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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.<\/jats:p>","DOI":"10.7554\/elife.76004","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T06:15:31Z","timestamp":1656915331000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.76004","source":"Crossref","is-referenced-by-count":40,"title":["Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1"],"prefix":"10.7554","volume":"11","author":[{"given":"Colin","family":"LaMont","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0087djs12","id-type":"ROR","asserted-by":"publisher"}],"name":"Max Planck Institute for Dynamics and Self-Organization","place":["G\u00f6ttingen, 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