Improving the Accuracy of Predictive 2D-LC Optimization Strategies: Incorporation of Simulated Elution Profiles to Account for Injection Band Broadening in Online Comprehensive Two-Dimensional Liquid Chromatography.

Method development in online comprehensive two-dimensional liquid chromatography (LC × LC) requires the selection of a large number of experimental parameters. The complexity of this process has led to several computer-based LC × LC optimization algorithms being developed to facilitate LC × LC method development. One particularly relevant challenge for predictive optimization software is to accurately model the effect of second dimension (2D) injection band broadening under sample solvent mismatch and/or sample volume overload conditions. We report a novel methodology that combines a chromatographic numerical simulation model capable of predicting elution profiles of analytes under conditions where peak distortion occurs with a predictive multiparameter Pareto optimization approach for online LC × LC. Preliminary method optimization is performed using a theoretical model to predict 2D injection profiles, and optimal experimental configurations obtained from the Pareto fronts are then subjected to further optimization using the simulation model. This approach drastically reduces the number of simulations and therefore the computational demand. We show that the optimal experimental conditions obtained in this manner are similar to those obtained using a complete optimization using only the simulation model. Online HILIC × RP-LC separation of phenolic compounds was used to compare experimental data to simulated two- and three-dimensional contour plots. The main advantage of the proposed approach is the ability to predict the formation of split or deformed peaks in the 2D, a significant benefit in online LC × LC method optimization, especially for separation combinations with mismatched mobile phases. A further benefit is that simulated elution profiles can be used for the visualization of predicted two-dimensional chromatograms for method selection.

High-Throughput Liquid Chromatographic Analysis Using a Segmented Flow Injector with a 1 s Cycle Time.

High-throughput screening (HTS) workflows are revolutionizing many fields, including drug discovery, reaction discovery and optimization, diagnostics, sensing, and enzyme engineering. Liquid chromatography (LC) is commonly deployed during HTS to reduce matrix effects, distinguish isomers, and preconcentrate prior to detection, but LC separation time often limits throughput. Although subsecond LC separations have been demonstrated, they are rarely utilized during HTS due to limitations associated with the speed of common autosamplers. In this work, these limits are overcome by utilizing droplet microfluidics for sample introduction. In the method, a train of samples segmented by air are continuously pumped into the inlet of an LC injection valve that is actuated once each sample fills the sample loop. Coupled with 2.1 mm diameter × 5 mm long columns packed with 2.7 µm superficially porous C18 particles operated at 5 mL/min, the injector enabled separation of 3 components at 1 s/sample and analysis of a 96-well plate in 1.6 min with <2% peak area relative standard deviation. Analyte-dependent carryover was minimized by including wash droplets composed of organic solvent in between sample droplets. High-throughput LC coupled with mass spectrometric detection using the segmented flow injector was applied to a screen of inhibitors of a cytochrome P450-catalyzed hydroxylation reaction. Measurements of the reaction substrate and product concentrations made using fast LC with the segmented flow injector correlated well with measurements made using a more conventional, 3 min LC method. These results demonstrate the potential for droplet microfluidics to be used for sample introduction during high-throughput LC analysis.

Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography.

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).

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

In liquid chromatography, 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 liquid chromatography conditions. This allows us to discover challenges associated with predicting isocratic k values 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.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale.

At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (1 D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H2 O and D2 O washes using an independent pump setup; and 3) a second dimension separation (2 D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.

In Silico Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid Chromatography.

Continued adoption of two-dimensional liquid chromatography (2D-LC) in industrial laboratories will depend on the development of approaches to make method development for 2D-LC more systematic, less tedious, and less reliant on user expertise. In this paper, we build on previous efforts in these directions by describing the use of multifactorial modeling software that can help streamline and simplify the method development process for 2D-LC. Specifically, we have focused on building retention models for second dimension (2D) separations involving variables including gradient time, temperature, organic modifier blending, and buffer concentration using LC simulator (ACD/Labs) software. Multifactorial retention modeling outcomes are illustrated as resolution map planes or cubes that enable straightforward location of 2D conditions that maximize resolution while minimizing analysis time. We also illustrate the practicality of this approach by identifying conditions that yield baseline separation of all compounds co-eluting from a first dimension (1D) separation using a single combination of 2D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the 1D and 2D separations, with differences between experimental and simulated retention times of less than 0.5%. Pharmaceutical applications of this approach for multiple heartcutting 2D-LC were demonstrated using IEC-IEC or achiral RPLC-chiral RPLC for 2D separations of multicomponent mixtures. The framework outlined here should help make 2D-LC method development more systematic and streamline development and optimization for a variety of 2D-LC applications in both industry and academia.

