Measuring and using scanning-gradient data for use in method optimization for liquid chromatography

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Quartz Crystal Microbalance as a Holistic Detector for Quantifying Complex Organic Matrices during Liquid Chromatography: 2. Compound-Specific Isotope Analysis

In carbon-compound-specific isotope analysis (carbon CSIA) of environmental micropollutants, purification of samples is often required to guarantee accurate measurements of a target compound. A companion paper has brought forward an innovative approach to couple a quartz crystal microbalance (QCM) with high-performance liquid chromatography (HPLC) for the online quantification of matrices during a gradient HPLC purification. This work investigates the benefit for isotope analysis of polar micropollutants typically present in environmental samples. Here, we studied the impact of the natural organic matter (NOM) on the isotopic integrity of model analytes and the suitability of the NOM-to-analyte ratio as a proxy for the sample purity. We further investigated limitations and enhancement of HPLC purification using QCM on C18 and C8 phases for single and multiple targets. Strong isotopic shifts of up to 3.3% toward the isotopic signature of NOM were observed for samples with an NOM-to-analyte ratio ≥10. Thanks to QCM, optimization of matrix removal of up to 99.8% of NOM was possible for late-eluting compounds. The efficiency of HPLC purification deteriorated when aiming for simultaneous purification of two or three compounds, leading to up to 2.5% less NOM removal. Our results suggest that one optimized HPLC purification can be achieved through systematic screening of 3 to 5 different gradients, thereby leading to a shift of the boundaries of accurate carbon CSIA by up to 2 orders of magnitude toward lower micropollutant concentrations.

Characterization of hybrid organo-silica monoliths for possible application in the gradient elution of peptides

We characterized thermally polymerized organo-silica hybrid monolithic capillaries to test their applicability in the gradient elution of peptides. We have used a single-pot approach utilizing 3-(methacryloyloxy)propyltrimethoxysilane (MPTMS), ethylene dimethacrylate (EDMA), and n-octadecyl methacrylate (ODM) as functional monomers. The organo-silica monolith containing MPTMS and EDMA was compared with the stationary phase prepared by adding ODM to the original polymerization mixture. Column prepared using a three-monomer system provided a lower accessible volume of flow-through pores, a higher proportion of mesopores, and higher efficiency. We utilized isocratic and gradient elution data to predict peak widths in gradient elution. Both protocols provided comparable results and can be used for peptide peak width prediction. However, applying gradient elution data for peak width prediction seems simpler. Finally, we tested the effect of gradient time on achievable peak capacity in the gradient elution of peptides with a column prepared with a three-monomer system providing a higher peak capacity. However, the performance of hybrid organo-silica monolithic stationary phases in gradient elution of peptides must be improved compared to other monolithic stationary phases. The limiting factor is column efficiency in highly aqueous mobile phases, which needs to be focused on.

Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography

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.

Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation

Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.

A Strategy for assessing peak purity of pharmaceutical peptides in reversed-phase chromatography methods using two-dimensional liquid chromatography coupled to mass spectrometry. Part II: Development of second-dimension gradient conditions

The importance of therapeutic peptides continues to increase in the marketplace for treating a range of diseases including diabetes and obesity. Quality control analyses for these pharmaceutical ingredients usually depends on reversed-phase liquid chromatography, and it is critically important to ensure that no impurities coelute with the target peptide at levels that would compromise the safety or effectiveness of the drug products. This can be challenging due to the broad range of properties of impurities that can be present on one hand (e.g., amino acid substitutions, chain cleavages, etc.), and the similarity of other impurities on the other hand (e.g., d-/l-isomers). Two-dimensional liquid chromatography (2D-LC) is a powerful analytical tool that is well suited to address this particular problem; the first dimension can be used to detect impurities over a broad range in properties, while the second dimension can be used to focus specifically on those species that might coelute with the target peptide in the first dimension. While hundreds of papers have been published on the use of 2D-LC for proteomics applications, there are very few papers that have focused on its use for characterisation of therapeutic peptides. This paper is the second in a two-part series. In Part I of the series, we studied several different column / mobile phase combinations that could be useful in 2D-LC separations of therapeutic peptides, with a focus on selectivity, peak shape, and complementarity to other combinations, particularly for isomeric peptides under mass spectrometry-friendly conditions (i.e., volatile buffers). In this second part in the series, we describe a strategy to derive second-dimension (²D) gradient conditions that both, ensure elution from the ²D column, and increase the likelihood of resolving peptides with very similar properties. We find that a two-step process yields conditions that place the target peptide in the middle of the ²D chromatogram. This process begins with two scouting gradient elution conditions in the second dimension of a 2D-LC system, followed by building and refining a retention model for the target peptide using a third separation. The process is shown to be generically useful by developing methods for four model peptides, and application to a sample of degraded model peptide to demonstrate its utility for resolving impurities in a real sample.

