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Proc Natl Acad Sci U S A. 2014 Sep 16;111(37):13475-80. doi: 10.1073/pnas.1406663111. Epub 2014 Aug 5.

Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1.

Author information

1
Program for Evolutionary Dynamics, Department of Mathematics, and Department of Organismic and Evolutionary Biology, and Biophysics Program and Harvard-MIT Division of Health Sciences and Technology, Harvard University, Cambridge, MA 02138; alhill@fas.harvard.edu.
2
Program for Evolutionary Dynamics, Department of Mathematics, and Department of Organismic and Evolutionary Biology, and Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032;
3
Institute of Integrative Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland; and.
4
Program for Evolutionary Dynamics, Department of Mathematics, and Department of Organismic and Evolutionary Biology, and.
5
Department of Medicine and Howard Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.

Erratum in

  • Proc Natl Acad Sci U S A. 2014 Oct 28;111(43):15598.

Abstract

Massive research efforts are now underway to develop a cure for HIV infection, allowing patients to discontinue lifelong combination antiretroviral therapy (ART). New latency-reversing agents (LRAs) may be able to purge the persistent reservoir of latent virus in resting memory CD4(+) T cells, but the degree of reservoir reduction needed for cure remains unknown. Here we use a stochastic model of infection dynamics to estimate the efficacy of LRA needed to prevent viral rebound after ART interruption. We incorporate clinical data to estimate population-level parameter distributions and outcomes. Our findings suggest that ∼2,000-fold reductions are required to permit a majority of patients to interrupt ART for 1 y without rebound and that rebound may occur suddenly after multiple years. Greater than 10,000-fold reductions may be required to prevent rebound altogether. Our results predict large variation in rebound times following LRA therapy, which will complicate clinical management. This model provides benchmarks for moving LRAs from the laboratory to the clinic and can aid in the design and interpretation of clinical trials. These results also apply to other interventions to reduce the latent reservoir and can explain the observed return of viremia after months of apparent cure in recent bone marrow transplant recipients and an immediately-treated neonate.

KEYWORDS:

HIV cure; HIV latent reservoir; viral dynamics

Comment in

PMID:
25097264
PMCID:
PMC4169952
DOI:
10.1073/pnas.1406663111
[Indexed for MEDLINE]
Free PMC Article

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