Evaluation of calibration-free concentration analysis provided by Biacore™ systems

Pol, Ewa; Roos, Håkan; Markey, Francis; Elwinger, Fredrik; Shaw, Alan; Karlsson, Robert

doi:10.1016/j.ab.2016.07.009

Cited by 37 publications

(51 citation statements)

References 18 publications

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“…Kinetic analysis and CFCA share theoretical concepts, but differ in the way data are generated and analyzed (Pol et al 2016). CFCA analysis is focused on initial binding rates, and requires that the transport coefficient can be precalculated and used as a constant during data analysis.…”

Section: Theoretical Background To Cfcamentioning

confidence: 99%

“…CFCA has been used in a number of situations (Table 1) including: quality control of protein reagents, to measure the effect of modification or stress on protein activity, to (Watanabe et al 2013), or batch-to-batch comparisons (Pol et al 2016). CFCA data have been successfully correlated to growth inhibition in vaccine development (Spiegel et al 2015) and for the identification of biomarkers Shah et al 2015).…”

Section: Applicationsmentioning

confidence: 99%

“…This is surprising, as CFCA can potentially be developed to an absolute concentration method. For this, a detailed understanding of the transport coefficient is necessary (Pol et al 2016). By examining the parameters in the transport coefficient, it is possible to estimate the repeatability and accuracy of CFCA measurements.…”

Section: The Transport Coefficientmentioning

confidence: 99%

“…The degree of MTL can be obtained from the following relation (de Mol and Fischer 2008;Pol et al 2016):…”

Section: The Degree Of Mtlmentioning

confidence: 99%

“…This k t value was obtained using an Mw of 150 kDa and a diffusion coefficient of 4.0*10 −11 m 2 /s as inputs (Eq. 1) to mimic antibody experiments described by Pol et al (2016).…”

Section: The Degree Of Mtlmentioning

confidence: 99%

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Biosensor binding data and its applicability to the determination of active concentration

Karlsson¹

2016

Biophys Rev

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Protein concentration data are required for understanding protein interactions and are a prerequisite for the determination of affinity and kinetic properties. It is vital for the judgment of protein quality and for monitoring the effect of therapeutic agents. Protein concentration values are typically obtained by comparison to a standard and derived from a standard curve. The use of a protein standard is convenient, but may not give reliable results if samples and standards behave differently. In other cases, a standard preparation may not be available and has to be established and validated. Using surface plasmon resonance (SPR) biosensors, an alternative concentration method is possible. This method is called calibration-free concentration analysis (CFCA); it generates active concentration data directly and without the use of a standard. The active concentration of a protein is defined through its interaction with its binding partner. This concentration can differ from the total protein concentration if some protein fraction is incapable of binding. If a protein has several different binding sites, active concentration data can be established for each binding site using site-specific interaction partners. This review will focus on CFCA analysis. It will reiterate the theory of CFCA and describe how CFCA has been applied in different research segments. The major part of the review will, however, try to set expectations on CFCA and discuss how CFCA can be further developed for absolute and relative concentration measurements.

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Section: Theoretical Background To Cfcamentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: The Transport Coefficientmentioning

confidence: 99%

“…The degree of MTL can be obtained from the following relation (de Mol and Fischer 2008;Pol et al 2016):…”

Section: The Degree Of Mtlmentioning

confidence: 99%

“…This k t value was obtained using an Mw of 150 kDa and a diffusion coefficient of 4.0*10 −11 m 2 /s as inputs (Eq. 1) to mimic antibody experiments described by Pol et al (2016).…”

Section: The Degree Of Mtlmentioning

confidence: 99%

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Biosensor binding data and its applicability to the determination of active concentration

Karlsson¹

2016

Biophys Rev

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Label‐Free Affinity‐Based Biosensors

2022

Biochips and Medical Imaging

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VHH characterization. Comparison of recombinant with chemically synthesized anti‐HER2 VHH

Hartmann

Botzanowski

Galibert³

et al. 2019

Protein Science

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In the continuous exploration of the VHH chemistry, biochemistry and therapeutic future use, we investigated two different production strategies of this small antibody‐like protein, using an anti‐HER2 VHH as a model. The total chemical synthesis of the 125 amino‐acid peptide was performed with reasonable yield, even if optimization will be necessary to upgrade this kind of production. In parallel, we expressed the same sequence in two different hosts: Escherichia coli and Pichia pastoris. Both productions were successful and led to a fair amount of VHHs. The integrity and conformation of the VHH were characterized by complementary mass spectrometry approaches, while surface plasmon resonance experiments were used to assess the VHH recognition capacity and affinity toward its “antigen.” Using this combination of orthogonal techniques, it was possible to show that the three VHHs—whether synthetic or recombinant ones—were properly and similarly folded and recognized the “antigen” HER2 with similar affinities, in the nanomolar range. This opens a route toward further exploration of modified VHH with unnatural amino acids and subsequently, VHH‐drug conjugates.

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Evaluation of calibration-free concentration analysis provided by Biacore™ systems

Cited by 37 publications

References 18 publications

Biosensor binding data and its applicability to the determination of active concentration

Biosensor binding data and its applicability to the determination of active concentration

Label‐Free Affinity‐Based Biosensors

VHH characterization. Comparison of recombinant with chemically synthesized anti‐HER2 VHH

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