the immuassay handbook parte29

209© 2013 David G. Wild. Published by Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/B978-0-08-097037-0.00015-4

A variety of technologies, including Surface Plasmon Res-onance, BioLayer Interferometry, Resonant Waveguide Grating, and Quartz Crystal Microbalance, permit studies of molecular interactions in a label-free format. With these technologies it is possible to detect directly, and monitor continuously, specific binding between, for example, an antibody and an antigen or a receptor and a ligand. These technologies provide a more complete picture of the dynamics of binding events than end-point assays such as ELISA. Over the last twenty years label-free technologies have contributed to a shift in focus from the affinity to the kinetics of an interaction. In parallel the technology has matured to provide higher sensitivity and higher through-put. This has had a major impact on antibody selection and development procedures and is starting to impact drug dis-covery where kinetic data is now being used to predict drug residence time and thereby drug efficacy.

Label-free technologies can also provide key informa-tion on activity and concentration very rapidly—within minutes—and are increasingly used for activity monitor-ing in bioprocess development and manufacturing. The use of label-free technologies is widespread both in aca-demia and in industry. Based on the number of published articles, Surface Plasmon Resonance (SPR) is the dominating technology.

In this chapter we provide a general background to pro-tein interactions, provide essential information on SPR technology and discuss the use of SPR for binding site, kinetic, and concentration analyses.

Protein Interactions in Basic and Applied ResearchThe understanding of protein–protein interactions and the effects they have in a biological system remains incomplete.

Protein expression can be studied using specific binding reagents, such as antibodies, to detect the protein of inter-est in a cell lysate or a tissue preparation (Uhlén et al., 2005, and http://www.proteinatlas.org/ ). Together with expres-sion studies this can establish the spatial and temporal pat-terns of a protein, and while this can often provide some clues to function, this latter aspect is much more complex and requires information concerning interactions with other biomolecules.

Orchard et al. (2012) discuss the efforts by the Interna-tional Molecular Exchange Consortium (http://www.imexconsortium.org/) to establish a single search interface for users to learn about protein–protein interactions. Of the interactions listed in the IMEX data resource only

5–6% are identified through a direct interaction. The majority of all interactions are identified by physical asso-ciation i.e., the proteins are present in a complex where all partners may not be identified. This suggests that still very little is known of the specificity of protein interactions, and consequently also of their importance in health and disease.

In vitro methods such as SPR can provide direct evi-dence of protein–protein interactions. More than one thousand papers a year describing different applications of SPR have been published since 2006, addressing impor-tant questions, and the total number of articles now exceed twelve thousand.

Do these molecules interact (Lin et al., 2008; Yang et al., 2010; Prinsloo et al., 2012)? What protein domains are involved in the interaction (Suzuki et al. 2009)? How does this mutation affect binding (Zhukov et al., 2011)? How does post-translational modification of an Fc receptor affect binding of immunoglobulin (Radaev and Sun, 2001; Zeck et al., 2011; Ferrara et al., 2011)? Do these molecules bind to one or several sites (Navratilova et al., 2012)? These are but a few of many typical questions that are readily addressed by SPR studies.

While these are very practical questions, SPR is fre-quently used to determine the kinetics and affinities of molecular interactions. At a first level of interpretation this data is used for sorting molecular interactions as being of high, intermediate, or low affinity, or with fast or slow on and off-rates. In drug discovery, Copeland et al. (2006), Lu and Tonge (2010), and Swinney and Anthony (2012), have taken this one step further and link binding kinetics to pharmacodynamics and the molecular mechanism of action. On-rates and concentrations are considered impor-tant for target occupancy, while off-rates may be predic-tive of drug-target residence time.

Interaction kinetics has also been linked to specificity. Wu et al. (2007) suggested that ka driven mutations of Motavizumab increased non-specific binding to various tissues, while Biehl (2011) linked complex antibody kinet-ics to specificity and described screening methods for identification of monophasic binders with high specificity.

When new proteins are expressed and tested no stan-dard preparation is available and instead the concentration is often derived from a combination of purity tests and measurement of optical density. Such measurements how-ever give no information of the active concentration i.e., the concentration of molecules that are able to bind to their specific interaction partner. Pol (2010) described an SPR-based method for active concentration analysis that can be used already on non-purified samples. This method has the potential to measure protein quality and to improve the reliability of ligand-binding assays.

Surface Plasmon Resonance in Binding Site, Kinetic, and Concentration AnalysesRobert Karlsson ([email protected])

C H A P T E R

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210 The Immunoassay Handbook

Known reagent quality is crucial for all types of research and testing and two recent papers (Stack et al. [2011] and O’Hara et al. [2012]) discuss quality control requirements of reagents such as soluble receptors, monoclonal anti-bodies, polyclonal antibodies, engineered proteins, peptides, and their conjugates from an industry perspec-tive. Both papers point to SPR as one of the several tech-nologies that can be used to determine critical reagent parameters.

Screening for antibody candidates with given specifica-tions is routine in development of diagnostic reagents and antibody therapeutics. The specification to be met by the successful candidate typically includes site specificity, slow off-rate and defined cross-reactivity with targets from dif-ferent species. A typical antibody-screening assay, as described by Säfsten et al. (2006), includes immobilization of a capture antibody followed by antibody and antigen injections. Recently Katayama et al. (2012) described opti-mization of immobilization levels leading to improved kinetic discrimination. Hayes et al. (2012) focused on the antibody construct and reported higher expression levels and improved screening results when ScFv was replaced by ScAb. Hardy et al. (2012) took a different approach and developed a screening assay using self-assembled lipid sur-faces to identify antibodies that mimic HIV neutralizing antibodies 4E10 and 2F5.

