title: Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1.
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.
authors.affiliation: 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.
authors.forename: Alison L
authors.initials: AL
authors.lastname: Hill
authors.affiliation: 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;
authors.forename: Daniel I S
authors.identifier: http://orcid.org/0000-0003-1413-3907
authors.initials: DI
authors.lastname: Rosenbloom
authors.affiliation: Institute of Integrative Biology, Eidgenössische Technische Hochschule Zürich, 8092 Zurich, Switzerland; and.
authors.forename: Feng
authors.initials: F
authors.lastname: Fu
authors.affiliation: Program for Evolutionary Dynamics, Department of Mathematics, and Department of Organismic and Evolutionary Biology, and.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Nowak
authors.affiliation: Department of Medicine and Howard Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.
authors.forename: Robert F
authors.initials: RF
authors.lastname: Siliciano
mesh_terms: D060740:Disease Eradication; D004197:Disease Reservoirs / Q000821:virology*; D015658:HIV Infections / Q000628:therapy* / Q000821:virology*; D015497:HIV-1 / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D013269:Stochastic Processes; D013997:Time Factors; D016896:Treatment Outcome; D035501:Uncertainty
keywords: HIV cure; HIV latent reservoir; viral dynamics
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