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

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 , proportional to the full-width-at-half-maximum of the capture profile, was shown to be independent of the antibody concentration. The parameter correlated significantly (Pearson's [value]: 0.89 [3 × 10]) with the equilibrium dissociation constant K, a gold-standard affinity measure. Our method showed good intermediate precision (median coefficient of variation: 5%) and a dynamic range corresponding to K values spanning from ~10 to ~10 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.