As antibody and recombinant therapeutics are devel-oped and move into process development and manufactur-ing, SPR can again be used to measure active concentration and to validate that critical interaction properties, such as antigen and Fc-receptor binding, are unaffected by filtra-tion and chromatographic procedures. The use of SPR here supports quality by design procedures and compara-bility studies. This application area is growing but here there are fewer publications.

Once the antibody enters into pre-clinical and clinical research, SPR is again used but now to determine the pres-ence of anti-drug antibodies (ADA) in animal or human serum. Studies of ADA to panitumumab (Lofgren et al., 2007), tocilizumab (Stubenrauch et al., 2010), and epoetin alfa (Barger et al., 2012), indicate earlier detection and more complete detection of ADAs with SPR than with more sensitive assays such as bridging ELISA. This seem-ingly contradictory finding can be explained by the obser-vation that SPR can detect low affinity ADAs and ADAs of IgG4 type. The clinical relevance of such ADAs is unclear as yet. While few studies have employed both ELISA and SPR, these findings suggest that a combination of ELISA and SPR approaches can give a broader picture of the developing ADA response. The combined data may there-fore provide a better picture of safety and efficacy than if either of the technologies was used alone.

SPR BiosensorsTECHNOLOGY PRINCIPLESSPR biosensors directly monitor the interaction between an analyte molecule introduced in solution and a second mole-cule or molecular complex present on a sensor surface. Detection of binding events is instantaneous and requires no

labeling or auxiliary reagents. The sensor surface typically consists of a thin-gold film deposited on a glass support. Normally, carboxymethylated dextran polymers are attached to the gold film, providing a convenient starting point for the immobilization of biomolecules ( Johnsson et al., 1995).

The gold side of the sensor surface is placed in contact with a flow system. This makes it possible to address sam-ples to discrete spots or flow cells on the sensor surface. The glass side of the sensor surface is positioned on a prism and is thereby connected to the optical unit of the biosen-sor. With this configuration, it is possible to exploit the phenomenon of surface plasmon resonance (Fig. 1). Under conditions of total internal reflection, energy, and momen-tum from light at a certain angle of incidence, are trans-formed from photons into surface plasmons in the metal film of the sensor chip. At this angle, which depends on the refractive index close to the gold/dextran layers of the sen-sor surface, the intensity of the reflected light is therefore reduced. When an analyte binds to the sensor surface the resulting change in mass concentration causes a change in refractive index at the sensor surface, and the conditions for SPR are fulfilled at a slightly different angle of inci-dence. This is monitored by the detector as a shift in the position at which the light intensity minimum is observed. The position of the SPR angle is plotted vs. time to yield a sensorgram. The SPR response on the y-axis of the sensor-gram is expressed in response units (RU). One RU corre-sponds to a mass increase of approximately 1 pg/mm2 of protein.

SENSORGRAMS AND REPORT POINTSFrom a technical perspective, all SPR experiments can be viewed as a series of sample injections, where the duration of each injection and the time between injections is varied. Typical injection times are from 30 s to 10 min and the flow rate can be varied during an experiment, with typical sample volumes ranging from 5–100 µL. In most cases, a negative control surface is employed to correct for the changes in bulk refractive index between different buffers, enabling a reference-subtracted response that depends specifically on molecular interactions at the surface to be viewed directly.

SPR biosensors are computer controlled and the control software guides the user through the various steps in an assay. Once the assay is programmed, automation allows analysis of more than a hundred samples without user intervention. Results are saved in a file and the data is ana-lyzed using dedicated evaluation software.

The interpretation of sensorgrams is relatively straight-forward, as shown in the example presented in Fig. 2. Here, data from two interactions are displayed in an over-lay plot. Analyte 1 binds rapidly to the immobilized ligand and steady state is reached during injection. As the injec-tion is stopped, the analyte dissociates in buffer flow and the signal rapidly approaches the baseline level. In con-trast, analyte 2 binds more slowly but also dissociates at a much slower rate. Interestingly both samples have the same affinity for the immobilized ligand, but binding and dissociation rates differ significantly. As the affinity con-stant, KD, equals the ratio between the rate constants, kd/ka, antibodies (or any other molecules) of the same

211CHAPTER 2.12 Surface Plasmon Resonance in Binding Site, Kinetic, and Concentration Analyses

affinity can have very different half-lives ranging from minutes to days.

Five report points are set for each injection. One report point defines the baseline level and 4 other report points provide information of response levels at specified times. Report points during injection are often called binding early and binding late respectively while report points after injection are called stability early and stability late. The result file gives access to both the full sensorgram and

to a table listing the response values associated with each report point.

In screening assays with hundreds to thousands of sen-sorgrams, report point values can be used to rapidly dis-play data to follow trends, and set threshold values. Report point values are also used for data analysis in concentration assays, in large epitope mapping studies and whenever response levels from separate injections are analyzed and correlated to each other.

