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Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1

Journal Article · · PLoS Computational Biology (Online)
 [1];  [2];  [2];  [3]
  1. University of Leeds (United Kingdom)
  2. Beth Israel Deaconess Medical Center, Boston, MA (United States); MIT and Harvard, Cambridge, MA (United States). Ragon Institute of MGH
  3. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
PGT121 is a broadly neutralizing antibody in clinical development for the treatment and prevention of HIV-1 infection via passive administration. PGT121 targets the HIV-1 V3-glycan and demonstrated potent antiviral activity in a phase I clinical trial. Resistance to PGT121 monotherapy rapidly occurred in the majority of participants in this trial with the sampled rebound viruses being entirely resistant to PGT121 mediated neutralization. However, two individuals experienced long-term ART-free viral suppression following antibody infusion and retained sensitivity to PGT121 upon viral rebound. Here, we develop mathematical models of the HIV-1 dynamics during this phase I clinical trial. We utilize these models to understand the dynamics leading to PGT121 resistance and to identify the mechanisms driving the observed long-term viral control. Our modeling highlights the importance of the relative fitness difference between PGT121 sensitive and resistant subpopulations prior to treatment. Specifically, by fitting our models to data, we identify the treatment-induced competitive advantage of previously existing or newly generated resistant population as a primary driver of resistance. Finally, our modeling emphasizes the high neutralization ability of PGT121 in both participants who exhibited long-term viral control.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; National Institutes of Health (NIH)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2433941
Report Number(s):
LA-UR--23-30547
Journal Information:
PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 3 Vol. 20; ISSN 1553-7358
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (8)