The Emergence of Universal Chromatographic Methods in the Research and Development of New Drug Substances

Digital Restoration of Separations Data

In experimental separations, the acquired signal can have four common but unwanted elements: (i) high-frequency noise, (ii) drift, (iii) occasional periodic and pink noise, and (iv) a mixture of components traveling with close velocities. Additionally, separation scientists observe partially resolved peaks in chromatographic or capillary electrophoresis detector output. A protocol for digitally recovering the "true data" is provided with clear-cut details and open-access codes for formulating denoising, baseline correction, and peak resolution problems as linear or nonlinear optimizations. The smoother of Bohlmann-Whittaker denoises and preserves the total area of the signal. Fourier transform methods to detect and remove periodic noise are also proposed for erratic periodic noise problems. A peak width preserving filter, called the bilateral filter, is also given. This procedure is followed by two reliable baseline correction algorithms based on asymmetric reweighted least squares or curvature-based weights to deal with low-frequency signal variations. Once low- and high-frequency noise components are removed, a convenient peak mode location method is introduced, relying upon numerically stable second derivative calculation from Savitzky-Golay (SG) filter. Peak locations, area, height, and higher statistical moments can be derived from iterative curve fitting. The protocol shows two peak models that can model left or right skewed peaks. The new peak models are (i) a numerically stable version of the bi-directional exponentially modified Gaussian (EMG), suitable for a wide variety of real chromatograms, and (ii) the twice generalized normal model with third- and fourth-moment parameters built into it. The nonlinear least squares regression approach uses the trust-region reflective optimization method. The algorithm converges to a solution even with multiple peaks and approximate guesses of peak parameters. The protocol can be generalized to fit more than 200 peak functions in separation sciences available in literature by minor adaptations of the provided codes in the MATLAB environment.

Strategies for automated affinity purification-resin screening for non-traditional biopharmaceuticals in the discovery space

Biotherapeutics occupy a significant portion of the pharmaceutical pipeline and are projected to continue growing in sales and scope. Further, the field is advancing novel and more complex molecules beyond monoclonal antibodies including multi-target proteins, engineered proteins and bioconjugates. In this aspect, the development of increasingly advanced and challenging therapies necessitates a commiserate degree of innovation to develop automated methods for resin screening, purification, and analytics in the discovery space to quickly identify liabilities and rank candidates with minimal impact on developmental resources. In this work, we introduce an automated resin screening platform tailored to small scale production runs for clone evaluation and process development in the biologics discovery space. The complex characteristics of these novel therapies requires empirical testing of resin to ensure optimal recovery of high-quality material for evaluation to inform on cell line development and future downstream process and analytical method development. This workflow enables the purification of milligrams of protein material for analytical testing and identifies ideal resins to leverage downstream as a candidate quickly progresses. This workflow was validated using a research monoclonal antibody and applied to a novel bispecific fusion protein to evaluate resin performance with respect to recovery, purity and impact on higher-order structure.

Ion exchange chromatography of biotherapeutics: Fundamental principles and advanced approaches

Ion exchange chromatography (IEX) is an important analytical technique for the characterization of biotechnology-derived products, such as monoclonal antibodies (mAbs) and more recently, cell and gene therapy products such as messenger ribonucleic acid (mRNA) and adeno-associated viruses (AAVs). This review paper first outlines the basic principles and separation mechanisms of IEX for charge variant separation of biotherapeutics, and examines the different elution modes based on salt or pH gradients. It then highlights several recent trends when applying IEX for the characterization of biotechnology-derived products, including: i) the effective use of pH gradients, ii) the improvement of selectivity by using organic solvents in the mobile phase, multi-step gradients, or by combining ion pairing and ion exchange, and iii) the increase in analytical throughput using ultra-short columns or automated screening of conditions. The review also discusses the incorporation of IEX into multidimensional liquid chromatography setups, integrating it with other chromatographic dimensions for the analysis of complex biotherapeutic products. It also covers the coupling of IEX with mass spectrometry (MS), ion mobility spectrometry (IMS), and multi-angle light scattering (MALS) to identify the various species contained in complex biotherapeutic samples. In conclusion, IEX is considered today as an essential technique in the analytical toolbox for the characterization and quality control of biotechnology-derived products. It offers a unique separation mechanism and can be coupled with highly informative detectors, such as MS and MALS.

