CIUSuite 3: Next-Generation CCS Calibration and Automated Data Analysis Tools for Gas-Phase Protein Unfolding Data.

Ion mobility-mass spectrometry (IM-MS) has become a technology deployed across a wide range of structural biology applications despite the challenges in characterizing closely related protein structures. Collision-induced unfolding (CIU) has emerged as a valuable technique for distinguishing closely related, iso-cross-sectional protein and protein complex ions through their distinct unfolding pathways in the gas phase. With the speed and sensitivity of CIU analyses, there has been a rapid growth of CIU-based assays, especially regarding biomolecular targets that remain challenging to assess and characterize with other structural biology tools. With information-rich CIU data, many software tools have been developed to automate laborious data analysis. However, with the recent development of new IM-MS technologies, such as cyclic IM-MS, CIU continues to evolve, necessitating improved data analysis tools to keep pace with new technologies and facilitating the automation of various data processing tasks. Here, we present CIUSuite 3, a software package that contains updated algorithms that support various IM-MS platforms and supports the automation of various data analysis tasks such as peak detection, multidimensional classification, and collision cross section (CCS) calibration. CIUSuite 3 uses local maxima searches along with peak width and prominence filters to detect peaks to automate CIU data extraction. To support both the primary CIU (CIU1) and secondary CIU (CIU2) experiments enabled by cyclic IM-MS, two-dimensional data preprocessing is deployed, which allows multidimensional classification. Our data suggest that additional dimensions in classification improve the overall accuracy of class assignments. CIUSuite 3 also supports CCS calibration for both traveling wave and drift tube IM-MS, and we demonstrate the accuracy of a new single-field CCS calibration method designed for drift tube IM-MS leveraging calibrant CIU data. Overall, CIUSuite 3 is positioned to support current and next-generation IM-MS and CIU assay development deployed in an automated format.

Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis.

INTRODUCTION: The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market. AREAS COVERED: This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder. EXPERT OPINION: IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers.

Sensitive analytical techniques that are capable of detecting and quantifying disease-associated biomolecules are indispensable in our efforts to understand disease mechanisms and guide therapeutic intervention through early detection, accurate diagnosis, and effective monitoring of disease. Parkinson's Disease (PD), for example, is one of the most prominent neurodegenerative disorders in the world, but the diagnosis of PD has primarily been based on the observation of clinical symptoms. The protein α-synuclein (α-syn) has emerged as a promising biomarker candidate for PD, but a lack of analytical methods to measure complex disease-associated variants of α-syn has prevented its widespread use as a biomarker. Antibody-based methods such as immunoassays and mass spectrometry-based approaches have been used to measure a limited number of α-syn forms; however, these methods fail to differentiate variants of α-syn that display subtle differences in only the sequence and structure. In this work, we developed a cyclic ion mobility-mass spectrometry method that combines multiple stages of activation and timed ion selection to quantify α-syn variants using both mass- and structure-based measurements. This method can allow for the quantification of several α-syn variants present at physiological levels in biological fluid. Taken together, this approach can be used to galvanize future efforts aimed at understanding the underlying mechanisms of PD and serves as a starting point for the development of future protein-structure-based diagnostics and therapeutic interventions.

High-Throughput Capillary Liquid Chromatography Using a Droplet Injection and Application to Reaction Screening.

The cycle time of a standard liquid chromatography (LC) system is the sum of the time for the chromatographic run and the autosampler injection sequence. Although LC separation times in the 1-10 s range have been demonstrated, injection sequences are commonly >15 s, limiting throughput possible with LC separations. Further, such separations are performed on relatively large bore columns requiring flow rates of ≥5 mL/min, thus generating large volumes of mobile phase waste when used for large scale screening and increasing the difficulty in interfacing to mass spectrometry. Here, a droplet injector system was established that replaces the autosampler with a four-port, two-position valve equipped with a 20 nL internal loop interfaced to a syringe pump and a three-axis positioner to withdraw sample droplets from a well plate. In the system, sample and immiscible fluid are pulled alternately from a well plate into a capillary and then through the injection valve. The valve is actuated when sample fills the loop to allow sequential injection of samples at high throughput. Capillary LC columns with 300 µm inner diameter were used to reduce the consumption of mobile phase and sample. The system achieved 96 separations of 20 nL droplet samples containing 3 components in as little as 8.1 min with 5-s cycle time. This system was coupled to a mass spectrometer through an electrospray ionization source for high-throughput chemical reaction screening.

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.

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.

In silico method development for the reversed-phase liquid chromatography separation of proteins using chaotropic mobile phase modifiers.

