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.

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.

Introducing online multicolumn two-dimensional liquid chromatography screening for facile selection of stationary and mobile phase conditions in both dimensions.

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.

High resolution two-dimensional liquid chromatography coupled with mass spectrometry for robust and sensitive characterization of therapeutic antibodies at the peptide level.

Separations of complex peptide mixtures have been a common target application for two-dimensional liquid chromatography over the last few decades. These separations have most frequently been carried out at the capillary scale, with columns on the order of 75 µm i.d. and flow rates on the order of 500 nL/min. Recently, however, several groups have worked to optimize comprehensive 2D-LC (LC × LC) separations of peptides at the analytical scale (i.e., 2 mm i.d. columns, and ca. 1 mL/min flow rates) and demonstrated peak capacities on the order of 5000 in analysis times of a few hours, using reversed-phase separations in both dimensions. In this paper we aim to advance the performance of such separations in two primary ways. First, we demonstrate that active solvent modulation (ASM) can be used to improve the 2D peak capacity by both enabling use of long and highly efficient first dimension (1D) columns, and by mitigating the deleterious effects of injecting large fractions of 1D effluent into the small columns that are required for fast and highly sensitive second dimension (2D) separations. Taken together these two benefits enable the realization of a peak capacity of 10,000 in an analysis time of four hours. This comes at the cost of increased instrument complexity compared to 1D-LC separations, but the 2D-LC approach is unquestionably the most efficient way to improve upon the resolving power of existing 1D-LC. Second, we have systematically studied the compromise between the peak capacity of each 2D separation and the operating pressure required to achieve that peak capacity. Understanding this compromise will be important to the development of LC × LC methods that both produce high peak capacities, and are sufficiently robust to operate for days at a time without significant losses in separation performance. Based on the results of this study we chose conditions for subsequent separations that required less than 400 bar operating pressure in the second dimension, but yielded a 2D peak capacity of about 3500 in 2 h. After 160 h of continuous operation of the LC × LC separation under these conditions (and about 20,000 injections into the 2D column) the 2D column had only lost about 18% of its initial isocratic efficiency. These results should motivate further development and implementation of such high performing and robust separations for the identification and quantification of peptides in a variety of application areas, including digests of therapeutic proteins such as monoclonal antibodies.