Introducing macrocyclic glycopeptide columns as unique achiral stationary phases: Insights from hydrophobic subtraction model and in-silico modeling

BACKGROUND

The need for stationary phases with unique selectivity in reversed-phase liquid chromatography has been of utmost importance to chromatographers for advancing the analysis of complex samples. Macrocyclic glycopeptide based stationary phases have been widely used for chiral separations with different chromatographic modes such as normal phase, reversed phase, and supercritical fluid chromatography. Given the multimodal retention mechanisms namely π-π complex interaction, hydrogen bonding, dipole-dipole interaction, and strong Coulombic interactions by which analytes are separated using the macrocyclic glycopeptides, these stationary phases are expected to provide novel selectivity when used under the reversed phase for achiral separations.

RESULTS

Herein, for the first time we have conducted a systematic study using the improved hydrophobic subtraction model (HSM) which incoporates dipole-dipole interactions to demonstrate the novel selectivity offered by four different macrocyclic glycopeptide based stationary phases, namely NicoShell, TeicoShell, TagShell, and VancoShell. A comparison of the HSM parameters for these columns has been made with 551 commercially available reversed phase stationary phases and the differences in the values point to the importance of adding these columns to the already existing arsenal. These stationary phases offer separations over a wide range of pH and show variability in selectivity depending on the pH of the mobile phase which make them versatile for method development in the reversed phase mode. Additionally, we have provided an actual example of a separation from an Amgen discovery project using the VancoShell column aided by computer-assisted modelling.

SIGNIFICANCE

This is the first report characterizing macrocyclic glycopeptides for achiral RPLC applications. The selectivity of these stationary phases were found to be unique when compared to other commercially available stationary phases thereby acting as their own class of columns. The unusual selectivity of the columns enabled separation of complex pharmaceutical samples.

Digitally Enabled Generic Analytical Framework Accelerating the Pace of Liquid Chromatography Method Development for Vaccine Adjuvant Formulations

The growing use of adjuvants in the fast-paced formulation of new vaccines has created an unprecedented need for meaningful analytical assays that deliver reliable quantitative data from complex adjuvant and adjuvant-antigen mixtures. Due to their complex chemical and physical properties, method development for the separation of vaccine adjuvants is considered a highly challenging and laborious task. Reversed-phase liquid chromatography (RPLC) is among the most important tests in the (bio)pharmaceutical industry for release and stability indicating measurements including adjuvant content, identity, and purity profile. However, the time constraints of developing "on-demand" robust quantitative methods prior to each change in formulation can easily lead to sample analysis becoming a bottleneck in vaccine development. Herein, a simple and efficient generic analytical framework capable of chromatographically resolving the most commonly used non-aluminum-based adjuvants across academic and industrial sectors is introduced. This was designed to seek a more proactive approach for fast-paced assay development endeavors that evolved from extensive stationary phase screening in conjunction with multifactorial in silico simulations of adjuvant retention time (RT) as a function of gradient time, temperature, organic modifier blending, and buffer concentration. The multifactorial retention models yield 3D resolution maps with excellent baseline separation of all adjuvants in a single run, which was found to be very accurate, with differences between experimental and simulated retention times of less than 1%. The analytical framework described here also includes the introduction of a more versatile approach to method development by introducing a dynamic RT database for adjuvants covering the entire library of adjuvants with broad mechanisms of action across numerous vaccine formulations with excellent linearity, accuracy, precision, and specificity. The power of this framework was also demonstrated with numerous analytical assays that can be generated rapidly from simulations guiding vaccine processes in the development of new adjuvant formulations. Analytical assay in this work covers content, purity profile by LC with diode array detector (DAD) and charged aerosol detector (CAD), and component identification by LC with mass spectrometry (MS) across complex vaccine formulations, including the use of surfactants (e.g., polysorbates) as well as their separation from adjuvant targets.

Automated multicolumn screening workflow in ultra-high pressure hydrophilic interaction chromatography for streamlined method development of polar analytes

The pharmaceutical industry is rapidly advancing toward new drug modalities, necessitating the development of advanced analytical strategies for effective, meaningful, and reliable assays. Hydrophilic Interaction Chromatography (HILIC) is a powerful technique for the analysis of polar analytes. Despite being a well-established technique, HILIC method development can be laborious owing to the multiple factors that affect the separation mechanism, such as the selection of stationary phase chemistry, mobile phase eluents, and optimization of column equilibration time. Herein, we introduce a new automated multicolumn and multi-eluent screening workflow that streamlines the development of new HILIC assays, circumventing the existing tedious 'hit-or-miss' approach. A total of 12 complementary columns packed with sub-2 µm fully porous and 2.7 µm superficially porous particles operated on readily available ultra-high pressure liquid chromatography (UHPLC) instrumentation across a diverse set of commercially available polar stationary phases were investigated. Different mobile phases with pH ranging from pH 3 to 9 were evaluated using different organic modifiers. The gradient and column re-equilibration were judiciously set to ensure a reliable assay screening framework that indicates promising conditions for subsequent method optimization to achieve resolution of challenging mixtures. This UHPLC screening system is coupled with a diode array and charged aerosol detectors (DAD, CAD and mass spectrometry) to ensure versatile detection for a variety of compounds. This fast-screening platform lays the foundation for a convenient generic workflow, accelerating the pace of HILIC method development and transfer across both academic and industrial sectors.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

