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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., Competing Interests: Declaration of competing interest DWA is president of AZYP, LLC. All other authors declare no competing financial or personal interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" ( p ) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any p if the data are collected at p = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal p for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to inappropriately influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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 (Δt R  < 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., (© 2024. Merck & Co., Inc., Rahway, NJ, USA and its affiliates under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

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.

Isolation and chemical characterization of target components in fast-paced pharmaceutical laboratories can often be challenging, especially when dealing with mixtures of closely related, possibly unstable species. Traditionally, this process involves intense labor and manual intervention including chromatographic method development and optimization, fraction collection, and drying processes prior to NMR analyses for unambiguous structure elucidation. To circumvent these challenges, a foundational framework for the proper utilization of supercritical carbon dioxide (scCO 2 ) and deuterated modifiers (CD 3 OD) in sub/supercritical fluid chromatography (SFC) is herein introduced. This facilitates a streamlined multicomponent isolation with minimized protic residues, further enabling immediate NMR analysis. In addition to bypassing tedious drying processes and minimizing analyte degradation, this approach (complementary to traditional reversed-phase liquid chromatography, RPLC) delivers highly efficient separations and automated fraction collection using readily available analytical/midscale SFC instrumentation. A series of diverse analytes across a wide spectrum of chemical properties (acid, basic, and neutral), combined with different stationary-phase columns in SFC are investigated using both a protic organic modifier (CH 3 OH) and its deuterated counterpart (CD 3 OD). The power of this framework is demonstrated with pharmaceutically relevant applications in the context of target characterization and analysis of complex multicomponent reaction mixtures from modern synthetic chemistry, demonstrating high isolation yields while reducing both the environmental footprint and manual intervention. This workflow enables unambiguous fast-paced structure elucidation on the analytical scale, providing results that are comparable to traditional, but time-consuming, RPLC purification approaches.

Chiral sub/supercritical fluid chromatography (SFC) has established itself as one of the preferred techniques for enantioseparations at both analytical and preparative scale. Herein, we introduce a parallel multicolumn SFC screening for automated chiral method development in fast-paced settings. The practicality and speed advantages of this approach are illustrated with parallel screening of a diverse set of chiral molecules across ten columns with five different organic modifiers/CO 2 based eluents enabling rapid identification of suitable enantioseparation conditions for accelerated purification of pharmaceutical targets. Rapid delivery turnarounds of pure enantiomers of less than 1 h from screening to target isolation are demonstrated illustrating the power of this approach., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

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 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.

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 ( 2 D) 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 2 D 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 ( 1 D) separation using a single combination of 2 D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the 1 D and 2 D 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 recent years, the use of quantitative liquid chromatography (LC) coupled charged aerosol detection (CAD) for poor UV absorbing analytes in multicomponent mixtures has grown exponentially across academic and industrial sectors. The ballpark of previous LC-CAD reports is focused on practical applications, as well as optimization of critical parameters such as: response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition. However, straightforward approaches to deal with the characteristic nonlinear response of CAD still scarce. A highly overlooked parameter is the power function value (PFV), whose optimization enables a detection signal that is more linear with higher signal-to-noise ratio (S/N) and lower relative standard deviation (RSD) of area counts. Herein, a systematic investigation of different regression models (log-log, first-and second-degree polynomial) by both interpolation and extrapolation process in conjunction with PFV optimization throughout the development of LC-CAD assays is reported. The accuracy of the results via interpolation is always good (< 5%) when operating in the vicinity of the optimum PFV regardless the regression model choice. On the contrary, extrapolation process only worked when applying log-log regression at the optimum PFV (accuracy <5%). This outcome indicates that a first-order regression via interpolation can be a safe and simple choice for quantitative LC-CAD in highly regulated laboratories (GLP, GMP, etc.). Whereas a straightforward extrapolation combined with log-log regression can enable the deployment of high-throughput LC-CAD assays, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization (medicinal chemistry, discovery, biocatalysis, process chemistry, etc.). This approach is crucial in developing quantitative LC-CAD assays for poor UV absorbing pharmaceuticals that are sensitive, precise, accurate and robust across early and late-stage pharmaceutical development., Competing Interests: Declaration of Competing Interest The authors have no conflict of interests to declare. No funding to declare., (Copyright © 2021. Published by Elsevier B.V.)

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., (Copyright © 2021. Published by Elsevier B.V.)

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.