Mapping the Separation Landscape in Two-Dimensional Liquid Chromatography: Blueprints for Efficient Analysis and Purification of Pharmaceuticals Enabled by Computer-Assisted Modeling.

Recent developments in two-dimensional liquid chromatography (2D-LC) now make separation and analysis of very complex mixtures achievable. Despite being such a powerful chromatographic tool, current 2D-LC technology requires a series of arduous method development activities poorly suited for a fast-paced industrial environment. Recent introductions of new technologies including active solvent modulation and a support for multicolumn 2D-LC are helping to overcome this stigma. However, many chromatography practitioners believe that the lack of a systematic way to effectively optimize 2D-LC separations is a missing link in securing the viability of 2D-LC as a mainstay for industrial applications. In this work, a computer-assisted modeling approach that dramatically simplifies both offline and online 2D-LC method developments is introduced. Our methodology is based on mapping the separation landscape of pharmaceutically relevant mixtures across both first (1D) and second (2D) dimensions using LC Simulator (ACD/Labs) software. Retention models for 1D and 2D conditions were built using a minimal number of multifactorial modeling experiments (2 × 2 or 3 × 3 parameters: gradient slope, column temperature, and different column and mobile phase combinations). The approach was first applied to online 2D-LC analysis involving achiral and chiral separations of complex mixtures of enantiomeric species. In these experiments, the retention models proved to be quite accurate for both the 1D and 2D separations, with retention time differences between experiments and simulations of less than 3.5%. This software-based concept was also demonstrated for offline 2D-LC purification of drug substances.

Orthogonal Middle-up Approaches for Characterization of the Glycan Heterogeneity of Etanercept by Hydrophilic Interaction Chromatography Coupled to High-Resolution Mass Spectrometry.

Etanercept is a recombinant Fc fusion protein widely used to treat rheumatic diseases. This protein is highly glycosylated and contains numerous O- and N-glycosylation sites. Since glycosylation is recognized as an important critical quality attribute (CQA) that can affect immunogenicity, solubility, and stability of Fc fusion proteins, it should be thoroughly characterized. In this work, hydrophilic interaction chromatography (HILIC) was combined with high-resolution mass spectrometry (HRMS) by using a quadrupole time-of flight mass spectrometer to assess glycosylation of etanercept at the middle-up level of analysis (fragments of ca. 25-30 kDa). In addition, a combination of different enzymatic digestion procedures (i.e., glycosidase, sialidase, and protease) was systematically employed to facilitate spectra deconvolution. With the developed procedure, the main post-translational modifications (PTMs) of etanercept were assessed, and a global overview of the subunit-specific distribution of the glycosylation pattern was obtained at a middle-up level of analysis.

Development of Comprehensive Online Two-Dimensional Liquid Chromatography/Mass Spectrometry Using Hydrophilic Interaction and Reversed-Phase Separations for Rapid and Deep Profiling of Therapeutic Antibodies.

Monoclonal antibodies (mAb) and related molecules are being developed at a remarkable pace as new therapeutics for the treatment of diseases ranging from cancer to inflammatory disorders. However, characterization of these molecules at all stages of development and manufacturing presents tremendous challenges to existing analytical technologies because of their large size (ca. 150 kDa) and inherent heterogeneity resulting from complex glycosylation patterns and other post-translational modifications. Multidimensional liquid chromatography is emerging as a powerful platform technology that can be used to both improve analysis speed for these molecules by combining existing one-dimensional separations into a single method (e.g., Protein A affinity separation and size-exclusion chromatography) and increasing the resolving power of separations by moving from one dimension of separation to two. In the current study, we have demonstrated the ability to combine hydrophilic interaction (HILIC) and RP separations in an online comprehensive 2D separation coupled with high resolution MS detection (HILIC × RP-HRMS). We find that active solvent modulation (ASM) is critical for coupling these two separation modes, because it mitigates the otherwise serious negative impact of the acetonitrile-rich HILIC mobile phase on the second dimension RP separation. The chromatograms obtained from these HILIC × RP-HRMS separations of mAbs at the subunit level reveal the extent of glycosylation on the Fc/2 and Fd subunits in analysis times on the order of 2 h. In comparison to previous CEX × RP separations of the same molecules, we find that chromatograms from the HILIC × RP separations are richer and reveal separation of some glycoforms that coelute in the CEX × RP separations.

