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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, Advance Article
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, Advance Article , DOI: 10.1039/D0AN00190B

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