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FIGURE 1 (a) Changes in mass concentration at the sensor surface alter the refractive index and thereby alter the angle at which an SPR signal is generated. This is observed as a shift in the angle at which the minimum of intensity of reflection is detected. This shift is plotted as response units (RU) against time in a sensorgram. (b) A sensor chip enables adsorption or covalent coupling of molecules to the sensor chip. By using a polymer matrix as scaffold, immobilized molecules are accessible for interactions with their binding partners in three-dimensional space. (c) A flow system with four flow cells in contact with the sensor surface. Each flow cell can be addressed separately. This allows immobilization of different ligands in different flow cells and for the use of a non-functionalized flow cell as a reference. (d) A control software with method builder functionality is used to set experiments and evaluation software is used for display and analysis of interaction data. (e) Kits and buffers simplify coupling of molecules to the sensor chip and contribute to ease of use. (The color version of this figure may be viewed at www.immunoassayhandbook.com)


IMMOBILIZATIONBiomolecules can be immobilized on the sensor surface either by covalent coupling or via capture. These two prin-ciples are illustrated in Fig. 3. Covalent coupling through amine groups, carboxyl groups, thiol groups, or sugar alco-hols gives stable surfaces, and the level of immobilization can be controlled over a very wide range (less than 1 RU to over 10,000 RU). Covalent coupling involves activation of surface carboxyl groups, coupling of the biomolecule, and deactivation of unreacted groups on the surface.

This approach minimizes the consumption of the mol-ecule for immobilization, but normally requires a purified preparation of the biomolecule. For covalent protein immobilization, amine coupling is usually straightforward and is often the first method of choice. If this proves to be difficult, a capture approach can be tried if the biomolecule is appropriately tagged.

Capture is a two-step process in which an affinity cap-ture molecule, such as an anti-histidine antibody, an anti-GST antibody, a biotin capture reagent, or an anti-Fc antibody, is first covalently immobilized to the sensor sur-face. In the second step, the biomolecule of interest is injected and captured ready for the analyte injection. In a capture assay, both analyte and ligand are removed from the sensor surface between each cycle. With this approach, it is possible to use a single sensor surface for interaction studies with many different surface-bound biomolecules. Another advantage is that it is possible to capture biomol-ecules directly from culture media or cell lysates without prior purification. Capture is particularly useful for the analysis of tagged proteins and for antibodies. The poten-tial drawbacks of capture techniques are reduced stability and lower binding capacity.

However in some cases stability can be improved by optimizing the protein construct. Fischer et al. (2011) report that the introduction of a double histidine tag, where tags are separated by a short spacer, dramatically improves the stability of histidine capture.

Combinations of capture and covalent coupling tech-niques are also possible, either by performing capture prior to surface deactivation or by reactivating the surface with the capturing molecule in place. Using these approaches, histi-dine-tagged molecules can be covalently coupled after cap-ture on nickel-loaded nitrilotriacetic acid (NTA) surfaces or on surfaces with immobilized anti-histidine antibodies.

SURFACE ACTIVITY AND IMMOBILIZATION LEVELSThe binding activity of the immobilized biomolecule is conveniently determined by injecting increasing concen-trations of the analyte, as illustrated in Fig. 4.

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FIGURE 3 The two major principles for immobilizing one interactant (the ligand in BiacoreTM terms) on the sensor surface. In the covalent coupling approach (left panel), different chemical groups such as amine, thiol, or aldehyde groups on the ligand can be used for the covalent coupling. In the capture approach (right panel), a capturing molecule (for example an anti-Fc antibody) is first covalently coupled to the sensor surface, after which the ligand is injected and captured.

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FIGURE 2 In the beginning of the sensorgram a baseline level is established as buffer flows over the surface with the immobilized interactant. As analyte is injected, there is an increase in the response due to the change in mass generated by the complex formation on the surface. Analyte 1 binds rapidly and analyte 2 more slowly. After sample injection, buffer again flows over the surface. Here analyte 1 dissociates more rapidly than analyte 2. For each injection five report points are set. BL baseline, BE binding early, BL binding late, SE stability early, and SL stability late. The associated (response, time) data are used in many data analysis procedures. Report point times are set relative to the injection for instance 10 s before, into, or after the injection. In this case the response level associated with the report point stability early is 145 RU for analyte 1 and 92 RU for analyte 2. (The color version of this figure may be viewed at www.immunoassayhandbook.com)


A ten-fold dilution series of the analyte molecule is injected, starting with the lowest concentration. In this example, the surface becomes saturated at an analyte con-centration of just over 80 µg/mL and the binding capacity is approximately 50 RU. This response can then be com-pared to the amount of immobilized biomolecule and the fraction of active biomolecules can be estimated from the following equation:

The number of binding sites refers to those on the immo-bilized biomolecule. Typical activity numbers range from 0.2 to 0.8 (i.e., 20–80%), but both lower and higher activi-ties can be obtained. The surface activity can be influenced by the method of immobilization. If less than 20% of the immobilized biomolecules are active, alternative immobi-lization chemistry should be considered.

In many cases however proteins are not fully active from the start, so improved expression or purification schemes should also be considered.

Binding Site Analysis—Epitope MappingEpitope mapping is commonly performed with the pur-pose of structural analysis or for the identification and characterization of antibody-binding properties. In the latter case, studies for finding pairs of antibodies that can be used in sandwich assays or screening for therapeutic antibody candidates with potential receptor neutralizing activity are common.