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

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

Thermodynamic solubility measurement without chemical analysis

In this work, the optical imaging based single particle analysis (SPA) and the gold standard shake-flask (SF) solubility methods are compared. We show that to analyze pharmaceutical compounds spanning 7 log units in solubility and a diverse chemical space with limited resources, several analytical techniques are required (HPLC-UV, LC-MS, refractometry and UV-Vis spectrometry), whereas solely the SPA method is able to analyze all the same compounds. SPA experiments take only minutes, while for SF, it may take days to reach thermodynamic equilibration. This decreases the time span needed for the solubility experiment from initial preparations to obtaining the result from roughly three days to less than three hours. The optimal particle size for SPA ranges from approximately one to hundreds of microns. Challenges include measuring large particles, very fast dissolving compounds and handling small sample sizes. Inherent exclusion of density from the SPA measurement is a potential source of error for compounds with very low or high density values. The average relative difference of 37 % between the two methods is very good in the realm of solubility, where 400 % interlaboratory reproducibility can be expected.

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.

Computer-assisted multifactorial method development for the streamlined separation and analysis of multicomponent mixtures in (Bio)pharmaceutical settings

The (bio)pharmaceutical industry is rapidly moving towards complex drug modalities that require a commensurate level of analytical enabling technologies that can be deployed at a fast pace. Unsystematic method development and unnecessary manual intervention remain a major barrier towards a more efficient deployment of meaningful analytical assay across emerging modalities. Digitalization and automation are key to streamline method development and enable rapid assay deployment. This review discusses the use of computer-assisted multifactorial chromatographic method development strategies for fast-paced downstream characterization and purification of biopharmaceuticals. Various chromatographic techniques such as reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC), and supercritical fluid chromatography (SFC) are addressed and critically reviewed. The most significant parameters for retention mechanism modelling, as well as mapping the separation landscape for optimal chromatographic selectivity and resolution are also discussed. Furthermore, several computer-assisted approaches for optimization and development of chromatographic methods of therapeutics, including linear, nonlinear, and multifactorial modelling are outlined. Finally, the potential of the chromatographic modelling and computer-assisted optimization strategies are also illustrated, highlighting substantial productivity improvements, and cost savings while accelerating method development, deployment and transfer processes for therapeutic analysis in industrial settings.

An improved workflow for the development of MS-compatible liquid chromatography assay purity and purification methods by using automated LC Screening instrumentation and in silico modeling

The development of liquid chromatography UV and mass spectrometry (LC-UV-MS) assays in pharmaceutical analysis is pivotal to improve quality control by providing critical information about drug purity, stability, and presence and identity of byproducts and impurities. Analytical method development of these assays is time-consuming, which often causes it to become a bottle neck in drug development and poses a challenge for process chemists to quickly improve the chemistry. In this study, a systematic and efficient workflow was designed to develop purity assay and purification methods for a wide range of compounds including peptides, proteins, and small molecules with MS-compatible mobile phases (MP) by using automated LC screening instrumentation and in silico modeling tools. Initial LC MPs and chromatography column screening experiments enabled quick identification of conditions which provided the best resolution in the vicinity of the target compounds, which is further optimized using computer-assisted modeling (LC Simulator from ACD/Labs). The experimental retention times were in good agreement with the predicted retention times from LC Simulator (ΔtR < 7%). This workflow presents a practical workflow to significantly expedite the time needed to develop optimized LC-UV-MS methods, allowing for a facile, automatic method optimization and reducing the amount of manual work involved in developing new methods during drug development.

Accelerating Pharmaceutical Process Development with an Acoustic Droplet Ejection-Multiple Reaction Monitoring-Mass Spectrometry Workflow

Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement.

