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Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

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  • Brian D Williamson
  • Liana Wu
  • Yunda Huang
  • Aaron Hudson
  • Peter B Gilbert

Abstract

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory’s Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory 80% concentration

Suggested Citation

  • Brian D Williamson & Liana Wu & Yunda Huang & Aaron Hudson & Peter B Gilbert, 2024. "Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0310042
    DOI: 10.1371/journal.pone.0310042
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    References listed on IDEAS

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    1. Craig A Magaret & David C Benkeser & Brian D Williamson & Bhavesh R Borate & Lindsay N Carpp & Ivelin S Georgiev & Ian Setliff & Adam S Dingens & Noah Simon & Marco Carone & Christopher Simpkins & Dav, 2019. "Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-35, April.
    2. Anna Hake & Nico Pfeifer, 2017. "Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-23, October.
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    1. Craig A Magaret & David C Benkeser & Brian D Williamson & Bhavesh R Borate & Lindsay N Carpp & Ivelin S Georgiev & Ian Setliff & Adam S Dingens & Noah Simon & Marco Carone & Christopher Simpkins & Dav, 2019. "Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-35, April.

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