When two are better than one: Modeling the mechanisms of antibody mixtures

Abstract

<jats:title>Abstract</jats:title><jats:p>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 <jats:italic>n</jats:italic> individual antibodies and their <jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="808394v2_inline1.gif" /></jats:alternatives></jats:inline-formula> pairwise interactions to predict the 2<jats:sup><jats:italic>n</jats:italic></jats:sup> 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.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>With the rise of new antibody combinations in therapeutic regimens, it is important to understand how antibodies work together as well as individually. Here, we investigate the specific case of monoclonal antibodies targeting a cancer-causing receptor or the influenza virus and develop a statistical mechanical framework that predicts the effectiveness of a mixture of antibodies. The power of this model lies in its ability to make a large number of predictions based on a limited amount of data. For example, once 10 antibodies have been individually characterized and their epitopes have been mapped, our model can predict how any of the 2<jats:sup>10</jats:sup> = 1024 combinations will behave. This predictive power can aid therapeutic efforts by assessing which combinations of antibodies will elicit the most effective response.</jats:p></jats:sec>

An antibody-escape calculator for mutations to the SARS-CoV-2 receptor-binding domain

“…Second, the calculator assumes the neutralizing activity of human polyclonal serum is represented by an equipotent mix of the monoclonal antibodies that happen to have been previously characterized by deep mutational scanning. Third, the calculator simply averages site-level escape measurements across antibodies, and does not yet implement a real biophysical model of the combined activity of multiple antibodies (Einav and Bloom 2020). Finally, and in our minds most significantly, the calculator estimates the impact of mutations in reference to antibodies targeted to the early Wuhan-Hu-1 RBD-an approach that is currently reasonable, but will become problematic as human exposure and vaccination histories diversify in the years to come (see last paragraph).…”

Using synthetic activity to design ultra-potent antibody cocktails

“…We report here the first use of quantitative analyses of drug synergy for preclinical and clinical development of a therapeutic antibody cocktail. These analyses build on a general framework for evaluating the effects of multiple drugs acting on a single target that was initially developed to model enzyme inhibition 55 . In that context it was shown that mutually exclusive inhibitors (i.e., competitive) have an additive inhibitory effect, whereas mutually nonexclusive inhibitors (i.e., independent) have a multiplicative (i.e., synergistic) effect.…”

“…In that context it was shown that mutually exclusive inhibitors (i.e., competitive) have an additive inhibitory effect, whereas mutually nonexclusive inhibitors (i.e., independent) have a multiplicative (i.e., synergistic) effect. This framework can be applied more generally, for example to antibody inhibitors of a protein 56 , and has the potential to inform decisions around drug cocktail formulation, including selection of lead candidates and optimization of their relative concentrations.…”

Shaping Polyclonal Responses via Antigen-Mediated Antibody Interference

“…And yet, polyclonal responses cannot be fully explained by monoclonal binding, at least in part due to two factors. First, Abs present in a mixture do not behave additively ( Zwick et al., 2001 ; Ndifon et al., 2009 ; Koefoed et al., 2011 ; Luo and Perelson, 2015 ; Derking et al., 2015 ; Howell et al., 2017 ; Caskey et al., 2019 ; Einav and Bloom, 2020 ; Greiff et al., 2012 ; Bachmann et al., 1997) . Second, seemingly passive Abs can influence ongoing selection of B cells during affinity maturation ( Ng et al., 2010 ; Zhang et al., 2013 ; Schoofs et al., 2016 ; Vono et al., 2019 ; Garg et al., 2019) .…”