Popular techniques for epitope mapping experiments include ELISA and RIA. One advantage in using SPR

biosensors for this type of experiment lies in the real-time measurements, which allow each step in the mapping experiment to be followed. In the case of structural analy-sis, direct mapping with antibodies as described here can be complemented with peptide inhibition mapping so that regions of the antigen can be linked to specific antibodies. The use of SPR for pair-wise and peptide inhibition map-ping was first described by Fägerstam et al. (1990), and more recently Abdiche et al. (2009) compared the use of different label-free platforms for epitope mapping experiments.

PAIR-WISE EPITOPE MAPPINGThe design of a typical pair-wise mapping using mouse monoclonal antibodies (MAbs) is illustrated in Fig. 5.

To study mouse monoclonal antibodies it is convenient to first immobilize an anti-mouse-Fc antibody onto the sensor surface, in order to capture the monoclonals of interest. Next, a first MAb is injected for 1–2 min and cap-tured by the anti-mouse-Fc, after which a blocking anti-body with no relevance for the antigen is injected to block the remaining antibody-binding sites on the surface. Anti-gen is then injected followed by the injection of a second MAb. This injection cycle is terminated by the injection of a regeneration agent (typically a low pH solution such as 10 mM glycine at pH 1.8) that removes captured antibod-ies and antigen (not shown). In this way, a single surface can often be reused over 100 times. When a reference sur-face is used in an epitope mapping experiment, active and reference surfaces are often identical but antigen injection is not performed on the reference surface.

In contrast to other techniques where a response is obtained after the binding of the second antibody, SPR biosensors provide a detailed picture of all binding events. In Fig. 5a, the first antibody is captured to a relatively high level and antigen binding proceeds rapidly. Bound antigen dissociates slowly and the second MAb can bind to another

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FIGURE 5 Schematic illustrations of epitope mapping experiments. (a) MAb2 generates a distinct response, indicating a binding epitope separate from the epitope of MAb1. (b) A slow binding of antigen to MAb1 results in a small binding of MAb2, which is nevertheless relevant in comparison with antigen levels. The epitopes of these two MAbs, therefore, are likely to be separate from each other. (c) Because of the very rapid dissociation of MAb1, no conclusions regarding epitope specificity can be drawn from these data. (d) Although the binding response of MAb2 is low, it is relevant for the levels of antigen and MAb1 on the surface and it is likely that it recognizes a second epitope on the antigen.


epitope of the antigen. In Fig. 5b, the second antibody gives a very small response. By examining the sensorgram, this is clearly due to a very slow binding of antigen to the first MAb. In comparison to the level of antigen binding, the response from the second MAb is relevant and it can be concluded that the second antibody binds to another epit-ope on the antigen.

In Fig. 5c, the second antibody does not give any response. This can be explained by a very rapid dissocia-tion of antigen from the first MAb. This means that when the second antibody is injected, antigen is no longer pres-ent on the surface and therefore, no conclusions can be made regarding epitope specificity. In Fig. 5d, the low response from the second MAb is due to the low captured level of the first MAb to the sensor surface. This indicates a low concentration of this antibody in the culture media. Again, the response of the second MAb is relevant for the level of antigen on the surface and it is likely that it recog-nizes a second epitope on the antigen.

Clearly, the complete picture of all binding events makes it possible to improve the interpretation of the mapping experiment. It also makes it possible to troubleshoot the assay and to adapt experimental conditions if necessary. For example, the results in Fig. 5b could be clarified by injecting a higher concentration of antigen. Earlier injec-tion of the second MAb in Fig. 5c could make this data interpretable. The data in Fig. 5d could be improved by increasing the injection time or concentration of the first MAb.

Control experiments include injection of antigen directly on the anti-mouse-Fc antibody and over a surface with captured blocking antibody as well as injection of buf-fer instead of antigen in the mapping experiment.

As demonstrated by these examples, the mapping exper-iment provides data relevant to epitope specificity. At the same time it can give an approximate idea of the concen-tration of MAb in culture media and, more importantly, it provides data for ranking of complex stability. This is use-ful for identifying antibody pairs to be used in immuno-metric (sandwich) immunoassays.

Epitope mapping experiments using SPR biosensors often involve 5–50 antibodies, which are either purified or injected directly from cell culture media. Antigen is typi-cally used at concentrations from 10–100 µg/mL.

We will now look at examples of binding site analysis from three areas: 1) therapeutic antibodies, 2) antigen availability in virus-like particles, 3) characterization of antibodies against amyloid-β peptide involved in Alzheim-er’s disease.

THERAPEUTIC ANTIBODIESAlvarenga et al. (2012) used pair-wise epitope mapping to investigate binding of matuzumab, cetuximab, and panitumumab to soluble EGF-receptor. Matuzumab and cetuximab bound simultaneously to EGF-receptor with unchanged kinetics whereas matuzumab and panitumumab bound simultaneously to the receptor but with altered kinetics. Cetuximab and panitumumab were mutually exclusive and could not bind at the same time to the recep-tor. These results demonstrate that these antibodies have different epitope specificity and indicate that therapeutic

antibodies targeted at the EGF-receptor may act synergistically.

