Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody

TL;DR

This study models HIV-1 escape from broadly neutralizing antibodies in vivo, revealing how dosage and viral load influence resistance development and predicting escape dynamics through a fitness landscape approach.

Contribution

It introduces a model that predicts HIV-1 escape dynamics based on in vivo fitness landscapes derived from clinical trial data, highlighting resistance-cost tradeoffs.

Findings

Identified antibody dosage and viral load as key fitness factors.

Successfully predicted HIV-1 escape dynamics during antibody treatment.

Revealed a dosage-dependent fitness ranking influencing resistance evolution.

Abstract

Broadly neutralizing antibodies are promising candidates for treatment and prevention of HIV-1 infections. Such antibodies can temporarily suppress viral load in infected individuals; however, the virus often rebounds by escape mutants that have evolved resistance. In this paper, we map an in vivo fitness landscape of HIV-1 interacting with broadly neutralizing antibodies, using data from a recent clinical trial. We identify two fitness factors, antibody dosage and viral load, that determine viral reproduction rates reproducibly across different hosts. The model successfully predicts the escape dynamics of HIV-1 in the course of an antibody treatment, including a characteristic frequency turnover between sensitive and resistant strains. This turnover is governed by a dosage-dependent fitness ranking, resulting from an evolutionary tradeoff between antibody resistance and its collateral cost in drug-free growth. Our analysis suggests resistance-cost tradeoff curves as a measure of antibody performance in the presence of resistance evolution.