Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1
- aProgram for Evolutionary Dynamics, Department of Mathematics, and Department of Organismic and Evolutionary Biology, and
- bBiophysics Program and Harvard-MIT Division of Health Sciences and Technology, Harvard University, Cambridge, MA 02138;
- cDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032;
- dInstitute of Integrative Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland; and
- eDepartment of Medicine and
- fHoward Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
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Edited by John M. Coffin, Tufts University School of Medicine, Boston, MA, and approved July 9, 2014 (received for review April 12, 2014)

Significance
HIV infection cannot be cured by current antiretroviral drugs, due to the presence of long-lived latently infected cells. New antilatency drugs are being tested in clinical trials, but major unknowns remain. It is unclear how much latent virus must be eliminated for a cure, which remains difficult to answer empirically due to few case studies and limited sensitivity of viral reservoir assays. In this paper, we introduce a mathematical model of HIV dynamics to calculate the likelihood and timing of viral rebound following antilatency treatment. We derive predictions for the required efficacy of antilatency drugs, and demonstrate that rebound times may be highly variable and occur after years of remission. These results will aid in designing and interpreting HIV cure studies.
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.
Footnotes
↵1A.L.H. and D.I.S.R. contributed equally to this work.
- ↵2To whom correspondence should be addressed. Email: alhill{at}fas.harvard.edu.
Author contributions: A.L.H., D.I.S.R., M.A.N., and R.F.S. designed research; A.L.H., D.I.S.R., and F.F. performed research; A.L.H., D.I.S.R., and F.F. contributed new analytic tools; A.L.H. and D.I.S.R. analyzed data; and A.L.H., D.I.S.R., M.A.N., and R.F.S. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
See Commentary on page 13251.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1406663111/-/DCSupplemental.
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