Immune repertoire mining for rapid affinity optimization of mouse monoclonal antibodies

Traditional hybridoma and B cell cloning antibody discovery platforms have inherent limits in immune repertoire sampling depth. One consequence is that monoclonal antibody (mAb) leads often lack the necessary affinity for therapeutic applications, thus requiring labor-intensive and time-consuming affinity in vitro engineering optimization steps. Here, we show that high-affinity variants of mouse-derived mAbs can be rapidly obtained by testing of somatic sequence variants obtained by deep sequencing of antibody variable regions in immune repertories from immunized mice, even with a relatively sparse sampling of sequence variants from large sequence datasets. Affinity improvements can be achieved for mAbs with a wide range of affinities. The optimized antibody variants derived from immune repertoire mining have no detectable in vitro off-target binding and have in vivo clearance comparable to the parental mAbs, essential properties in therapeutic antibody leads. As generation of antibody variants in vitro is replaced by mining of variants generated in vivo, the procedure can be applied to rapidly identify affinity-optimized mAb variants.