Two-Dimensional Liquid Chromatography: A State of the Art Tutorial.

In this tutorial, we discuss the motivations for doing two-dimensional liquid chromatography (2D-LC) and describe the commonly used implementations of the method. We review important guiding principles for method development, discuss the state of the art in 2D-LC performance as measured by peak capacity, and describe example applications from different fields that we hope will inspire new users to adopt 2D-LC for their analytical problems.

Active Solvent Modulation: A Valve-Based Approach To Improve Separation Compatibility in Two-Dimensional Liquid Chromatography.

Two-dimensional liquid chromatography (2D-LC) is increasingly being viewed as a viable tool for solving difficult separation problems, ranging from targeted separations of structurally similar molecules to untargeted separations of highly complex mixtures. In spite of this performance potential, though, many users find method development challenging and most frequently cite the "incompatibility" between the solvent systems used in the first and second dimensions as a major obstacle. This solvent strength related incompatibility can lead to severe peak distortion and loss of resolution and sensitivity in the second dimension. In this paper, we describe a novel approach to address the incompatibility problem, which we refer to as Active Solvent Modulation (ASM). This valve-based approach enables dilution of 1D effluent with weak solvent prior to transfer to the 2D column but without the need for additional instrument hardware. ASM is related to the concept we refer to as Fixed Solvent Modulation (FSM), with the important difference being that ASM allows toggling of the diluent stream during each 2D separation cycle. In this work, we show that ASM eliminates the major drawbacks of FSM including complex elution solvent profiles, baseline disturbances, and slow 2D re-equilibration and demonstrate improvements in 2D separation quality using both simple small molecule probes and degradants of heat-treated bovine insulin as case studies. We believe that ASM will significantly ease method development for 2D-LC, providing a path to practical methods that involve both highly complementary 1D and 2D separations and sensitive detection.

Direct identification of rituximab main isoforms and subunit analysis by online selective comprehensive two-dimensional liquid chromatography-mass spectrometry.

In this proof-of-concept study, rituximab, which is a reference therapeutic monoclonal antibody (mAb), was characterized through the implementation of online, selective comprehensive two-dimensional liquid chromatography (sLC×LC) coupled with mass spectrometry (MS), using a middle-up approach. In this setup, cation exchange chromatography (CEX) and reverse-phase liquid chromatography (RPLC) were used as the first and second separation dimensions, respectively. As illustrated in this work, the combination of these two chromatographic modes allows a direct assignment of the identities of CEX peaks, using data from the TOF/MS detector, because RPLC is directly compatible with MS detection, whereas CEX is not. In addition, the resolving power of CEX is often considered to be limited; therefore, this 2D approach provides an improvement in peak capacity and resolution when high-performance second-dimension separations are used, instead of simply using the second-dimension separation as a desalting step. This was particularly relevant when separating rituximab fragments of medium size (25 kDa), whereas most of the resolution was provided by CEX in the case of intact rituximab samples. The analysis of a commercial rituximab sample shows that online sLC×LC-TOF-MS can be used to rapidly characterize mAb samples, yielding the identification of numerous variants, based on the analysis of intact, partially digested, and digested/reduced mAb samples.

Evaluation of detection sensitivity in comprehensive two-dimensional liquid chromatography separations of an active pharmaceutical ingredient and its degradants.

