Predicting HIV-1 transmission and antibody neutralization efficacy in vivo from stoichiometric parameters


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Date

2017-05-04

Publication Type

Journal Article

ETH Bibliography

yes

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Abstract

The potential of broadly neutralizing antibodies targeting the HIV-1 envelope trimer to prevent HIV-1 transmission has opened new avenues for therapies and vaccines. However, their implementation remains challenging and would profit from a deepened mechanistic understanding of HIV-antibody interactions and the mucosal transmission process. In this study we experimentally determined stoichiometric parameters of the HIV-1 trimer-antibody interaction, confirming that binding of one antibody is sufficient for trimer neutralization. This defines numerical requirements for HIV-1 virion neutralization and thereby enables mathematical modelling of in vitro and in vivo antibody neutralization efficacy. The model we developed accurately predicts antibody efficacy in animal passive immunization studies and provides estimates for protective mucosal antibody concentrations. Furthermore, we derive estimates of the probability for a single virion to start host infection and the risks of male-to-female HIV-1 transmission per sexual intercourse. Our work thereby delivers comprehensive quantitative insights into both the molecular principles governing HIV-antibody interactions and the initial steps of mucosal HIV-1 transmission. These insights, alongside the underlying, adaptable modelling framework presented here, will be valuable for supporting in silico pre-trial planning and post-hoc evaluation of HIV-1 vaccination or antibody treatment trials.

Publication status

published

Editor

Book title

Volume

13 (5)

Pages / Article No.

Publisher

PLOS

Event

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Organisational unit

09490 - Stadler, Tanja / Stadler, Tanja check_circle
03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian check_circle

Notes

Funding

149769 - Infering virus colonization pathways from high-throughput genetic data (SNF)

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