Determination of the composition of heterogeneous binder solutions by surface plasmon resonance biosensing

Abstract

Surface plasmon resonance-based biosensors have been extensively applied to the characterization of the binding kinetics between purified (bio)molecules, thanks to robust data analysis techniques. However, data analysis for solutions containing multiple interactants is still at its infancy. We here present two algorithms for (1) the reliable and accurate determination of the kinetic parameters of N interactants present at different ratios in N mixtures and (2) the estimation of the ratios of each interactant in a given mixture, assuming that their kinetic parameters are known. Both algorithms assume that the interactants compete to bind to an immobilized ligand in a 1:1 fashion and necessitate prior knowledge of the total concentration of all interactants combined. The effectiveness of these two algorithms was experimentally validated with a model system corresponding to mixtures of four small molecular weight drugs binding to an immobilized protein. This approach enables the in-depth characterization of mixtures using SPR, which may be of considerable interest for many drug discovery or development applications, notably for protein glycovariant analysis.

On the Use of Surface Plasmon Resonance Biosensing to Understand IgG-FcγR Interactions

“…Si Mehand et al [ 104 , 105 ] first demonstrated that it is possible to identify from the SPR recorded signal the kinetic parameters of two distinct analytes that are simultaneously injected over a given ligand. While the original intent of Si Mehand’s study was to increase the SPR biosensor throughput in a screening context, Gaudreault et al [ 106 ] recently extended this theoretical framework to the case of analytes. Here, the goal was to identify the kinetic parameters for each individual analyte within a complex analyte mixture, an asset if the analytes are difficult to separate from each other.…”

“…The model is described by the following ODE system, which can be integrated numerically: where represents the set of fractions of each analyte in the mixture. The basis of the fitting method used by Gaudreault et al [ 106 ] revolves around the fact that the model of Equation (7) exhibits the same behaviour at equilibrium as a 1:1 Langmuir model with the following parameters: …”

“…Interestingly, Gaudreault et al [ 106 ] also suggested a way to estimate the mixture composition (represented by the set of fractions of each analyte in the mixture), assuming that the kinetic parameters have been previously identified. This approach thus potentially paves the way to using SPR biosensing to determine the composition of IgG glycovariants mixtures if the glycovariants are characterized by distinct kinetics of interaction with their cognate receptors.…”

On the Use of Surface Plasmon Resonance-Based Biosensors for Advanced Bioprocess Monitoring

“…An inadequate fit to an ideal 1:1 Langmuir model can typically be explained either by a poor SPR assay design or by the presence of a more complex interaction scheme. Such complexity can hail from the presence of heterogeneity in the system (either in the analyte [26,[28][29][30][31] or the ligand [85][86][87][88] molecules), the stoichiometry of the analyte-ligand interaction [89][90][91][92], or a conformational change following analyte-ligand binding [93,94]. Forest-Nault et al [51] provided a description of the modeling approaches that can be used to analyze such complexities in SPR data and offered insight on the modeling of IgG-Fcγ receptor (FcγR) interactions, which has long been studied using SPR and for which nonideal behaviors have repeatedly been observed and confirmed.…”

“…Advances in the liquid handling and control systems have greatly improved the precision and sensitivity of SPR-based biosensors in the last three decades [15][16][17][18][19][20][21]. On top of this, robust protocols [22] and data analysis works [23][24][25][26][27][28][29][30][31] have helped establish SPR as a premiere technique in the field of biomolecule interaction analyses. Its main advantage lies in its ability to detect interactions without a label, which simplifies the assay design.…”