Baseline separation and analysis of multicomponent mixtures of closely related pharmaceuticals using single column selectivity can often be challenging, requiring the combination of orthogonal stationary and mobile phase methods to monitor all the species and optimize reaction outcomes. In recent years, two-dimensional liquid chromatography (2D-LC) has become a valuable tool for improving peak capacity and selectivity. Though powerful, standard 2D-LC instrumentation and software can often lead to tedious method development and has a requirement for very specific expertise that is poorly suited for a fast-paced industrial environment. In this regard, the introduction of an automated online 2D-LC setup that could screen multiple columns in both dimensions without manual intervention will undeniably serve to streamline column/mobile phase selection and secure the viability of 2D-LC as a mainstay instrument for industrial applications. Herein, we introduce and investigate a multicolumn online 2D-LC approach that simplifies column screening and method development dramatically. This setup incorporates 6-position column selection valve technology whose functionality enables us to combine multiple columns in the first and second dimensions. This strategy in conjunction with diode array detection (DAD) in both dimensions and mass spectrometry (MS) acquisition in the second dimension serves to explore different columns and mobile phases as a framework for screening targeted compounds in multicomponent mixtures without having to perform chromatographic purification. Multiple online heart cutting achiral RPLC - achiral RPLC and achiral RPLC - chiral RPLC coupled to DAD and ESI-MS methods combining several stationary phase selectivity in an automated fashion are successfully applied to the separation and analysis of complex mixtures of drug substances, where in many instances, traditional 1D-ultra-high performance liquid chromatography (UHPLC) fails or delivers sub-optimal results. This automated online multicolumn 2D-LC workflow enables rapid and efficient identification of column/eluent combinations, as well as sample analysis across multiple columns in both dimensions overnight with a single click., Competing Interests: Declaration of competing interest None., (Copyright © 2020. Published by Elsevier B.V.)

The analysis of complex mixtures of closely related species is quickly becoming a bottleneck in the development of new drug substances, reflecting the ever-increasing complexity of both fundamental biology and the therapeutics used to treat disease. Two-dimensional liquid chromatography (2D-LC) is emerging as a powerful tool to achieve substantial improvements in peak capacity and selectivity. However, 2D-LC suffers from several limitations, including the lack of automated multicolumn setups capable of combining multiple columns in both dimensions. Herein, we report an investigation into the development and implementation of a customized online comprehensive multicolumn 2D-LC-DAD-MS setup for screening and method development purposes, as well as analysis of multicomponent biopharmaceutical mixtures. In this study, excellent chromatographic performance in terms of selectivity, peak shape, and reproducibility were achieved by combining reversed-phase (RP), strong cation exchange (SCX), strong anion exchange (SAX), and size exclusion chromatography (SEC) using sub-2-μm columns in the first dimension in conjunction with several 3.0 mm × 50 mm RP columns packed with sub-3-μm fully porous particles in the second dimension. Multiple combinations of separation modes coupled to UV and MS detection are applied to the LC × LC analysis of a protein standard mixture, intended to be representative of protein drug substances. The results reported in this study demonstrate that our automated online multicolumn 2D-LC-DAD-MS workflow can be a powerful tool for comprehensive chromatographic column screening that enables the semi-automated development of 2D-LC methods, offering the ability to streamline full visualization of sample composition for an unknown complex mixture while maximizing chromatographic orthogonality. Graphical Abstract.

Chromatographic separation, analysis and characterization of complex highly polar analyte mixtures can often be very challenging using conventional separation approaches. Analysis and purification of hydrophilic compounds have been dominated by liquid chromatography (LC) and ion-exchange chromatography (IC), with sub/supercritical fluid chromatography (SFC) moving toward these new applications beyond traditional chiral separations. However, the low polarity of supercritical carbon dioxide (CO 2 ) has limited the use of SFC for separation and purification in the bioanalytical space, especially at the preparative scale. Reaction mixtures of highly polar species are strongly retained even using polar additives in alcohol modifier/CO 2 based eluents. Herein, we overcome these problems by introducing chaotropic effects in SFC separations using a nontraditional mobile phase mixture consisting of ammonium hydroxide combined with high water concentration in the alcohol modifier and carbon dioxide. The separation mechanism was here elucidated based on extensive IC-CD (IC couple to conductivity detection) analysis of cyclic peptides subjected to the SFC conditions, indicating the in situ formation of a bicarbonate counterion (HCO 3 - ). In contrast to other salts, HCO 3 - was found to play a crucial role acting as a chaotropic agent that disrupts undesired H-bonding interactions, which was demonstrated by size-exclusion chromatography coupled with differential hydrogen-deuterium exchange-mass spectrometry experiments (SEC-HDX-MS). In addition, the use of NH 4 OH in water-rich MeOH modifiers was compared to other commonly used basic additives (diethylamine, triethylamine, and isobutylamine) showing unmatched chromatographic and MS detection performance in terms of peak shape, retention, selectivity, and ionization as well as a completely different selectivity and retention behavior. Moreover, relative to ammonium formate and ammonium acetate in water-rich methanol modifier, the ammonium hydroxide in water additive showed better chromatographic performance with enhanced sensitivity. Further optimization of NH 4 OH and H 2 O levels in conjunction with MeOH/CO 2 served to furnish a generic modifier (0.2% NH 4 OH, 5% H 2 O in MeOH) that enables the widespread transition of SFC to domains that were previously considered out of its scope. This approach is extensively applied to the separation, analysis, and purification of multicomponent reaction mixtures of closely related polar pharmaceuticals using readily available SFC instrumentation. The examples described here cover a broad spectrum of bioanalytical and pharmaceutical applications including analytical and preparative chromatography of organohalogenated species, nucleobases, nucleosides, nucleotides, sulfonamides, and cyclic peptides among other highly polar species.

