Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1
Colin LaMont,
Jakub Otwinowski,
Kanika Vanshylla,
Henning Gruell,
Florian Klein,
Armita Nourmohammad
Affiliations
Colin LaMont
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Jakub Otwinowski
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Kanika Vanshylla
Laboratory of Experimental Immunology, Institute of Virology Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
Laboratory of Experimental Immunology, Institute of Virology Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
Florian Klein
Laboratory of Experimental Immunology, Institute of Virology Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany; Department of Physics, University of Washington, Seattle, United States; Fred Hutchinson Cancer Research Center, Seattle, United States
Infusion of broadly neutralizing antibodies (bNAbs) has shown promise as an alternative to anti-retroviral therapy against HIV. A key challenge is to suppress viral escape, which is more effectively achieved with a combination of bNAbs. Here, we propose a computational approach to predict the efficacy of a bNAb therapy based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we predict the distribution of rebound times in three clinical trials. We show that a cocktail of three bNAbs is necessary to effectively suppress viral escape, and predict the optimal composition of such bNAb cocktail. Our results offer a rational therapy design for HIV, and show how genetic data can be used to predict treatment outcomes and design new approaches to pathogenic control.
