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Issue 8, 2020
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Improving the throughput of immunoaffinity purification and enzymatic digestion of therapeutic proteins using membrane-immobilized reagent technology

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Abstract

Continued interest in protein therapeutics has motivated the development of improved bioanalytical tools to support development programs. LC-MS offers specificity, sensitivity, and multiplexing capabilities without the need for target-specific reagents, making it a valuable alternative to ligand binding assays. Immunoaffinity purification (IP) and enzymatic digestion are critical, yet extensive and time-consuming components of the “gold standard” bottom-up approach to LC-MS-based protein quantitation. In the present work, commercially available technology, based on membrane-immobilized reagents in spin column and plate format, is applied to reduce IP and digestion times from hours to minutes. For a standard monoclonal antibody, the lower limit of quantitation was 0.1 ng μL−1 compared to 0.05 ng μL−1 for the standard method. A pharmacokinetics (PK) study dosing Herceptin in rat was analyzed by both the membrane and the standard method with a total sample processing time of 4 h and 20 h, respectively. The calculated concentrations at each time point agreed within 8% between both methods, and PK values including area under the curve (AUC), half-life (T1/2), mean residence time (MRT), clearance (CL), and volume of distribution (Vdss) agreed within 6% underscoring the utility of the membrane methodology for quantitative bioanalysis workflows.

Graphical abstract: Improving the throughput of immunoaffinity purification and enzymatic digestion of therapeutic proteins using membrane-immobilized reagent technology

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Supplementary files

Article information


Submitted
27 Jan 2020
Accepted
06 Mar 2020
First published
11 Mar 2020

Analyst, 2020,145, 3148-3156
Article type
Paper

Improving the throughput of immunoaffinity purification and enzymatic digestion of therapeutic proteins using membrane-immobilized reagent technology

M. R. Robinson, L. A. Vasicek, C. Hoppmann, M. Li, G. Jokhadze and D. S. Spellman, Analyst, 2020, 145, 3148
DOI: 10.1039/D0AN00190B

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