In recent years, charged aerosol detection (CAD) has become a valuable tool for fast and efficient quantitative chromatographic analysis of drug substances with weak UV absorption. In analytical method development using CAD, the power function settings available in the instrument software are key for linearization of the signal response with respect to analyte concentration. However, the relatively poor understanding of the power function algorithm has limited a more widespread use of CAD for quantitative assays, especially in the late stage of method validation and GMP laboratories. Herein, we present an approach to understand the inner workings of the power function value (PFV), the PFV optimization algorithm, as well as a method to determine the optimum PFV based on the signals acquired at PFV = 1 (default CAD settings). The exponent and the constant in the PFV equation used for modeling follow a trend as a function of PFV. The CAD signal at any PFV was modeled based on the signal acquired at PFV = 1, the modelling was successful for two analytes at different concentration levels on two different CAD detectors of the same model. This method reveals the functionality of the PFV which substantially simplifies the workflow needed to optimize the detector signal. The accuracy between the experimental and theoretical results showed high correlation and always resulted in the same optimum PFV determined by both ways. The approach described in this investigation simplifies the selection of the optimum PFV at which the signal is more linear, the signal-to-noise is higher, and the area reproducibility is better. The power function algorithm elucidated herein enables determination of optimum PFV from minimal experimental output and excellent overall accuracy. This paper provides an approach that includes no data transformation outside the vendor software, a very important requirement to easily validate and report results in a GMP environment., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Manufacturing process development of new drug substances in the pharmaceutical industry combines numerous chemical challenges beyond the efficient synthesis of complex molecules. Optimization of a synthetic route involves the screening of multiple reaction variables with a desired outcome that not only depends on an increased product yield but is also highly influenced by the removal efficacy of residual chemicals and reaction byproducts during the subsequent synthetic route. Consequently, organic chemists must survey a wide array of synthetic variables to develop a highly productive, green, and cost-effective manufacturing process. The time constraints of developing robust quantitative methods prior to each processing step can easily lead to sample analysis becoming a bottleneck in synthetic route development. In this regard, conventional "on demand" analytical method development and optimization approaches, traditionally used for guiding synthetic chemistry efforts, become unsustainable. This Account introduces recent efforts to address the aforementioned challenges through the development and implementation of generic or more universal chromatographic methods that can cover a broad spectrum of targeted compound classes. Such generic methods require significant resolving power to enable baseline resolution of multicomponent mixtures in a single experimental run without additional method customization but must be simple enough to allow for routine use by chemists, chemical engineers and other researchers with little experience in chromatographic method development. These powerful analytical methodologies are often employed to minimize the time spent developing new analytical assays, while also facilitating method transfer to manufacturing facilities and application in regulatory settings. Diverse examples of universal and fit-for-purpose analytical procedures are presented herein, illustrating the power of modern readily available analytical technology for streamlining the development of new drug substances in organic chemistry laboratories across both academic and industrial sectors. With recent advances in analytical instrumentation and column technologies, universal chromatographic methods are quickly becoming a proactive and effective strategy to accelerate the discovery and implementation of new synthetic methodologies, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization. Targets of these generic methods include analysis of organic solvents, acid and basic additives, nucleotide species, palladium scavengers, impurity mapping, enantiopurity, synthetic intermediates, active pharmaceutical ingredients and their counterions, dehalogenation byproducts, and mixtures of organohalogenated pharmaceuticals, among other chemicals used or formed in process chemistry reactions.

