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

TL;DR

This paper develops a computational method to optimize combinations of broadly neutralizing antibodies for HIV-1 therapy, predicting viral rebound times and designing effective antibody cocktails based on HIV population genetics.

Contribution

It introduces a novel approach that uses high-throughput HIV sequence data to predict therapy efficacy and determine the optimal bNAb cocktail composition to prevent viral escape.

Findings

Early rebounds are mainly due to pre-treatment HIV variation.

A cocktail of three bNAbs reduces escape probability below 1%.

The method accurately predicts rebound times in clinical trials.

Abstract

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.