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 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 x 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 KD values spanning from similar to 10(-7) to similar 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. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.