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

ABSTRACT: 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.

SUBMITTER: LaMont C

PROVIDER: S-EPMC9467514 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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

LaMont Colin C Otwinowski Jakub J Vanshylla Kanika K Gruell Henning H Klein Florian F Nourmohammad Armita A

eLife 20220719

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 an ...[more]

PMID: 35852143

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