In this paper, we describe the findings of a study aimed at assessing the detection sensitivity of comprehensive two-dimensional high-performance liquid chromatography (LCxLC) separation of a degraded active pharmaceutical ingredient (API) with UV absorption as the detection technique. Specifically, we have examined the impact of the volume and solvent composition (referred to as "interface conditions") of fractions of first-dimension column effluent transferred to the second dimension for further separation on the ability to resolve and detect low-abundance compounds. Historically, LCxLC has been perceived as being inferior to 1D-LC from the point of view of detection sensitivity. In this work, we demonstrate that LCxLC is sufficiently sensitive to be useful in the pharmaceutical context where in general impurities present at 0.05 % (relative to the API concentration) should be quantified. Moreover, we find that this level of sensitivity is only attained under certain conditions: dilution of the first column effluent with weak solvent (water in this case) prior to injection into the second-dimension column is very beneficial because it promotes focusing of the analyte band in the second column, thereby improving the detection sensitivity of the LCxLC system; and, quantitation limits are also a strong function of peak location in the second-dimension separation window, where baseline disturbances near the dead time of the second column can limit reliable detection of low-abundance compounds.

Development of multiple heartcutting two-dimensional liquid chromatography with ion-pairing reversed-phase separations in both dimensions for analysis of impurities in therapeutic oligonucleotides.

Oligonucleotides constitute an emerging and highly complex bioanalytical challenge and it is becoming increasingly clear that 1D methodologies are unable to fully resolve all possible impurities present in these samples. 2D-LC therefore constitutes a perfect solution wherein critical pairs can be sampled from a steep gradient 1D and separated in a shallower 2D gradient. Herein, we provide a facile 2D-LC method development approach to quickly generate high selectivity gradients utilizing ion pairing reverse phase (IPRP-IPRP). In particular we demonstrate how to iteratively generate a 12 % gradient from two training runs and then to utilize that data to predict retentions of analytes with a 2 % gradient with retention prediction errors as low as 3 and 11 %, respectively. This iterative method development workflow was applied to impurity profiling down to 1:1000 for the full-length product and phosphorothioate modified impurities. Additionally, we demonstrated the elucidation of critical pairs in complex crude pharmaceutical oligonucleotide samples by applying tailored high selectivity gradients in the second dimension. It was found that the iterative retention modeling approach allows fast and facile 2D-LC method development for complex oligonucleotide separations.

Fingerprinting of hydroxy propyl methyl cellulose by comprehensive two-dimensional liquid chromatography-mass spectrometry of monomers resulting from acid hydrolysis.

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 ß-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.

Enhancing LC×LC separations through multi-task Bayesian optimization.

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.

Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity.

Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity.

Characterization of fullerene-modified silica as a complement to graphite-like phases for use in two-dimensional high performance liquid chromatography.

Carbonaceous sorbents have a long and rich history of development and application in all areas of separation science. Interest in these materials has been fueled by observations that solute-sorbent interactions are mainly adsorptive in nature and thus selectivities are frequently quite different from what is observed with small organic ligands bonded to porous substrates. However, despite over four decades of intense study and development of these materials for use in reversed-phase liquid chromatography, wide adoption continues to be hindered by a few significant, negative attributes of these materials, most notably irreversible adsorption and poor peak shape and separation efficiency for some classes of compounds. In this work we describe the results of a study aimed at characterization of C60 fullerene-modified silica (FMS) materials that we believe nicely complement existing graphite-like carbonaceous phases for use in liquid chromatography. Since their first synthesis about 20 years ago, FMS materials have received surprisingly little attention, which has been focused mainly on the separation of highly aromatic compounds. Here, we use retention data for well-established sets of both nonionizable and ionizable low molecular weight probe solutes to demonstrate that FMS both exhibits graphite-like characteristics (i.e., selectivity for structural isomers and enhanced retention of polar compounds) and has selectivity characteristics that are largely unique in comparison to over 600 other materials used for reversed-phase liquid chromatography. In addition, FMS exhibits much improved peak shape and separation efficiency for compounds that are known to be problematic when separated by use of graphite-like phases. This combination of attributes makes FMS an excellent complement to graphite-like phases for use in two-dimensional liquid chromatography, where unique selectivity compared to conventional bonded reversed-phase materials, along with good peak shape and separation efficiency are of paramount importance for successful two-dimensional liquid chromatographic separations.