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Broadly neutralizing antibodies (bNAbs) are promising targets for vaccinationand therapy against HIV. Passive infusions of bNAbs have shown promise inclinical trials as a potential alternative for anti-retroviral therapy. A keychallenge for the potential clinical application of bnAbs is the suppression ofviral escape, which is more effectively achieved with a combination of bNAbs.However, identifying an optimal bNAb cocktail is combinatorially complex. Here,we propose a computational approach to predict the efficacy of a bNAb therapytrial based on the population genetics of HIV escape, which we parametrizeusing high-throughput HIV sequence data from a cohort of untreated bNAb-naivepatients. By quantifying the mutational target size and the fitness cost ofHIV-1 escape from bNAbs, we reliably predict the distribution of rebound timesin three clinical trials. Importantly, we show that early rebounds aredominated by the pre-treatment standing variation of HIV-1 populations, ratherthan spontaneous mutations during treatment. Lastly, we show that a cocktail ofthree bNAbs is necessary to suppress the chances of viral escape below 1%, andwe predict the optimal composition of such a bNAb cocktail. Our results offer arational design for bNAb therapy against HIV-1, and more generally show howgenetic data could be used to predict treatment outcomes and design newapproaches to pathogenic control.

Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1