Unified chromatography in drug development: Exploiting chaotropic/kosmotropic salts for an accelerated method development

Recent trends in supercritical fluid chromatography (SFC) introduced an innovative gradient profile called Unified Chromatography (UC), which pushes the amount of liquid modifier up to 80-100 % of the total mobile phase composition. These new conditions allow the full transition from a supercritical to a liquid state, unifying the benefits of both SFC and liquid chromatography. However, to facilitate the use of UC for industrial drug development, a stronger effort is needed to streamline and simplify its method development and optimization. In this work, a quick and novel method development procedure for UC is introduced, enabled by the first-time use of novel additives in SFC/UC that exploit chaotropic/kosmotropic properties. A comprehensive view on some fundamental properties, such as the amount of liquid modifier blended with supercritical CO2 (scCO2) and the percentage of water added in the mobile phase is given, to clarify the benefits of using either a chaotropic salt (NaClO4), kosmotropic (HCOONa) or salt with mixed properties (NaOMs - sodium methanesulfonate). With this expanded knowledge, challenging separations of nucleosides, nucleotide, indoles, triazoles and related derivates have been accomplished with UC. Finally, we provide an example of UC delivering a faster and better method for an AbbVie pipeline compound under accelerated stability study. The combined use of scCO2-based chromatography and the novel additive NaClO4 ensures the retention and elution of all degradation species generated at different conditions, where RP-HPLC failed to provide satisfactory performance.

Platform headspace gas chromatography method for high-throughput determination of residual solvents in pharmaceutical materials

Static headspace capillary gas chromatography (HSGC) has been employed to monitor the level of residual solvents in the pharmaceutical materials. Most of the HSGC methods, however, consume significant amounts of diluents and require considerable amount of sample preparation time. Accordingly, a HSGC method featured with fast turnaround time, and minimal amount of solvent use has been developed for the quantitative analysis of 27 residual solvents frequently used in the development and manufacturing processes of pharmaceutical industry. This HSGC-FID method employs a commercially available fused silica capillary column, a split injection (40:1), and a programmed temperature ramp. It was qualified for specificity, accuracy, repeatability/precision, linearity, LOQ, solution stability, and robustness using two representative sample matrices. The standards, samples and spiked samples were demonstrated to be stable for at least 10 days at room temperature in sealed headspace vials with a recovery of ≥ 93%. The method was also shown to be robust, and its performance was not affected by small changes of carrier gas flow rate, initial oven temperature or the headspace oven temperature. In this new approach, the analytical sample was prepared by dissolving the sample into 1 mL of the diluent and the standard solution was prepared by diluting 1 mL of the custom-made stock into 9 mL of the diluent whereas the traditional approach requires liters of the diluent, making the new approach environmentally friendly, sustainable, economical, agile, error-proofing and thus appropriate for a variety of pharmaceutical applications.

High-throughput infrared spectroscopy for quantification of peptides in drug discovery

Peptides have gained an increasing importance in drug discovery as potential therapeutics. Discovery efforts toward finding new, efficacious peptide-based therapeutics have increased the throughput of peptide development, allowing the rapid generation of unique and pure peptide samples. However, high-throughput analysis of peptides may be still challenging and can encumber a high-throughput drug discovery campaign. We report herein a fit-for-purpose method to quantify peptide concentrations using high-throughput infrared spectroscopy (HT-IR). Through the development of this method, multiple critical method parameters were optimized including solvent composition, droplet deposition size, plate drying procedures, sample concentration, and internal standard. The relative absorbance of the amide region (1600-1750 cm^-1) to the internal standard, K₃Fe(CN)₆ (2140 cm^-1), was determined to be most effective at providing lowest interference for measuring peptide concentration. The best sample deposition was achieved by dissolving samples in a 50:50 v/v allyl alcohol/water mixture. The developed method was used on 96-well plates and analyzed at a rate of 22 min per plate. Calibration curves to measure sample concentration versus response relationship displayed sufficient linearity (R² > 0.95). The repeatability and scope of detection was demonstrated with eighteen peptide samples that were measured with most values below 20% relative standard deviation. The linear dynamic range of the method was determined to be between 1 and 5 mg/mL. This developed HT-IR methodology could be a useful tool in peptide drug candidate lead identification and optimization processes.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Automated Hydrophobic Interaction Chromatography Screening Combined with In Silico Optimization as a Framework for Nondenaturing Analysis and Purification of Biopharmaceuticals

