![]() ![]() This information enables us to translate the molecular details of how each antibody acts individually into the macroscopic readout of a system’s activity in the presence of an arbitrary mixture. Specifically, each antibody is characterized by its binding affinity and potency, while its interaction with other antibodies is described by whether its epitope is distinct from or overlaps with theirs. Here, we develop a statistical mechanical model that bridges the gap between how an antibody operates on its own and how it behaves in concert. Often, the vast number of potential combinations is prohibitively large to systematically test, since both the composition of the mixture and the relative concentration of each component can influence its efficacy. However, it is difficult to predict how antibody mixtures will behave relative to their constitutive parts. There is ample precedent for the idea that combinations of therapeutics can be extremely powerful-for instance, during the past 50 years the monumental triumphs of combination anti-retroviral therapy and chemotherapy cocktails have provided unprecedented control over HIV and multiple types of cancer, and in many cases no single drug has emerged with comparable effects. While most therapies currently use monoclonal antibodies (mAbs), mounting evidence suggests that mixtures of antibodies can lead to better control through improved breadth, potency, and effector functions. Due to their clinical and commercial success, antibodies are one of the largest and fastest growing classes of therapeutic drugs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Īntibodies can bind with strong affinity and exquisite specificity to a multitude of antigens. JDB is an investigator of the Howard Hughes Medical Institute ( ). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and its Supporting Information files.įunding: This work was supported in part by the Mahan Fellowship ( ) from the Fred Hutchinson Cancer Center (TE) and the NIAID of the NIH (National Institute of Allergy and Infectious Diseases: ) through R01 AI127893 and R01 AI141707 (JBD). Received: OctoAccepted: MaPublished: May 4, 2020Ĭopyright: © 2020 Einav, Bloom. 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.Ĭitation: Einav T, Bloom JD (2020) When two are better than one: Modeling the mechanisms of antibody mixtures. ![]() 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. ![]() 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. This model requires measuring n individual antibodies and their pairwise interactions to predict the 2 n potential combinations. 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. It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. ![]()
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