Stephan et al. (2011) discuss conjugation of therapeutic antibodies with cytotoxic agents for use in cancer therapy. They identify SPR and Biolayer Interferometry as tech-nologies capable of analyzing therapeutic antibody prop-erties and the availability of the cytotoxic agent in the conjugate. Acchione et al. (2012) used SPR to investigate the effect of conjugation chemistries on antigen and Fc-receptor binding. They identified one case where conjuga-tion affected Fc-Receptor binding, but the overall conclusion was that IgG1 antibody function was remark-ably insensitive to conjugation.

These papers demonstrate how SPR can be used to identify antibodies with separate, partly overlapping and completely overlapping epitopes, and determine the impact of conjugation on the functional binding sites of an antibody.

VIRUS-LIKE PARTICLESFleury et al. (2009) performed epitope mapping experi-ments with 15 monoclonal antibodies with the aim to char-acterize epitopes on papillomavirus type 31 major capsid protein. Using SPR they established an epitope map and identified antibodies that neutralized cell attachment, cell internalization, or virus-like particle binding to heparin.

In a series of papers Zhao et al. (2011, 2012) highlighted the use of SPR for comparing antigen availability on virus-like particles (VLPs) composed by Hepatitis B virus major surface antigen or papillomavirus capsid protein L1. These VLPs are used as vaccines. The authors used SPR to deter-mine antigenicity after heat- and redox-treatment (hepati-tis) and acid disassembly and reassembly (papilloma) of VLPs. These treatments improved antigenicity, and SPR measurements are now used in process development of the vaccines to improve vaccine constructs and also later for quality control. Mulder et al. (2012) discussed a toolbox for non-intrusive functional and structural analysis of recom-binant VLP-based vaccines and suggested the combined use of SPR and solution competitive ELISA.

These papers illustrate how SPR can be used in virus research to identify epitope-specific antibodies and how such antibodies can be used to follow changes in binding site availability in VLPs.

ALZHEIMER’S DISEASEIn Alzheimer’s disease, small and soluble amyloid-β pep-tide oligomers are suggested to exert the major pathologi-cal effects. Lindhagen-Persson et al. (2010) characterized neutralizing autoantibodies against amyloid-β peptide (NAbs–Aβ) and developed an IgM antibody, OMAB, that in cell assays fully inhibited the cytotoxic effect exerted by Aβ(1–42). SPR experiments showed that OMAB bound the N-terminus peptide Aβ(1–16) with an affinity of 0.5 µM but did not recognize Aβ(26–40). OMAB bound mono-mers of Aβ(1–40) but complexes were short-lived with t1/2 in the order of minutes whereas antibody complexes with oligomer peptide were stable for days.

Dodel et al. (2011) characterized another set of NAbs–Aβ. Here peptide ELISA identified the mid-/C-terminal


end of Aβ peptide as the recognition site and SPR again showed that antibodies preferentially bound oligomers but failed to bind monomers of the Aβ peptide. Animal experi-ments conducted in parallel suggested a physiological mechanism involving NAbs–Aβ to dispose of proteins or peptides that were prone to forming toxic aggregates.

The papers of Lindhagen-Persson et al. (2010) and Dodel et al. (2011) illustrate the use of SPR for identifica-tion of binding sites using truncated proteins and for the characterization of monomer oligomer binding.

Kinetic AnalysisFor a bimolecular interaction, the affinity constant reflects the equilibrium of the interaction and can be expressed in the following terms:

Here, KD is the equilibrium dissociation constant, A and B are the concentrations of the interactants and AB is the concentration of the complex. The kinetics on the other hand, provide information on the rates of the interaction and may be expressed in the following terms:

In this expression, t = time and ka and kd represent the asso-ciation and dissociation rate constants. At equilibrium, the following conditions apply:

,

Affinity and rate constants are operationally defined and depend on experimental conditions such as temperature, pH, and buffer composition. It is therefore important to state under what conditions affinity and rate constants have been determined. From the relationship between rate constants and the affinity constant, it is also clear that kinetic analysis has a greater resolving power than affinity analysis. The affinity constant is the ratio between the two rate constants and the affinity constant can therefore be obtained from the combination of an infinite number of rate constants.

When the kinetic experiment is repeated at different temperatures, reaction enthalpy and entropy can be deter-mined. The affinity constant provides information on changes in free energy between the free and bound reac-tion states, while kinetic analysis provides additional infor-mation related to the energy changes between the free state, the transition state and the bound state (Fig. 6).

While affinity and rate constants, as well as changes in enthalpy and entropy, are used to describe a single inter-action, it is frequently the comparison of interaction properties for related interactions that is of interest. This is the case in antibody selection, where binding properties for a set of antibodies reacting with the same antigen are compared, in mutagenesis studies where wild type and mutants are compared, and in drug discovery where the

interaction properties of drug candidates are investigated. Furthermore, two or more molecules that exhibit the same interaction properties at one set of interaction con-ditions may differ from each other when these conditions are changed.

When large sets of interaction pairs are compared, it is useful to display data in a plot of ka vs. kd values (Markgren et al., 2002). This is illustrated in Fig. 7a, where a set of molecules with unknown interaction properties is first classified into three groups corresponding to low, interme-diate, and high affinity. By kinetic analysis, a higher resolu-tion is obtained and the classification of these interactions becomes more detailed and informative. Figure 7b is a fur-ther clarification of the ka vs. kd plot and illustrates the extent to which interactions with identical affinity actually can differ in binding profile when kinetic data is included in the comparison. Clearly even high affinity antibodies can form very short-lived complexes.