The mounting complexity of new modalities in the biopharmaceutical industry entails a commensurate level of analytical innovations to enable the rapid discovery and development of novel therapeutics and vaccines. Hydrophobic interaction chromatography (HIC) has become one of the widely preferred separation techniques for the analysis and purification of biopharmaceuticals under nondenaturing conditions. Inarguably, HIC method development remains very challenging and labor-intensive owing to the numerous factors that are typically optimized by a "hit-or-miss" strategy (e.g., the nature of the salt, stationary phase chemistry, temperature, mobile phase additive, and ionic strength). Herein, we introduce a new HIC method development framework composed of a fully automated multicolumn and multieluent platform coupled with in silico multifactorial simulation and integrated fraction collection for streamlined method screening, optimization, and analytical-scale purification of biopharmaceutical targets. The power and versatility of this workflow are showcased by a wide range of applications including trivial proteins, monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), oxidation variants, and denatured proteins. We also illustrate convenient and rapid HIC method development outcomes from the effective combination of this screening setup with computer-assisted simulations. HIC retention models were built using readily available LC simulator software outlining less than a 5% difference between experimental and simulated retention times with a correlation coefficient of >0.99 for pharmaceutically relevant multicomponent mixtures. In addition, we demonstrate how this approach paves the path for a straightforward identification of first-dimension HIC conditions that are combined with mass spectrometry (MS)-friendly reversed-phase liquid chromatography (RPLC) detection in the second dimension (heart-cutting two-dimensional (2D)-HIC-RPLC-diode array detector (DAD)-MS), enabling the analysis and purification of biopharmaceutical targets.

Expediting the chromatographic analysis of COVID-19 antibody therapeutics with ultra-short columns, retention modeling and automated method development

The COVID-19 pandemic necessitated the emergency use authorization (EUA) of several new therapeutics and vaccines. Several monoclonal antibodies (mAbs) were among those authorized for use, and they have served a purpose to provide passive immunity and to help minimize dangerous secondary effects in at-risk and hospitalized patients infected with SARS-CoV-2. With an EUA submission, scientific data on a drug candidate is often collected near simultaneously alongside drug development. In such a situation, there is little time to allow misguided method development nor time to wait on traditional turnaround times. We have taken this dilemma as a chance to propose new means to expediting the chromatographic characterization of protein therapeutics. To this end, we have combined the use of automated, systematic modeling and ultrashort LC columns to quickly optimize high throughput RP, IEX, HILIC and SEC separations for two COVID-19-related mAbs. The development and verification of these four complementary analytical methods required only 2 days of experimental work. In the end, one chromatographic analysis can be performed with a sub-2 min run time such that it is feasible to comprehensively characterize a COVID-19 mAb cocktail by 4 different profiling techniques within a 1-hour turnaround time.

Nonlinear Predictive Modelling Enables In Silico Optimization of Chromatographic Methods for Complex Stationary Phase‑Analyte Interactions

The development of robust analytical assays for separation and analysis of complex multicomponent mixtures can often be challenging, reflecting the increased complexity of new medicine and vaccine processes. In silico liquid chromatography (LC) method development strategies for small molecules have reached a mature stage across the pharmaceutical industry. However, a straightforward approach for large molecules remains elusive because of conformational changes that can often influence chromatographic retention. Nonetheless, an excellent correlation between experimental and predicted retention time is possible by deploying the correct regression retention models in terms of ln k vs. %B and ln k vs. 1/T (ΔtR < 0.1%). Excellent outcomes generated through in silico chromatographic method development of large molecules using different chaotropic and denaturing mobile phases are illustrated. Linear and nonlinear (polynomial regression) retention models using readily available software were deployed as a function of several chromatographic parameters (gradient slope and column temperature) for a variety of proteins (12–670 kDa) and peptides.

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.

Automated ion exchange chromatography screening combined with in silico multifactorial simulation for efficient method development and purification of biopharmaceutical targets

Bioprocess development of increasingly challenging therapeutics and vaccines requires a commensurate level of analytical innovation to deliver critical assays across functional areas. Chromatography hyphenated to numerous choices of detection has undeniably been the preferred analytical tool in the pharmaceutical industry for decades to analyze and isolate targets (e.g., APIs, intermediates, and byproducts) from multicomponent mixtures. Among many techniques, ion exchange chromatography (IEX) is widely used for the analysis and purification of biopharmaceuticals due to its unique selectivity that delivers distinctive chromatographic profiles compared to other separation modes (e.g., RPLC, HILIC, and SFC) without denaturing protein targets upon isolation process. However, IEX method development is still considered one of the most challenging and laborious approaches due to the many variables involved such as elution mechanism (via salt, pH, or salt-mediated-pH gradients), stationary phase's properties (positively or negatively charged; strong or weak ion exchanger), buffer type and ionic strength as well as pH choices. Herein, we introduce a new framework consisting of a multicolumn IEX screening in conjunction with computer-assisted simulation for efficient method development and purification of biopharmaceuticals. The screening component integrates a total of 12 different columns and 24 mobile phases that are sequentially operated in a straightforward automated fashion for both cation and anion exchange modes (CEX and AEX, respectively). Optimal and robust operating conditions are achieved via computer-assisted simulation using readily available software (ACD Laboratories/LC Simulator), showcasing differences between experimental and simulated retention times of less than 0.5%. In addition, automated fraction collection is also incorporated into this framework, illustrating the practicality and ease of use in the context of separation, analysis, and purification of nucleotides, peptides, and proteins. Finally, we provide examples of the use of this IEX screening as a framework to identify efficient first dimension (¹D) conditions that are combined with MS-friendly RPLC conditions in the second dimension (²D) for two-dimensional liquid chromatography experiments enabling purity analysis and identification of pharmaceutical targets.

