ArticlePLoS computational biology2020
When two are better than one: Modeling the mechanisms of antibody mixtures.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing clinical dosing of combination broadly neutralizing antibodies for HIV prevention.PLoS computational biology · 2022Pooled it
- Comparative evaluation of screening approaches for antiviral monoclonal antibody combinations.Journal of virology · 2026Article
- Review
- Modelling the mechanisms of antibody mixtures in viral infections: the cases of sequential homologous and heterologous dengue infections.Journal of the Royal Society, Interface · 2024Article
- Pharmacodynamics and Bactericidal Activity of Combination Regimens in Pulmonary Tuberculosis: Application to Bedaquiline-Pretomanid-Pyrazinamide.Antimicrobial agents and chemotherapy · 2022Article
- A biophysical model of viral escape from polyclonal antibodies.Virus evolution · 2022Article
- An antibody-escape estimator for mutations to the SARS-CoV-2 receptor-binding domain.Virus evolution · 2022Article
- An antibody-escape calculator for mutations to the SARS-CoV-2 receptor-binding domain.bioRxiv : the preprint server for biology · 2021Article
- Article
- Quantitatively Visualizing Bipartite Datasets.Physical review. XArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
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 [Formula: see text] pairwise interactions to predict the 2n 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.