SPR IN KINETIC ANALYSISThe fact that SPR measures mass changes at the sensor surface in real time makes it ideal for kinetic analysis. This type of universal detection makes it possible to apply almost the same experimental protocol to the study of interactions involving proteins, nucleic acids, and small molecules. Since the analyte is injected over the sensor surface under conditions of laminar flow, there is always a balance between the rate at which the analyte is trans-ported to the sensor surface by diffusion processes (mass transport) and the interaction-dependent binding rate at the sensor surface. The entire process can become trans-port limited and the observed binding rate may not reflect interaction properties when the transport rate is lower than the interaction-dependent binding rate. The binding rate at the sensor surface can be reduced by using low immobilization levels. For protein–protein interactions the immobilization levels should be adjusted so that the binding capacity (Rmax) falls between 10 and 100 RU. If this is the case, data is, except in very rare cases, not fully transport limited and it is possible to determine kinetic parameters.

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FIGURE 6 The affinity constant (KD) provides information on changes in free energy between the free and bound reaction states. Kinetic rate constants (ka and kd) give information related to energy changes between the free state, the transition state and the bound state.


Partially transport-limited data may still be observed and as a result the concentration of the analyte at the sen-sor surface will be depleted. Kinetics can still be deter-mined by introducing a mathematical formula by which the actual analyte concentration at the sensor surface is calculated. This leads to a set of rate equations that can be used in data analysis. For a reaction between analyte A and immobilized molecule B, therefore:

In these equations, “conc” is the concentration of the injected analyte and kt is a transport coefficient. To the right of each rate equation the starting values at time zero for each parameter are listed with the corresponding unit in parentheses. The analyte concentration at the sensor surface and the concentration of the complex are both zero, while the concentration of the immobilized ligand is expressed in terms of the saturation response.

In a kinetic experiment, the analyte is typically injected at two (minimum) to five concentrations. Injection times can be short (i.e., 1–2 min), whereas the dissociation phase, during which the complex dissociates under conditions of buffer flow, is normally in the range of 15–20 min. With the experimental design and data analysis model described here, it should be possible to determine rate constants in the following ranges:

ka = 103 – 108 M−1 s−1

kd = 10−4 – 10−1 s−1

These are basically the same ranges as illustrated in the ka vs. kd plots in Fig. 7. The ranges quoted are approximate, how-ever, and can be extended up to one order of magnitude in either direction. For the association rate constant, the upper level depends on the balance between transport and binding rates and the lower level on the possibility of injecting very

high concentrations of the analyte. Lower dissociation rate constants can be determined if the dissociation phase is pro-longed. Dissociation events that occur faster than the time it takes to switch from analyte to buffer flow (approximately 1 s at a high flow rate) are not possible to resolve.

In Fig. 8, four sets of kinetic analysis data are presented. Sets b and c reflect interactions for which rate constants can be readily obtained. Note that the top curves approach almost the same response level. This indicates that suffi-cient information regarding the saturation level has been obtained. It is a clear characteristic of these sets that sev-eral analyte injections generate binding profiles with dis-tinct curvature during the association phase. In both sets, dissociation profiles can readily be observed and these data therefore hold information on Rmax, ka, and kd, which are the parameters calculated using the evaluation software.

In set a, the sensorgrams are almost straight during injection, with little or no curvature even when binding approaches saturation level. This indicates an unfavorable balance between transport rates and binding rates. Here it is difficult to obtain reliable values for the rate constants, but it is still possible to determine the affinity from these binding curves (Karlsson, 1999). In set d, dissociation is very rapid. The sensorgrams mainly reflect steady state levels and no kinetic data can be obtained. In this case, the affinity constant can be derived from a plot of steady state values vs. analyte concentration.

By performing the kinetic experiment in single-cycle mode (Karlsson et al., 2005; Fig. 9) all analyte injections are performed in sequence without regeneration between injections. This approach has several advantages. Kinetic analysis can be performed even when regeneration condi-tions cannot be found, in capture experiments it reduces the amount of capture reagent used and overall analysis becomes more rapid.

In many cases binding events are not monophasic but appear more heterogeneous. Heterogeneity can be related to both ligand and analyte properties as illustrated in Fig. 10.

Heterogeneity can have different origins. The immobi-lized binding partner may be partially inactive or have

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FIGURE 7 Resolution of binding affinities using a ka vs. kd plot. The y-axis indicates increasingly rapid association rates and the x-axis indicates increasingly rapid dissociation rates, producing isometric affinity diagonals (indicated by red dashed lines). On the plots, affinity increases from bottom-right to top-left. (a) Affinity analysis alone may discriminate several groups of analytes comprising of multiple molecules with very similar, or identical KD values (shown schematically in the color-coded groups to the left of the plot). The ka vs. kd plot however reveals very distinct kinetic characteristics for analytes falling on to the same affinity diagonal. (b) Insets to show the variation in kinetic profiles that may be seen among analytes with identical affinities (The color version of this figure may be viewed at www.immunoassayhandbook.com).


several non-identical binding sites for its analyte. In the case of antibodies, as discussed by Biehl (2011) heteroge-neity may also be linked to how soon after immunization the antibody is obtained. Antibodies with biphasic behav-ior are more common the first two weeks after immuniza-tion whereas monophasic antibodies dominate at later stages.