In Silico Method Development of Achiral and Chiral Tandem Column Reversed-phase Liquid Chromatography for Multicomponent Pharmaceutical Mixtures

Tandem column liquid chromatography (LC) is a convenient, cost-effective approach to resolve multicomponent mixtures by serially coupling columns on readily available one-dimensional separation systems without specialized user training. Yet, adoption of this technique remains limited, mainly due to the difficulty in identifying optimal selectivity out of many possible tandem column combinations. At this point, method development and optimization require laborious "hit-or-miss" experimentation and "blind" screening when investigating different column selectivity without standard analytes. As a result, many chromatography practitioners end up combining two columns of similar selectivity, limiting the scope and potential of tandem column LC as a mainstay for industrial applications. To circumvent this challenge, we herein introduce a straightforward in silico multifactorial approach as a framework to expediently map the separation landscape across multiple tandem columns (achiral and chiral) and eluent combinations (isocratic and gradient elution) under reversed-phase LC conditions. Retention models were built using commercially available LC simulator software showcasing less than 2% difference between experimental and simulated retention times for analytes of interest in multicomponent pharmaceutical mixtures (e.g., metabolites and cyclic peptides).

Enantioselective UHPLC Screening Combined with In Silico Modeling for Streamlined Development of Ultrafast Enantiopurity Assays

Enantioselective chromatography has been the preferred technique for the determination of enantiomeric excess across academia and industry. Although sequential multicolumn enantioselective supercritical fluid chromatography screenings are widespread, access to automated ultra-high-performance liquid chromatography (UHPLC) platforms using state-of-the-art small particle size chiral stationary phases (CSPs) is an underdeveloped area. Herein, we introduce a multicolumn UHPLC screening workflow capable of combining 14 columns (packed with sub-2 μm fully porous and sub-3 μm superficially porous particles) with nine mobile phase eluent choices. This automated setup operates under a vast selection of reversed-phase liquid chromatography, hydrophilic interaction liquid chromatography, polar-organic mode, and polar-ionic mode conditions with minimal manual intervention and high success rate. Examples of highly efficient enantioseparations are illustrated from the integration of chiral screening conditions and computer-assisted modeling. Furthermore, we describe the nuances of in silico method development for chiral separations via second-degree polynomial regression fit using LC simulator (ACD/Labs) software. The retention models were found to be very accurate for chiral resolution of single and multicomponent mixtures of enantiomeric species across different types of CSPs, with differences between experimental and simulated retention times of less than 0.5%. Finally, we illustrate how this approach lays the foundation for a streamlined development of ultrafast enantioseparations applied to high-throughput enantiopurity analysis and its use in the second dimension of two-dimensional liquid chromatography experiments.

Selective Plate-Based Assay for Trace EDTA Analysis via Boron Trifluoride-methanol Derivatization UHPLC-QqQ-MS/MS Enabling Biologic and Vaccine Processes

The employment of ethylenediaminetetraacetic acid (EDTA) across several fields in chemistry and biology has required the creation of a high number of quantitative assays. Nonetheless, the determination of trace EDTA, especially in biologics and vaccines, remains challenging. Herein, we introduce an automated high-throughput approach based on EDTA esterification in 96-well plates using boron trifluoride-methanol combined with rapid analysis by ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS). Derivatization of EDTA to its methyl ester (Me-EDTA) serves to significantly improve chromatographic performance (retention, peak shape, and selectivity), while also delivering a tremendous enhancement of sensitivity in the positive ion mode electrospray ionization (ESI+). This procedure, in contrast to previous EDTA methods based on complexation with metal ions, is not affected by high concentration of other metals, buffers, and related salts abundantly present in biopharmaceutical processes (e.g., iron, copper, citrate, etc.). Validation of this assay for the determination of ng·mL^-1 level EDTA in monoclonal antibody and vaccine products demonstrated excellent performance (repeatability, precision, and linear range) with high recovery from small sample volumes while also providing an advantageous automation-friendly workflow for high-throughput analysis.