A detailed evaluation of the advantages and limitations of online RP-LC×HILIC compared to HILIC×RP-LC for phenolic analysis

The combination of hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RP-LC) has proved effective in the LC × LC analysis of polyphenols due to the high degree of orthogonality associated with these separation modes for various classes of phenolic compounds. However, despite the growing number of such applications, HILIC is almost exclusively used as the first dimension (¹D) separation mode, and RP-LC in the second dimension (²D). This is somewhat surprising in light of the potential advantages of swapping these separation modes. In this contribution, we present a detailed evaluation of the potential of online RP-LC × HILIC-MS for the analysis of phenolic compounds, comparing the performance of this system to the more established HILIC × RP-LC-MS configuration. Method development was performed using a predictive optimisation program, and fixed solvent modulation was employed to combat the solvent incompatibility between HILIC and RP-LC mobile phases. Red wine, rooibos tea, Protea and chestnut phenolic extracts containing a large diversity of phenolic compound classes were analysed by both HILIC × RP-LC- and RP-LC × HILIC-MS in order to compare the separation performance. Overall, the kinetic performance of HILIC × RP-LC was found to be clearly superior, with higher peak capacities and better resolution obtained for the majority of samples compared to RP-LC × HILIC analyses using similar column dimensions. Dilution of the ¹D solvent combined with large volume injections proved insufficient to focus especially phenolic acids in the ²D HILIC separation, which resulted in severe ²D peak distortion for these compounds, and negatively impacted on method performance. On the other hand, a noteworthy improvement in the sensitivity of RP-LC × HILIC-MS analyses was observed due to higher ESI-MS response for the ²D HILIC mobile phase and greater sample loading capacity of the ¹D RP-LC column, brought on by the high solubility of phenolic samples in aqueous solutions. As a result, a significantly higher number of compounds were detected in the RP-LC × HILIC-MS separations. These findings point to the potential advantage of RP-LC × HILIC as a complementary configuration to HILIC × RP-LC for phenolic analysis.

Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization

Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.

Retention prediction of monoamine neurotransmitters in gradient liquid chromatography

Retention prediction of monoamine neurotransmitters has been compared for the generally applied linear solvent-strength model and quadratic polynomial three-parameter model. The design of experiments protocol has been applied to plan linear gradients within the experimental space with altered gradient time, mobile phase flowrate, and column temperature. Relative prediction errors increased at elevated temperature, which is more significant for the linear solvent-strength model when compared to the polynomial model. On the other hand, the predefined design of experiments space controls the retention time errors, as predictions for LC conditions that are outside of the plan are much less accurate and should be avoided. The final part of the work deals with the effect of extracolumn band dispersion on the peak capacity of linear gradients at various gradient times, mobile phase flowrates, and column temperature. The peak capacity determined for corrected experimental data were consistent with the published results dealing with the optimization of peak capacity in gradient elution. This article is protected by copyright. All rights reserved.

An approach to high throughput measurement of accurate retention data in liquid chromatography

Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on "overhead" associated with the instrument itself - for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference - which we interpret as an error in measurements based on data from the 5 mm column - to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.

Perspectives on the Use of Retention Modeling to Streamline 2D-LC Method Development: Current State and Future Prospects

The history of multidimensional liquid chromatography (MDLC) has been dominated by methods that have been developed using highly empirical, experience-driven, trial-and-error approaches. These approaches have been sufficient in progressing the field forward scientifically, primarily in academic research laboratories. However, more widespread usage of multidimensional separations will require more systematic approaches to method development that rely less on user experience and lower the barriers to development and use of methods by a wider community of users. In this mini-review, we discuss recent research aimed at developing such systematic, model-driven approaches to streamline method development and speculate about likely advances in the same direction in the near future. It seems likely that such model-driven approaches would be particularly helpful for methods developed for analyzing biopharmaceutical molecules, which tend to be very sensitive to slight changes in method conditions (for example, mobile phase composition).

Accuracy of retention model parameters obtained from retention data in liquid chromatography

In liquid chromatography (LC), it is often very useful to have an accurate model of the retention factor, k, over a wide range of isocratic elution conditions. In principle, the parameters of a retention model can be obtained by fitting either isocratic or gradient retention factor data. However, in spite of many of our own attempts to accurately predict isocratic k values using retention models trained with gradient retention data, this has not worked in our hands. In the present study we have used synthetic isocratic and gradient retention data for small molecules under reversed-phase LC conditions. This allows us to discover challenges associated with predicting isocratic k's without the confounding influences of experimental issues that are difficult to model or eliminate. The results indicate that it is not currently possible to consistently predict isocratic retention factors for small molecules with accuracies better than 10%, even when using synthetic gradient retention data. Two distinct challenges in fitting gradient retention data were identified: 1) a lack of 'uniqueness' in the parameters; and 2) an inability to find the global optimum fit in a complex fitting landscape. Working with experimental data where measurement noise is unavoidable will only make the accuracy worse. This article is protected by copyright. All rights reserved.

Bayesian optimization of comprehensive two-dimensional liquid chromatography separations

Comprehensive two-dimensional liquid chromatography (LC×LC), is a powerful, emerging separation technique in analytical chemistry. However, as many instrumental parameters need to be tuned, the technique is troubled by lengthy method development. To speed up this process, we applied a Bayesian optimization algorithm. The algorithm can optimize LC×LC method parameters by maximizing a novel chromatographic response function based on the concept of connected components of a graph. The algorithm was benchmarked against a grid search (11,664 experiments) and a random search algorithm on the optimization of eight gradient parameters for four different samples of 50 compounds. The worst-case performance of the algorithm was investigated by repeating the optimization loop for 100 experiments with random starting experiments and seeds. Given an optimization budget of 100 experiments, the Bayesian optimization algorithm generally outperformed the random search and often improved upon the grid search. Moreover, the Bayesian optimization algorithm offered a considerably more sample-efficient alternative to grid searches, as it found similar optima to the grid search in far fewer experiments (a factor of 16-100 times less). This could likely be further improved by a more informed choice of the initialization experiments, which could be provided by the analyst's experience or smarter selection procedures. The algorithm allows for expansion to other method parameters (e.g., temperature, flow rate, etc.) and unlocks closed-loop automated method development.

Recent applications of retention modelling in liquid chromatography

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.