The evolution of supercritical fluid chromatography (SFC) instrumentation, improved detection capability, and expanded modifier range has led to extending the reach of SFC to the analysis of a broader spectrum of analytes beyond enantioselective separations. However, preparative SFC has yet to see the same technological revitalization, especially in regards to the purification of highly polar analytes. Enhanced fluidity liquid chromatography (EFLC) has been demonstrated as one of the ways to extend the applicable range of SFC instrumentation to highly polar analytes such as proteins, carbohydrates, and nucleotides. Despite recent applications of EFLC for challenging mixtures of hydrophilic metabolites and analogs, its viability in preparative purification, which is of great importance to the pharmaceutical industry, remains unknown. Herein, multiple chromatographic parameters that are critical to achieve feasible EFLC purification methods were investigated, including system pressure as a function of modifier composition (for several MeOH:H 2 O ratios), effect of diluent injection conditions on peak shape, and optimization of mass load with diluent composition. The usage of 50% acetonitrile or methanol diluents provided the most volumetric loading capacity. In the case of sucrose, leveraging higher analyte solubility in water proved to be more favorable than the volumetric loading capacity of diluents with higher organic content. In fact, an 80 mg injection of sucrose was possible on a 2 cm preparative HILIC column with minimal peak shape degradation. The combined information led to the successful demonstration of EFLC for the preparative separation of sugars using readily available MS-directed SFC instrumentation., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Modern process research and development can often be hampered by the tedious method development required to chromatographically resolve mixtures of chemical species with very similar physical properties. Herein, we describe a simple approach for the development and implementation of an efficient ultra-high performance liquid chromatography (UHPLC) assay that is extensively applied to the separation and analysis of multicomponent reaction mixtures of closely related pharmaceutical intermediates and impurities. Methods are optimized using multi-column and multi-solvent UHPLC screening in conjunction with chromatography simulation software (ACD Labs/LC Simulator). This approach is implemented to enable the separation, identification, mapping and control of impurities formed within the process chemistry optimization of the dimeric catalyst used in the synthesis of new drug substances. The final method utilized a sub-2 μm C18 stationary phase (2.1 mm I.D. × 50 mm length, 1.7 μm particle size ACQUITY UPLC BEH C18) with a non-conventional chaotropic mobile phase buffer (35 mM potassium hexafluorophosphate in 0.1% phosphoric acid/acetonitrile) in order to achieve baseline separation of all reaction components. The chromatographic simulation and modeling strategy served to generate 3D resolution maps with robust separation conditions that match the outcome of subsequent experimental data (overall ΔtR < 0.35%). Our multi-column UHPLC screening with computer-assisted chromatographic modeling is a great addition to the toolbox of synthetic chemists and can be a powerful tool for streamlining process chemistry optimization in organic chemistry laboratories across both academic and industrial sectors.