Unified chromatography - Mass spectrometry as a versatile tool for determination of food dyes

Unified chromatography with mass spectrometric detection was assessed for determination of food dyes. Nineteen substances representing azo, triphenylmethane, xanthone, indigoid, quinoline and polyene classes covering an unprecedented range from nonpolar β-Carotene (logD 13.6) to ionic Tartrazine (logD -7.5) were analyzed simultaneously. The dyes were separated in a single experimental run by an 18-min gradient elution from 98% CO₂ to 100% aqueous-methanolic modifier on a diol column. Isomeric substances were resolved, and Isatin Sulfonic acid was detected as a degradation product of Indigo Carmine. Mobile phase properties reproducibly changed from supercritical to liquid state ensuring stable retention times (inter-day RSD<0.5%). Quantitative analysis of sports drinks after straightforward 10- or 25-fold dilution with dimethyl sulfoxide confirmed the method applicability to real-life samples. Sufficient limits of detection (typically 0.025 mg L^-1 in processed samples, equivalent to 0.25 mg L^-1 in drink) and a wide linear range (typically 0.5-50 mg L^-1 or 1.3-125 mg L^-1 in drink for 10× or 25× dilution, respectively) were demonstrated during validation. A comparison of method performance with competitive liquid chromatography procedures is also provided. Unified chromatography is a promising tool for comprehensive multiclass analysis of dyes in the context of food safety.

Advances in ultra-high-pressure and multi-dimensional liquid chromatography instrumentation and workflows

The present contribution discusses recent advances in ultra-high-pressure liquid chromatography (UHPLC) and multi-dimensional liquid chromatography (MDLC) technology. First, new developments in UHPLC column technology and system design are highlighted. The latter includes a description of a novel injector concept enabling method speed-up, emerging detectors, and instrument diagnostics approaches. Next, online MDLC workflows are reviewed and advances in modulation technology are highlighted. Finally, key applications published in 2020 are reviewed.

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.

Macrocyclic glycopeptide-based chiral selectors for enantioseparation in sub/supercritical fluid chromatography

Increasing number of reported works dealing with macrocyclic glycopeptide-based columns in sub/supercritical fluid chromatography (SFC) points to the growing interest in this area. With the development and production of sub 2 µm fully porous particles and superficially porous particles with bonded macrocyclic glycopeptides, significant improvements have been made in ultrafast high efficiency chiral SFC. This review article gives an overview of macrocyclic glycopeptide-based chiral selectors that were used in theoretical studies and/or applications in SFC. The review covers the period from 1997 when macrocyclic glycopeptides were first used in SFC till the end of July 2020 according to Web of Science. This work can also be helpful to analysts searching for an appropriate method for the separation/determination of enantiomers of their interest.

The 2021 Winners of the Lifetime Achievement and Emerging Leader in Chromatography Awards

Paul Haddad and Erik L. Regalado are the winners of the 14th annual LCGC Lifetime Achievement and Emerging Leader in Chromatography Awards, respectively. We review their achievements.

Characterization of stationary phases in supercritical fluid chromatography including exploration of shape selectivity

Achiral packed column supercritical fluid chromatography (SFC) has shown an important regain of interest in academic and industrial laboratories in the recent years. In relation to this increased concern, major instrument manufacturers have designed some stationary phases specifically for SFC use. SFC stationary phases have been widely examined over the last two decades, based on the use of linear solvation energy relationships (LSER), which relate analyte retention to its properties and to the interaction capabilities of the chromatographic system. The method provides some understanding on retention mechanisms (normal phase, reversed phase or mixed-mode) and the possibility to compare stationary phases on a rational basis, especially through a spider diagram providing a visual classification. The latter can be used as a primary tool to select complementary stationary phases to be screened for any separation at early stages of method development, before optimization steps. In this context, the characterization of the 14 columns from the Shim-pack UC series (Shimadzu Corporation, Kyoto, Japan), which are dedicated to SFC and more broadly to unified chromatography (UC), was performed, using the LSER methodology. As in previous works, seven descriptors, including five Abraham descriptors (E, S, A, B, V) and two descriptors describing positive and negative charges (D^- and D⁺) were first employed to describe interactions with neutral and charged analytes. Secondly, two more descriptors were introduced, which were previously employed solely for the characterization of enantioselective systems and expressing shape features of the analytes (flexibility F and globularity G). They brought additional insight into the retention mechanisms, showing how spatial insertion of the analytes in some stationary phases is contributing to shape separation capabilities and how folding possibilities in flexible molecules is unfavorable to retention in other stationary phases.

