Issue 44, 2022

Process integrated biosensors for real-time monitoring of antibodies for automated affinity purification

Abstract

Therapeutic monoclonal antibodies (mAbs) provide new means for treatments of a wide range of diseases and comprise a large fraction of all new approved drugs. Production of mAbs is expensive compared to conventional drug production, primarily due to the complex processes involved. The affinity purification step is dominating the cost of goods in mAb manufacturing. Process intensification and automation could reduce costs, but the lack of real-time process analytical technologies (PAT) complicates this development. We show a specific and robust fiber optical localized surface plasmon resonance (LSPR) sensor technology that is optimized for in-line product detection in the effluent in affinity capture steps. The sensor system comprises a flow cell and a replaceable sensor chip functionalized with biorecognition elements for specific analyte detection. The high selectivity of the sensor enable detection of mAbs in complex sample matrices at concentrations below 2.5 μg mL−1. In place regeneration of the sensor chips allowed for continuous monitoring of multiple consecutive chromatographic separation cycles. Excellent performance was obtained at different purification scales with flow rates up to 200 mL min−1. This sensor technology facilitates efficient column loading, optimization, and control of chromatography systems, which can pave the way for continuous operation and automation of protein purification steps.

Graphical abstract: Process integrated biosensors for real-time monitoring of antibodies for automated affinity purification

Supplementary files

Article information

Article type
Paper
Submitted
26 Sep 2022
Accepted
21 Oct 2022
First published
21 Oct 2022
This article is Open Access
Creative Commons BY license

Anal. Methods, 2022,14, 4555-4562

Process integrated biosensors for real-time monitoring of antibodies for automated affinity purification

T. Tran, E. Martinsson, R. Gustavsson, O. Tronarp, M. Nilsson, K. R. Hansson, I. Lundström, C. Mandenius and D. Aili, Anal. Methods, 2022, 14, 4555 DOI: 10.1039/D2AY01567F

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