Nucleotides are among the most frequently used chemical building blocks in the research, development and manufacture of drug substances. They are composed of three highly polar subunit molecules (a nucleobase, a sugar, and at least one phosphate group), which makes their separation and analysis very challenging by conventional liquid chromatography techniques. Herein, we describe a simple, efficient, and cost-effective ion-exchange chromatography (IEC) method for the separation and purification of over 20 nucleotides. This method combines the use of a Tosoh TSKgel SuperQ-5P W resin in conjunction with a fully aqueous eluent profile (ammonium bicarbonate-based) that allows for a straightforward scale-up transition and convenient drying process with minimal environmental impact. This generic method was optimized using chromatography simulation software (ACD Labs/LC Simulator) and successfully applied to the preparative purification of multicomponent nucleotide mixtures using readily available Fast Protein Liquid Chromatography (FPLC) instrumentation. These IEC method conditions can be effectively applied as the starting point for method development and isolation of other highly polar nucleotide species beyond those investigated in this study., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Amikacin, an aminoglycoside antibiotic lacking a UV chromophore, was developed into a drug product for delivery by inhalation. A robust method for amikacin assay analysis and aerosol particle size distribution (aPSD) determination, with comparable performance to the conventional UV detector was developed using a charged aerosol detector (CAD). The CAD approach involved more parameters for optimization than UV detection due to its sensitivity to trace impurities, non-linear response and narrow dynamic range of signal versus concentration. Through careful selection of the power transformation function value and evaporation temperature, a wider linear dynamic range, improved signal-to-noise ratio and high repeatability were obtained. The influences of mobile phase grade and glassware binding of amikacin during sample preparation were addressed. A weighed (1/X 2 ) least square regression was used for the calibration curve. The limit of quantitation (LOQ) and limit of detection (LOD) for this method were determined to be 5μg/mL and 2μg/mL, respectively. The method was validated over a concentration range of 0.05-2mg/mL. The correlation coefficient for the peak area versus concentration was 1.00 and the y-intercept was 0.2%. The recovery accuracies of triplicate preparations at 0.05, 1.0, and 2.0mg/mL were in the range of 100-101%. The relative standard deviation (S rel ) of six replicates at 1.0mg/mL was 1%, and S rel of five injections at the limit of quantitation was 4%. A robust HPLC-CAD method was developed and validated for the determination of the aPSD for amikacin. The CAD method development produced a simplified procedure with minimal variability in results during: routine operation, transfer from one instrument to another, and between different analysts., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The aim of this work is to identify the parameters that affect the recovery of pharmaceutical residues from the surface of stainless steel coupons. A series of factors were assessed, including drug product spike levels, spiking procedure, drug-excipient ratios, analyst-to-analyst variability, intraday variability, and cleaning procedure of the coupons. The lack of a well-defined procedure that consistently cleaned the coupon surface was identified as the major contributor to low and variable recoveries. Assessment of cleaning the surface of the coupons with clean-in-place solutions (CIP) gave high recovery (>90%) and reproducible results (Srel≤4%) regardless of the conditions that were assessed previously. The approach was successfully applied for cleaning verification of small molecules (MW <1,000 Da) as well as large biomolecules (MW up to 50,000 Da).

The parameters affecting the recovery of pharmaceutical residues from the surface of stainless steel coupons for quantitative cleaning verification method development have been studied, including active pharmaceutical ingredient (API) level, spiking procedure, API/excipient ratio, analyst-to-analyst variability, inter-day variability, and cleaning procedure of the coupons. The lack of a well-defined procedure that consistently cleaned coupon surface was identified as the major contributor to low and variable recoveries. Assessment of acid, base, and oxidant washes, as well as the order of treatment, showed that a base-water-acid-water-oxidizer-water wash procedure resulted in consistent, accurate spiked recovery (>90%) and reproducible results (S rel ≤4%). By applying this cleaning procedure to the previously used coupons that failed the cleaning acceptance criteria, multiple analysts were able to obtain consistent recoveries from day-to-day for different APIs, and API/excipient ratios at various spike levels. We successfully applied our approach for cleaning verification of small molecules (MW<1000Da) as well as large biomolecules (MW up to 50,000Da). Method robustness was greatly influenced by the sample preparation procedure, especially for analyses using total organic carbon (TOC) determination., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The peak capacity of small columns packed with 2.7μm core-shell particles and 1.8μm fully porous particles were compared at high temperatures using very steep (fast) gradient conditions and quite high linear velocities using the same instrument configuration as used to transfer first dimension effluent to the second dimension column as done in on-line comprehensive two-dimensional liquid chromatography. The experimental peak capacities of small columns (2.1mm×30mm) packed with both types of particles were measured with fast gradients (9s to 2min) at high temperature (95°C) using both the same flow rate (1.75mL/min) and then at different flow rates at the same pressure (400bar). Equal or slightly better peak capacities were achieved with the core-shell particle columns as compared to the fully porous particle columns at the same backpressure or the same flow rate. However, core-shell particles offer a real advantage over the smaller, fully porous particles because they can be operated at higher flow rates thus gradient mixer flush out and column reequilibration can be done in less time thereby allowing a greater fraction of the second dimension cycle time to be dedicated to the gradient time., (Copyright © 2014 Elsevier B.V. All rights reserved.)

