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 × 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.
Competing Interests: SB, FB, GV, PLS, CJB, GDG, DM and OF are (or were at the time of writing) employees of the GSK group of companies. AC and AM participated in a post graduate studentship program at GSK at the time of the study. GDG, DM and OF report ownership of GSK shares and/or restricted GSK shares as part of his/her employee remuneration at the time of writing.
(© 2021 The Authors.)