Analytes with several binding sites can give rise to avid-ity effects. A common situation is when the two binding sites of an IgG antibody bind to separate ligand molecules. An antibody where both arms engage in binding typically dissociates much slower than when only one arm binds. Avidity effects can often be reduced or even eliminated when the maximum binding response is reduced to approx-imately 5 RU. This is presumably because immobilized antigen molecules are then too far apart and the antibody cannot bridge the distance between immobilized antigen molecules.

Heterogeneity that is linked to the immobilized binding partner can be more difficult to analyze. In such cases it may be a good idea to investigate the assumed homogene-ity of protein ligands with other techniques, such as ion-exchange chromatography or 2D-electrophoresis, to learn more about the nature of the protein preparation.

Kinetic analysis is one of the most widespread applica-tions of SPR technology but the data are sometimes per-ceived as complex and difficult to interpret. This is also a

matter of experience. Illustrative examples of good and perhaps more questionable examples are highlighted in the annual reviews of SPR papers prepared by Rich and Myszka (2010, 2011).

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FIGURE 9 Overlay plot of two sensorgrams obtained in single-cycle kinetics mode. In single-cycle kinetics analyte injections follow directly after each other and a longer dissociation period is used after the last injection of analyte. With this design, kinetic experiments can be performed even when regeneration conditions cannot be found. In capture experiments less ligand is required and overall experiments are faster to perform. Typically 3–5 injections of analyte are used. (The color version of this figure may be viewed at www.immunoassayhandbook.com)


Concentration AnalysisConcentration measurements, particularly in areas under the control of regulatory authorities, put high demands on the reproducibility and accuracy of the techniques used. Validation experiments have demonstrated that BiacoreTM concentration assays exhibit a high level of reproducibility with %CV values that are often below the corresponding ELISA assays (Shelver and Smith, 2003). More important is perhaps that SPR detection is direct and without the need for additional amplification steps. There are two prin-cipal assay formats for concentration analysis using SPR biosensors: direct binding assays and inhibition assays.

DIRECT BINDING ASSAYSIn a direct binding assay (Fig. 11), the sample is injected over the sensor surface and the analyte interacts with the immobilized biomolecule, which is often an antibody. The sensitivity in a direct binding assay depends on the level of

immobilized binding partner, on the interaction proper-ties (ka, kd) of the reagents and on the injection time of the sample. Using selected antibodies and injection times of a few minutes, it is possible to detect analyte present in buf-fer at concentrations that range from 10 pM to 10 µM. In more complex sample matrixes such as serum the corre-sponding range starts at approximately 100 pM as non-specific binding of serum components to the sensor surface impairs the sensitivity.

The shape of the calibration curve in a direct binding assay, and hence the operating range, can be affected and optimized by adjusting several different parameters. In contrast to fixed volume assays, where the concentration of analyte is reduced as the reaction proceeds, the reaction here takes place under flow conditions and the analyte concentration is constant throughout the injection. This means that equilibrium responses will be higher than in fixed volume assays.

Equilibrium is however seldom reached during injec-tion, and therefore the kinetic properties of the immobi-lized molecule for the analyte affect the position of the

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FIGURE 10 Heterogeneous data. (a) Biphasic behavior is easily identified by inspecting the dissociation phase. The initial dissociation rate is often rapid and then becomes slower and starts to deviate from an expected monophasic behavior. This indicates the presence of components with fast and slow dissociation. (b) Avidity effects can be identified by comparing sensorgrams obtained at different immobilization levels. At high immobilization levels, avidity effects dominate. At lower immobilization levels, dissociation is usually faster and binding becomes more monophasic.


calibration curve. By increasing the injection time, lower concentrations can be measured, and sensitivity can also be improved by injecting a second antibody to enhance the signal.

It is also possible to determine analyte concentration without the use of a standard. Calibration-free concen-tration analysis, CFCA, was first described by Karlsson et al. (1993). CFCA has since been improved and experi-ments are very easy to perform, as described by Pol (2010). The analyte of interest is injected at two flow rates (typi-cally at 10 and 100 µL/min, Fig. 12). If the binding rate increases as the flow rate is increased, the binding rate is partially or completely limited by transport effects. If this is the case, sensorgram data can be fitted using a variant of the kinetic model where the transport coefficient is pre-determined. This assay therefore requires knowledge of the molecular weight or preferably the diffusion coeffi-cient of the analyte. Calibration-free concentration analy-sis has a limited dynamic range, usually from 1 to 50 nM, and if the interaction has too low an association rate con-stant the interaction may never become partially trans-port-limited and in that case CFCA will not be possible.

In spite of these limitations, calibration-free concentra-tion analysis is particularly useful for testing the quality of expressed proteins and can be used for establishing an in-house standard.

If the analyte reacts with different immobilized ligands the integrity of the analyte can be tested. If all parts of the analyte are equally active the same active concentration should be determined with different immobilized ligands. This infor-mation is clearly relevant to analytes with several biological functions, for instance for antibodies that interact with anti-gen and a number of Fc receptors and complement factors.

The data in Figs. 11 and 12 are representative of analysis of samples in matrixes such as antibody culture media, in process development of antibodies, or whenever the sample matrix can be simplified by buffer exchange or dilution.

