When Two are Better than One: Modeling the Mechanisms of Antibody Mixtures

TL;DR

This paper introduces a statistical mechanical model to predict the activity of antibody mixtures based on individual and pairwise interactions, enabling better design and understanding of antibody combinations.

Contribution

The study presents a novel model that predicts antibody mixture activity from pairwise interactions, without assuming synergy, and extends to engineered multidomain antibodies.

Findings

Accurately predicts antibody mixture activity from pairwise data.

Can infer molecular interactions from mixture activity measurements.

Generalizes to engineered multidomain antibodies and influenza antibodies.

Abstract

It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitopes. This model requires measuring $n$ individual antibodies and their $n(n-1)/2$ pairwise interactions to predict the $2^n$ potential combinations. We apply this model to epidermal growth factor receptor (EGFR) antibodies and find that the activity of antibody mixtures can be predicted without positing synergy at the molecular level. In addition, we demonstrate how the model can be used in reverse, where straightforward experiments measuring the activity of antibody mixtures can be used to infer the molecular interactions between antibodies. Lastly, we generalize this model to analyze engineered multidomain antibodies, where components of different antibodies are tethered together to form novel amalgams, and characterize how well it predicts recently designed influenza antibodies.