Computational modeling of microfluidic data provides high-throughput affinity estimates for monoclonal antibodies

Abstract

Affinity measurement is a fundamental step in the discovery of monoclonal antibodies (mAbs) and of antigens suitable for vaccine development. Innovative affinity assays are needed due to the low throughput and/or limited dynamic range of available technologies. We combined microfluidic technology with quantum-mechanical scattering theory, in order to develop a high-throughput, broad-range methodology to measure affinity. Fluorescence intensity profiles were generated for out-of-equilibrium solutions of labelled mAbs and their antigen-binding fragments migrating along micro-columns with immobilized cognate antigen. Affinity quantification was performed by computational data analysis based on the Landau probability distribution. Experiments using a wide array of human or murine antibodies against bacterial or viral, protein or polysaccharide antigens, showed that all the antibody-antigen capture profiles (n = 841) generated at different concentrations were accurately described by the Landau distribution. A scale parameter W , proportional to the full-width-at-half-maximum of the capture profile, was shown to be independent of the antibody concentration. The W parameter correlated significantly (Pearson’s r [ p– value]: 0.89 [3 × 10 −8 ]) with the equilibrium dissociation constant K D , a gold-standard affinity measure. Our method showed good intermediate precision (median coefficient of variation: 5%) and a dynamic range corresponding to K D values spanning from ~10 −7 to ~10 −11 Molar. Relative to assays relying on antibody-antigen equilibrium in solution, even when they are microfluidic-based, the method’s turnaround times were decreased from 2 days to 2 h. The described computational modelling of antibody capture profiles represents a fast, reproducible, high-throughput methodology to accurately measure a broad range of antibody affinities in very low volumes of solution.

Anti-brolucizumab immune response as one prerequisite for rare retinal vasculitis/retinal vascular occlusion adverse events

“…The relative binding affinities of anti-brolucizumab polyclonal antibodies in the sera from healthy donors, HAWK and HARRIER trials, and clinical practice were estimated using the Gyrolab CD–based automated system designed for the detection of antibodies in serum samples. On the basis of the finding that the affinity score describing the shape of the fluorescence intensity profiles is linearly correlated with equilibrium dissociation constant ( K D ) measured by surface plasmon resonance (SPR), a mathematical model was established using monoclonal antibodies to estimate antibody relative binding affinity ( 8 ). Given the nature of ADAs, which are full IgGs (bivalent molecules), this analysis did not allow discrimination between affinity (pure 1:1 interaction) and avidity.…”

“…We applied an exploratory monoclonal antibody–based methodology that used a Gyrolab CD–based automated system designed for the detection of antibodies in serum samples ( 8 ). Almost all tested sera from treatment-naïve and brolucizumab-treated individuals with and without RV/RO showed IgG responses with high relative binding affinity to brolucizumab, supporting the notion of a fully mature immune response.…”

“…The mathematical model was originally established using monoclonal antibodies on the basis of the finding that the relative binding affinity score describing the shape of the fluorescence intensity profiles is linearly correlated with K D measured by SPR. On the basis of this correlation, scores below 6 are categorized as high relative binding affinity, scores between 6 and 8 as medium relative binding affinity, and scores above 8 as low relative binding affinity ( 8 ).…”

IgY Antibodies from Birds: A Review on Affinity and Avidity

“…The Fc portion of the antibody may also contribute to the ab-ag interaction [61]. This interaction and its duration are related to a set of non-covalent forces that are inversely proportional to distance, such as ionic forces, hydrogen bridges, hydrophobic forces, and van der Walls forces [62,63]. These forces are stronger when the distance between the elements is smaller.…”