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

Ahmad, Imad A. Haidar; Kiffer, Alaina; Barrientos, Rodell C.; Losacco, Gioacchino Luca; Singh, Andrew N.; Shchurik, Vladimir; Wang, Heather; Mangion, Ian; Regalado, Erik L.

doi:10.1021/acs.analchem.1c05551

Cited by 17 publications

(6 citation statements)

References 44 publications

(68 reference statements)

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“…In silico modeling is a valuable approach for the seamless optimization of HPLC parameters. ,, In this study, we investigated whether a mathematical model can be effectively constructed in HIC under various conditions. Trypsin inhibitor, one of the mixture 1 components, consisted of two peaks, which we annotated here as trypsin inhibitors I and II.…”

Section: Results and Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Results and Discussionmentioning

confidence: 99%

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

et al. 2022

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“…[4][5][6][7] Characterization of antibodies, large proteins, viral vectors, virus-like particles, and other macromolecules that act as active pharmaceutical ingredients can often be challenging. [8][9][10][11] However, analytical assays that quantify small molecules present in vaccine products are needed throughout initial development to commercialization. [12][13][14] Among these, adjuvants are crucial components that enhance the immune response triggered by vaccines.…”

Section: Introductionmentioning

confidence: 99%

“…The resulting experimental data were processed using the ACD Labs/LC Simulator 2021.1.2 release (Version L35R41), Advanced Chemistry Development, Inc. (ACD), Toronto, Ontario, Canada (further referred to as an LC Simulator). A resolution map was obtained from LC Simulator software, indicating the optimum separation conditions listed in Figure The listed references describe how LC Simulator software works 8,[37][39]…”

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confidence: 99%

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

Hemida,

Barrientos,

Kinsey

et al. 2024

Preprint

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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 RPLC-UV-CAD, and component identification (RPLC-MS) across complex vaccine formulations, including the use of surfactants (e.g., polysorbates), as well as their separation from adjuvant targets.

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“…For example, several groups have worked on predicting the effect of the mismatch between sample solvent and the mobile phase on peak shape and width in both one-and two-dimensional liquid chromatography (LC) [1][2][3][4]. Other groups have advanced understanding of the effect of temperature on retention in reversedphase separations [5], worked to improve general models of retention and selectivity in RP separations [6,7], and explored the limits and implementations of retention models as a key component of method development workflows for both one-and two-dimensional LC separations [8][9][10]. Although several groups have assembled substantial LC datasets related to metabolite identification [11,12], peptide identification [13,14], characterization of column selectivity [7], or the application of retention indices [15], these datasets have always been highly asymmetric, with either data for many compounds on one column, or data for a few compounds on many columns.…”

Section: Introductionmentioning

confidence: 99%

Characterization of a High Throughput Approach for Large Scale Retention Measurement in Liquid Chromatography

Kempen

Dahlseid

Lauer

et al. 2023

Preprint

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Many contemporary challenges in liquid chromatography - such as the need for “smarter” method development tools, and deeper understanding of chromatographic phenomena - could be addressed more efficiently and effectively with larger volumes of experimental retention data than are available. The paucity of publicly accessible, high-quality measurements has largely been due to the high cost in time and resources associated with traditional retention measurement approaches. Recently we described an approach to improve the throughput of such measurements by using very short columns (typically 5 mm), while maintaining measurement accuracy. In this paper we present a perspective on the characteristics of a dataset containing about 13,000 retention measurements obtained using this approach, and describe a sample introduction method that improves upon the prior work. The dataset is comprised of results for 35 different small molecules, nine different stationary phases, and several mobile phase compositions for each analyte/phase combination. During the acquisition of these data, we have interspersed repeated measurements of a small number of compounds for quality control purposes. The data from these measurements not only enable detection of outliers but also assessment of the repeatability and reproducibility of retention measurements over time. For retention factors greater than 1, the mean relative standard deviation (RSD) of replicate (typically n=5) measurements is 0.4%, and the standard deviation of RSDs is 0.4%. Most differences between selectivity values measured six months apart for 15 non-ionogenic compounds were in the range of +/- 1%, indicating good reproducibility. A critically important observation from these analyses is that selectivity defined as retention of a given analyte relative to the retention of a reference compound (kx/kref) is a much more consistent measure of retention over a time span of months compared to the retention factor alone. While this work and dataset also highlight the importance of stationary phase stability over time for achieving reliable retention measurements, we are nevertheless optimistic that this approach will enable the compilation of large databases (>> 10,000 measurements) of retention values over long time periods (years), which can in turn be leveraged to address some of the most important contemporary challenges in liquid chromatography. All the data discussed in the manuscript are provided as Supplemental Information.

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

Cited by 17 publications

References 44 publications

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

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

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

Characterization of a High Throughput Approach for Large Scale Retention Measurement in Liquid Chromatography

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