. An essential further distinction of constitutional isomer distributions was achieved using an in-house developed probability-based deconvolution algorithm. Aided by differential heat maps for visualization and straightforward interpretation of the measured LC-MS data, compositional variation between bio-stable and non-bio-stable CEs could be identified using this new approach. Moreover, it disclosed unexpected methylations in EHEC samples. Overall, the obtained molecular information on relevant CE samples demonstrated the method's potential for the study of CE structure-property relationships., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations ( i.e. , sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work ( e.g. , previously published data handling algorithms).

Two-dimensional liquid chromatography (2DLC) offers great separation power for complex mixtures. The frequently encountered incompatibility of two orthogonal separation systems, however, makes its application complicated. Active-modulation strategies can reduce such incompatibility issues considerably. Stationary-phase-assisted modulation (SPAM) is the most-common of these techniques, but also the least robust due to the major disadvantage that analytes may elute prematurely. The range of liquid chromatography (LC) applications continues to expand towards ever more complex mixtures. Retention modelling is increasingly indispensable to comprehend and develop LC separations. In this research, a tool was designed to assess the feasibility of applying SPAM in 2DLC. Several parameters were investigated to accurately predict isocratic retention of analytes on trap columns under dilution-flow conditions. Model parameters were derived from scanning-gradient experiments performed on analytical columns. The trap-to-trap repeatability was found to be similar to the prediction error. Dead volumes for the trap columns could not be accurately determined through direct experimentation. Instead, they were extrapolated from dead-volume measurements on analytical columns. Several known retention models were evaluated. Better predictions were found using the quadratic model than with the log-linear ("linear-solvent-strength") model. Steep scanning gradients were found to result in inaccurate predictions. The impact of the dilution flow on the retention of analytes proved less straightforward than anticipated. Under certain conditions dilution with a weaker eluent was found to be counter productive. A tool was developed to quantify the effect of the dilution flow and to predict whether SPAM could be applied in specific situations. For nine different analytes under 36 different sets of conditions and with three different modulation times, the SPAM tool yielded a correct assessment in more than 95% of all cases (less than 5% false positives plus false negatives)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Rapid optimization of gradient liquid chromatographic (LC) separations often utilizes analyte retention modelling to predict retention times as function of eluent composition. However, due to the dwell volume and technical imperfections, the actual gradient may deviate from the set gradient in a fashion unique to the employed instrument. This makes accurate retention modelling for gradient LC challenging, in particular when very fast separations are pursued. Although gradient deformation has been addressed in method-transfer situations, it is rarely taken into account when reporting analyte retention parameters obtained from gradient LC data, hampering the comparison of data from various sources. In this study, a response-function-based algorithm was developed to determine analyte retention parameters corrected for geometry-induced deformations by specific LC instruments. Out of a number of mathematical distributions investigated as response-functions, the so-called "stable function" was found to describe the formed gradient most accurately. The four parameters describing the model resemble the statistical moments of the distribution and are related to chromatographic parameters, such as dwell volume and flow rate. The instrument-specific response function can then be used to predict the actual shape of any other gradient programmed on that instrument. To incorporate the predicted gradient in the retention modelling of the analytes, the model was extended to facilitate an unlimited number of linear gradient steps to solve the equations numerically. The significance and impact of distinct gradient deformation for fast gradients was demonstrated using three different LC instruments. As a proof of principle, the algorithm and retention parameters obtained on a specific instrument were used to predict the retention times on different instruments. The relative error in the predicted retention times went down from an average of 9.8% and 12.2% on the two other instruments when using only a dwell-volume correction to 2.1% and 6.5%, respectively, when using the proposed algorithm. The corrected retention parameters are less dependent on geometry-induced instrument effects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization., (© 2020 The Authors. Journal of Separation Science published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

In spatial multi-dimensional liquid chromatography (LC) devices the flow of each dimension has to remain in the corresponding region, otherwise the separation efficiency is undermined. Adequate flow-confinement measures are necessary. Here, the use of permeability differences across different compartments of spatial two-dimensional (2D) and three-dimensional (3D) LC devices as a method to guide fluid flow and reduce analyte loss during the first, second- and third-dimension development was investigated with computational fluid dynamics (CFD) simulations. In case of 2DLC devices, it was shown that porous barriers with a permeability on the order of 10 -12 m 2 suffice to keep the total sample spillage from an open 1 D channel under 1%. In case of 3DLC devices, it was shown that flow confinement could be achieved using an open 1 D channel in combination with a highly-permeable monolith (permeability on the order of 10 -12 m 2 ) in the second-dimension ( 2 D) and a less permeable packing with a permeability on the order of 10 -15 m 2 (e.g. 1 μm particles) in the third-dimension ( 3 D). Additionally, the impact of the 3 D flow-distributor has been studied and a novel design, capable of limiting the spillage to the other dimensions to the absolute minimum, is proposed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)