Ultra-high-throughput SPE-MALDI workflow: Blueprint for efficient purification and screening of peptide libraries

At the forefront of synthetic endeavors in the pharmaceutical industry, including drug discovery and high-throughput screening, timelines are tight and large quantities of pure chemical targets are rarely available. In this regard, the development of novel and increasingly challenging chemistries requires a commensurate level of innovation to develop reliable analytical assays and purification workflows with rapid turnaround that enables accelerated pharmacological evaluation. A small-scale automation platform enabling high-throughput analysis and purification to streamline the selection of candidate leads would be a transformative advance. Herein, we introduce an automation-friendly solid-phase extraction-matrix-assisted laser desorption/ionization (SPE-MALDI) platform applied to the high-throughput purification and analysis of peptide libraries. This advance enabled us to purify peptides from microgram levels in less than a day with results comparable to traditional high-performance liquid chromatography-diode array detection-mass spectrometry (HPLC-DAD-MS).

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.

Investigating the use of unconventional temperatures in supercritical fluid chromatography

The use of unorthodox temperatures, ranging from -5 °C up to 80 °C, have been thoroughly investigated in supercritical fluid chromatography. To this purpose, an initial evaluation of the kinetic and thermodynamic performance has been made with a set of 4 analytes eluting at different percentages of organic co-solvent in the mobile phase (3%-10% - 45%-80%). The van Deemter plots have demonstrated how, at low organic modifier presence, the use of low temperatures did not necessarily translate into worse performance, while high temperatures could pose more issues due to the poor handling of the super/subcritical mobile phase by the chromatographic system. With important percentages of co-solvent, however, high temperatures were fundamental in ensuring better profiles of the van Deemter plots, compared to low temperatures. Pressure plots have demonstrated that gradients reaching elevated percentages of organic modifiers can also be used on stationary phases packed with sub 2 μm silica particles if high temperatures are employed. The thermodynamic evaluation, made via the analysis of van't Hoff plots, indicates the presence of three retention behaviors happening in UHPSFC when switching from high to low temperatures, depending on the co-solvent percentage needed to elute one analyte. Finally, an assessment of the stationary phase stability at high temperatures was performed: the retention times variabilities recorded were minimal (RSD < 2.5%), as well as the peak widths and inlet column pressures were somewhat constant throughout the analyses. In the second part of this study, a focus on potential applications benefiting from such unconventional temperatures has been made. A series of challenging analytes have experienced better chromatographic resolution at either high or low temperatures, providing therefore a potentially interesting tool to analysts during the chromatographic method development process. In conclusion, the UV sensitivity at different temperatures was also taken into consideration, with no significant impact on the quality of the UV signal under any condition.

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling

Modern pharmaceutical processes can often lead to multicomponent mixtures of closely related species that are difficult to resolve under chromatographic conditions, and even worse in preparative scale settings. Despite recent improvements in column technology and instrumentation, there remains an urgent need for creating innovative approaches that address challenging coelutions of critical pair and poor chromatographic productivity of purification methods. Herein, we overcome these challenges by introducing a simple and practical technique named multifactorial peak crossover (MPC) via computer-assisted chromatographic modeling. The approach outlined here focuses on mapping the separation landscape of pharmaceutical mixtures to quickly identify spaces of peak coelution crossings which enables one to conveniently switch the elution order of target analytes. Diverse examples of MPC diagrams as a function of column temperature, mobile phase gradient or a multifactorial combination in reversed phase and ion exchange chromatography (RPLC and IEC) modes are generated using ACD Laboratories/LC Simulator software and corroborated with experimental data match (overall retention time differences of less than 1%). This powerful MPC technique allows us to gain massive productivity increases (shorter cycle time and higher sample loading) for purification of pharmaceuticals by selectively switching the elution order of target components away from undesired tailing peaks and coelution spaces. MPC chromatography dramatically reduces the time spent developing productive analytical and preparative scale separations. In addition, we illustrate how this new MPC concept can be used to gain substantial improvements of the signal-to-noise ratio, enabling straightforward ppb detection of low-level target components with direct impact in the quantitation of metabolites and potential genotoxic impurities (PGIs). These innovations are of paramount importance in order to facilitate efficient isolation, characterization, and quantitation of drug substances in the development of new medicines.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).