A major issue in optimizing the resolving power of two-dimensional chromatographic separations is the choice of the two phases so as to maximize the distribution of the analytes over the separation space. In this work, we studied the choice of appropriate reversed phases to use in on-line comprehensive two-dimensional liquid chromatography (LC×LC). A set of four chemically different conventional bonded reversed phases was used in the first dimension. The second dimension column was either a conventional bonded C18 phase or a carbon-clad phase (CCP). The LC×LC chromatograms and contour plots were all rather similar indicating that the selectivities of the two phases were also similar regardless of the reverse phase column used in the first dimension. Further, the spatial coverage seen with all four first dimension stationary phases when paired with a second dimension C18 phase were low and the retention times were strongly correlated. However, when the C18 column was replaced with the CCP column much improved separations were observed with higher spatial coverages, greater orthogonalities and significant increases in the number of observed peaks., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Previously, we described a novel method for cladding elemental carbon onto the surface of catalytically activated silica by a chemical vapor deposition (CVD) method using hexane as the carbon source and its use as a substitute for carbon-clad zirconia.1,2 In that method, we showed that very close to exactly one uniform monolayer of Al (III) was deposited on the silica by a process analogous to precipitation from homogeneous solution in order to preclude pore blockage. The purpose of the Al(III) monolayer is to activate the surface for subsequent CVD of carbon. In this work, we present an improved procedure for preparing the carbon-clad silica (denoted CCSi) phases along with a new column packing process. The new method yields CCSi phases having better efficiency, peak symmetry, and higher retentivity compared to carbon-clad zirconia. The enhancements were achieved by modifying the original procedure in three ways: First, the kinetics of the deposition of Al(III) were more stringently controlled. Second, the CVD chamber was flushed with a mixture of hydrogen and nitrogen gas during the carbon cladding process to minimize generation of polar sites by oxygen incorporation. Third, the fine particles generated during the CVD process were exhaustively removed by flotation in an appropriate solvent.

A general protocol for optimizing peak capacity for the separation of low molecular weight molecules under gradient elution conditions has not yet been developed. By studying the effects of gradient time, flow rate, temperature, final eluent composition, and column length on peak capacity, a protocol has been developed for the optimization of a separation of small molecules such as those seen in metabolomic studies. The strategy developed employs the Linear-Solvent-Strength Theory (LSS Theory) to predict retention, building on an approach for the optimization of the peak capacity of large molecules (peptides) in fixed column format separations., (Published by Elsevier B.V.)

The effect of different types of short- and long-range intrachain interactions along the polymeric backbone on the persistence length of a polymer, as well as on other properties such as solvation (characterized by the second virial coefficient), dilute solution conformation, specific refractive index increment, and intrinsic viscosity, were studied using multi-detector size-exclusion chromatography and off-line techniques. The polymers in this study, namely, polystyrene (PS), poly(vinyl chloride) (PVC), and poly(p-vinylbenzyl chloride) (PpVBC), were chosen based on intrachain interactions specific to each, intrachain repulsion in PVC, attraction in PS, and hindered attraction in PpVBC, and also based on a coincidence in molar mass averages and distributions between the polymers. The latter allowed polymeric properties of the three polymers to be compared to each other at the same molar mass and/or degree of polymerization. From the comparisons emerged the effects of intrachain repulsion between consecutive monomers and of the second virial coefficient on chain stiffness and solvation. The increase in the second virial coefficient corresponded to an increase in both polymer solvation and rigidity, while increased intrachain repulsion between consecutive monomers increased polymer solvation while decreasing chain rigidity.

We present a method by which to obtain the absolute, chemical-heterogeneity-corrected molar mass (M) averages and distributions of copolymers and apply the method to a gradient random copolymer of styrene and methyl methacrylate in which the styrene percentage decreases from approximately 30% to 19% as a function of increasing molar mass. The method consists of separation by size-exclusion chromatography (SEC) with detection using multi-angle static light scattering (MALS), differential viscometry (VISC), differential refractometry (DRI), and ultraviolet absorption spectroscopy (UV) and relies on the preferential absorption of styrene over methyl methacrylate at 260 nm. Using this quadruple-detector SEC/MALS/UV/VISC/DRI approach, the percentage of styrene (%St) in each elution slice is determined. This %St is then used to determine the specific refractive index increment, corrected for chemical composition, at each elution slice, which is then used to obtain the molar mass at each slice, corrected for chemical composition. From this corrected molar mass and from the chemical-composition-corrected refractometer response, the absolute, chemical-heterogeneity-corrected molar mass averages and distribution of the copolymer are calculated. The corrected molar mass and intrinsic viscosity at each SEC elution slice are used to construct a chemical-heterogeneity-corrected Mark-Houwink plot. The slice-wise-corrected M data are used, in conjunction with the MALS-determined R(G,z ) of each slice, to construct a conformation plot corrected for chemical heterogeneity. The corrected molar mass distribution (MMD) of the gradient copolymer extends over an approximately 30,000 g/mol wider range than the uncorrected MMD. Additionally, correction of the Mark-Houwink and conformation plots for the effects of chemical heterogeneity shows that the copolymer adopts a more compact conformation in solution than originally concluded.