In serum or plasma, SPR is used in immunogenicity studies as already mentioned. But the use is more wide-spread. Kikuchi et al. (2005) used SPR to follow the con-centration of injected antibody in mouse plasma. Male SCID mice were administered intravenously with unla-beled ScFv. 20 µL blood samples were collected from the tail of the mouse and analyzed for ScFv using SPR. The assay was set up with a standard curve ranging from 0.01 to 1.28 µg/mL and samples were analyzed with an injection time of 1 min. Gill et al. (2012) used the p38α level in serum as a biomarker of head and neck squamous cell car-cinoma both for prognostic and for follow up after radiation therapy. They used immobilized anti-p38α anti-body and a calibration curve from 0.1 to 1.8 µg/mL. They found significantly elevated levels of p38α in cancer patients compared with levels in a control group. After radiotherapy, the p38α levels became lower and approached those in the control group. Trabucchi et al. (2012) mea-sured proinsulin autoantibodies (PAA) in sera from child-hood-onset and adult-onset diabetic patients and found differences between groups both in the concentration and affinity of PAA. Westdijk et al. (2011) characterized GMP batches of inactivated poliovirus vaccine. They immobi-lized specific antibody and used calibration-free concen-tration analysis for determination of the concentration of monovalent vaccines. CFCA results for Sabin IPV type 2 were considerably lower than concentrations determined by A260 values, possibly reflecting that all D-antigen on the virus particle was not active.

Finally there is a continued interest in combining SPR with the use of gold nanoparticles that are coated with spe-cific detection reagents. Kwon et al. (2012) report on the use of nanoparticles, with at least one dimension in the 40–50 nm range, that when functionalized with an anti-thrombin antibody can detect thrombin in an immunomet-ric (sandwich) assay format at attomolar concentrations.

INHIBITION ASSAYSThe inhibition assay format (Fig. 13) is often used for anal-ysis of low molecular weight analytes. In this assay format, the small molecule (or an analog or derivative thereof) is immobilized onto the sensor surface. A fixed amount of a detecting molecule (usually an antibody against the ana-lyte) is mixed with and allowed to interact with the sample prior to injection into the instrument. The pre-incubated sample with antibody is injected and the remaining free antibodies interact with the immobilized substance and generate a binding response. This means that the measured response is inversely related to the concentration of analyte in the samples. In an inhibition assay, the sensitivity is determined by the affinity of the interaction and, to obtain a high sensitivity, it is an advantage to use a low concentra-tion of the antibody. In practice the sensitivity of an inhibi-tion assay is often close to 1 nM. An additional advantage of the inhibition format is that surfaces with small molecules immobilized are usually very stable and hundreds of sam-ples can often be analyzed on the sensor surface.

Changes in contact time affect the calibration curve in a similar way as for a direct binding assay, i.e., a lon-ger contact time increases the response range. This, in combination with a lower concentration of detecting

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FIGURE 12 Calibration-free concentration analysis, CFCA, is performed by injecting analyte at two flow rates typically at 10 and 100 µL/min. Left data set: The binding rate increases with increasing flow rate. This curve set can be used for CFCA. Right data set: The binding rate is independent of flow rate variations. This curve set cannot be used in CFCA. (The color version of this figure may be viewed at www.immunoassayhandbook.com)


molecule, enables the measurement of lower analyte concentrations.

Inhibition assays have often been used for the analysis of low molecular weight analytes such as toxins (Campbell et al., 2011) and for nutritional additives in food samples (Indyk et al., 2006; Vyas and O’Kane, 2011). Here, typical sample matrixes include milk, jam, juice, honey, wheat, meat, liver, urine, and bile. Simplified sample preparation, multiplexing, automation, and flexibility in data analysis are considered advantages with SPR over technologies such as HPLC and ELISA.

Inhibition assays are also frequently used to confirm spec-ificity and have also been used for influenza virus quantifica-tion. In the latter application Nilsson et al. (2010) used immobilized hemagglutinins (HA) from H1N1, H3N2 and B strains. Each HA was immobilized to a separate flow cell. Virus in solution competed with HA on the surface for binding to solution-phase antibodies. Compared with the traditional single radio immune diffusion assay the SPR assay was characterized by higher sensitivity, higher preci-sion and significantly lower analysis and hands-on time.

Summary and AcknowledgmentsLabel-free technologies such as SPR are extremely valuable tools for the analysis of binding events. With a growing range of capture reagents, simplified methods such as single-cycle kinetics, and calibration-free concentration analysis, SPR technology is now easier to use and an investi-gator can focus on the specific research questions and spend less time on assay development procedures. SPR can pro-vide answers to fundamental questions: Do these molecules interact? What sites are involved? What are the kinetics and thermodynamics of the interaction? How much is there?

With a strong base in research, SPR is now also recog-nized as a screening technology, and diagnostic applica-tions are starting to emerge. Future applications may be around the corner. It all depends on what questions we ask and how we apply our technologies.

I would like to thank my previous co-authors Gary Franklin and Marie Arvola who helped to establish the structure of this chapter in the previous edition. Many

thanks to Ewa Pol and Åsa Frostell for allowing me to use their data in Figs. 10–12 and to Jan Hörling and Alexander Kele for their feedback on the content of this chapter.

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