Recent advances in biomedical and pharmaceutical processes has enabled a notable increase of protein- and peptide-based drug therapies and vaccines that often contain a higher-order structure critical to their efficacy. Hyphenation of chromatographic and spectrometric techniques is at the center of all facets of biopharmaceutical analysis, purification and chemical characterization. Although computer-assisted chromatographic modeling of small molecules has reached a mature stage across the pharmaceutical industry, software-based method optimization approaches for large molecules has yet to see the same revitalization. Conformational changes of biomolecules under chromatographic conditions have been identified as the major culprit in terms of sub-optimal modeling outcomes. In order to circumvent these challenges, we herein investigate the outcomes generated via computer-assisted modeling from using different chaotropic and denaturing mobile phases (trifluoroacetic acid, sodium perchlorate and guanidine hydrochloride in acetonitrile/water-based eluents). Linear and polynomial regression retention models using ACD/Labs software were built as a function of gradient slope, column temperature and mobile phase buffer for eight different model proteins ranging from 12 to 670 kDa (holo-transferrin, cytochrome C, apomyoglobin, ribonuclease A, ribonuclease A type I-A, albumin, y-globulin and thyroglobulin bovine). Correlation between experimental and modeled outputs was substantially improved by using strong chaotropic and denaturing modifiers in the mobile phase, even when using linear regression modeling as typically observed for small molecules. On the contrary, the use of conventional TFA buffer concentrations at low column temperatures required the used of polynomial regression modeling indicating potential conformational structure changes of proteins upon chromatographic conditions. In addition, we illustrate the power of modern computer-assisted chromatography modeling combined with chaotropic agents in the developing of new RPLC assays for protein-based therapeutics and vaccines.

Measurement and modelling of the intra-particle diffusion and b-term in reversed-phase liquid chromatography.

In an ongoing effort to better understand the underlying mechanisms of band broadening in particle-packed reversed-phase liquid chromatography columns, new models for intra-particle diffusion, representing an adsorption- and partition-type retention behavior, are proposed. These models assume the mesoporous zone inside the particles is subdivided in four distinct regions: a fraction f1 filled with bulk mobile phase, a fraction f2 enriched in pure organic modifier extending outside the stationary phase layer, a fraction f3 comprising the liquid surrounding the alkyl chains and a fraction f4 consisting of the stationary phase alkyl chains. Intra-particle diffusion is calculated as a residence time weighted average of the diffusion in these different regions. Experimental procedures and models are proposed to determine the volumes of these four regions and applied to three reversed-phase liquid chromatography columns with different pore sizes (80 Å versus 300 Å) and different stationary phase types (C18 versus C8). The newly proposed models are then applied to predict the intra-particle diffusion of butyrophenone across a wide range of retention factors (1 ≤ k" ≤ 40) in each of these columns. These predictions are compared to experimental data that are extracted from the effective diffusion coefficients of butyrophenone obtained via peak parking experiments. It is demonstrated that both adsorption- and partition-type models for intra-particle diffusion model the actual behavior of the test compound well, and require the determination of only one (partition) or two (adsorption) fitting factors: the obstruction to free movement the analytes experience from the alkyl chains in the retained state (partition and adsorption) and in the unretained state (adsorption). Finally, it is demonstrated that the major contributor to the intra-particle diffusion of retained compounds (k" > 2) is the diffusion these analytes undergo when retained in the organic-modifier enriched zone surrounding the alkyl chains (partition model) or when adsorbed onto the alkyl chains (adsorption model), confirming that surface diffusion plays an important role in the mass transfer of retained compounds in reversed-phase liquid chromatography columns.

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.

Experimental investigation of the retention factor dependency of eddy dispersion in packed bed columns and relation to knox's empirical model parameters.

We report on a systematic and comprehensive (0.7 ≤ k'' ≤ 122) experimental study of the effect of the zone retention factor k'' on eddy dispersion (heddy) in packed bed columns for liquid chromatography. The values for heddy are obtained by subtracting rigorously estimated contributions to the total plate height from longitudinal diffusion (hB) and the mobile (hCm) and stationary zone (hCs) mass transfer resistances. For the first time, hCm-values are calculated using an expression for the Sherwood number (Sh) that has been established and validated in the relevant velocity range. Experiments were carried out on both a fully-porous and a core-shell particle column. In both cases, the eddy dispersion systematically decreased with increasing retention factor k'', dropping 0.5 to 0.8 reduced plate height units when going from the lowest to the highest k''. To establish a simple empirical fitting equation that can represent the observed effects, the widely used power law-based Knox model has been extended to express the dependence of its A- and n-parameters on the retention factor.