  Broadly neutralizing antibodies (bNAbs) are promising targets for vaccination
and therapy against HIV. Passive infusions of bNAbs have shown promise in
clinical trials as a potential alternative for anti-retroviral therapy. A key
challenge for the potential clinical application of bnAbs is the suppression of
viral 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 therapy
trial based on the population genetics of HIV escape, which we parametrize
using high-throughput HIV sequence data from a cohort of untreated bNAb-naive
patients. By quantifying the mutational target size and the fitness cost of
HIV-1 escape from bNAbs, we reliably predict the distribution of rebound times
in three clinical trials. Importantly, we show that early rebounds are
dominated by the pre-treatment standing variation of HIV-1 populations, rather
than spontaneous mutations during treatment. Lastly, we show that a cocktail of
three bNAbs is necessary to suppress the chances of viral escape below 1%, and
we predict the optimal composition of such a bNAb cocktail. Our results offer a
rational design for bNAb therapy against HIV-1, and more generally show how
genetic data could be used to predict treatment outcomes and design new
approaches to pathogenic control.
