High Throughput Determination and QSER Modeling of Displacer DC‐50 Values for Ion Exchange Systems

Liu, Jia; Yang, Ting; Ladiwala, Asif; Cramer, Steven M.; Breneman, Curt M.

doi:10.1080/01496390600894822

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…These results also demonstrate that data obtained from multiple displacer concentrations can provide important information on displacer efficacy. Thus, while previous analyses of high‐affinity displacers have focused primarily on the DC‐50 value32 (i.e., the concentration that displaced 50% of a protein), the use of robotics now enables a more complete evaluation of displacers under a range of concentrations. We believe that selectivity pathway plots derived from this data are a more rigorous method of evaluating chemically selective displacers.…”

Section: Resultsmentioning

confidence: 99%

Characterization and design of chemically selective cationic displacers using a robotic high‐throughput screen

Morrison

Cramer

2009

Biotechnology Progress

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A robotic high-throughput displacer screen was developed and employed to identify chemically selective displacers for several protein pairs in cation exchange chromatography. This automated screen enabled the evaluation of a wide range of experimental conditions in a relatively short period of time. Displacers were evaluated at multiple concentrations for these protein pairs, and DC-50 plots were constructed. Selectivity pathway plots were also constructed and different regimes were established for selective and exclusive separations. Importantly, selective displacement was found to be conserved for multiple protein pairs, demonstrating the technique to be applicable for a range of protein systems. Although chemically selective displacers were able to separate protein pairs that had similar retention in ion exchange but different surface hydrophobicities, they were not able to distinguish protein pairs with similar surface hydrophobicities. This corroborates that displacer-protein hydrophobic interactions play an important role for this class of selective displacers. Important functional group moieties were established and efficient displacers were identified. These results demonstrate that the design of chemically selective displacers requires a delicate balance between the abilities to displace proteins from the resin and to bind to a selected protein. The use of robotic screening of displacers will enable the extension of chemically selective displacement chromatography beyond hydrophobic displacer-protein interactions to other secondary interactions and more selective displacement systems.

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Section: Resultsmentioning

confidence: 99%

Characterization and design of chemically selective cationic displacers using a robotic high‐throughput screen

Morrison

Cramer

2009

Biotechnology Progress

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“…Only recently has the 96-well plate format been used to examine purification conditions by evaluating a single product with multiple purification systems. Cramer et al have used the 96-well format extensively for screening displacers for displacement chromatography (Cramer et al, 2001;Liu et al, 2006;Mazza et al, 2002a;Rege et al, 2003Rege et al, , 2005aSunasara et al, 2003), but did not use 96-well filter plates, which offer significant advantages in product recovery, design flexibility, and throughput. Kramarczyk was the first to describe the application of batch binding to the 96-well filter-plate format for the development of protein purification steps, examining hydrophobic interaction chromatography (HIC) and ion exchange (IEX) resins (Kramarczyk, 2003).…”

Section: Introductionmentioning

confidence: 99%

High‐throughput screening of chromatographic separations: I. Method development and column modeling

Coffman

Kramarczyk

Kelley

2008

Biotech & Bioengineering

190

138

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The development of purification processes for protein biopharmaceuticals is challenging due to compressed development timelines, long experimental times, and the need to survey a large parameter space. Typical methods for development of a chromatography step evaluate several dozen chromatographic column runs to optimize the conditions. An efficient batch-binding method of screening chromatographic purification conditions in a 96-well format with a robotic liquid-handling system is described and evaluated. The system dispenses slurries of chromatographic resins into filter plates, which are then equilibrated, loaded with protein, washed and eluted. This paper evaluates factors influencing the performance of this high-throughput screening technique, including the reproducibility of the aliquotted resin volume, the contact time of the solution and resin during mixing, and the volume of liquid carried over in the resin bed after centrifugal evacuation. These factors led to the optimization of a batch-binding technique utilizing either 50 or 100 mL of resin in each well, the selection of an industrially relevant incubation time of 20 min, and the quantitation of the holdup volume, which was as much as one quarter of the total volume added to each well. The results from the batchbinding method compared favorably to chromatographic column separation steps for a cGMP protein purification process utilizing both hydrophobic interaction and anionexchange steps. These high-throughput screening tools can be combined with additional studies on the kinetics and thermodynamics of protein-resin interactions to provide fundamental information which is useful for defining and optimizing chromatographic separations steps.

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“…Thus, this computational method allows for the ability to filter potential chromatographic ligands based on their ability to effectively capture the protein and yield higher percentage recovery. 4,8,16,17,49 To attain the very high level of purity required for biopharmaceuticals, we explored the use of a hybrid computational and empirical method to separate variants of Glargine from the desired insulin molecule. Computational modeling predicted small differences in binding affinity (DG), which correlated well with retention time and capacity factor (k') differences of the protein on the column.…”

Section: Resultsmentioning

confidence: 99%

“…Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR) or quantitative structure-retention relationship (QSRR) to identify and rank order affinity ligands based on their potential ability to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. [15][16][17] In most applications of QSAR or QSPR, detailed analyses are limited to the affinity ligands due to the complexity and computational cost of performing detailed atomistic modeling of these complex interactions. Hence, only a subset of the key features important for protein-ligand interaction as it pertains to purification is captured by the use of such computational modeling to direct bioprocess development.…”

Section: Introductionmentioning

confidence: 99%

Targeted purification development enabled by computational biophysical modeling

Insaidoo

Rauscher

Smithline

et al. 2014

Biotechnology Progress

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Chromatographic and non-chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid-liquid interface. These interactions are dominated by the protein-surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein-surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non-chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein-ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform.

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High Throughput Determination and QSER Modeling of Displacer DC‐50 Values for Ion Exchange Systems

Cited by 9 publications

References 26 publications

Characterization and design of chemically selective cationic displacers using a robotic high‐throughput screen

Characterization and design of chemically selective cationic displacers using a robotic high‐throughput screen

High‐throughput screening of chromatographic separations: I. Method development and column modeling

Targeted purification development enabled by computational biophysical modeling

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