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Phan T, Conway JM, Pagane N, Kreig J, Sambaturu N, Iyaniwura S, Li JZ, Ribeiro RM, Ke R, Perelson AS. Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion. PLoS Pathog 2024; 20:e1012236. [PMID: 39074163 PMCID: PMC11309407 DOI: 10.1371/journal.ppat.1012236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 08/08/2024] [Accepted: 06/27/2024] [Indexed: 07/31/2024] Open
Abstract
Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jessica M. Conway
- Department of Mathematics, Pennsylvania State University, College Township, Pennsylvania, United States of America
- Department of Biology, Pennsylvania State University, College Township, Pennsylvania, United States of America
| | - Nicole Pagane
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America
- Ragon Institute of MGH, MIT, and Harvard; Cambridge, Massachusetts, United States of America
| | - Jasmine Kreig
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Narmada Sambaturu
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Sarafa Iyaniwura
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jonathan Z. Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alan S. Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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Phan T, Conway JM, Pagane N, Kreig J, Sambaturu N, Iyaniwura S, Li JZ, Ribeiro RM, Ke R, Perelson AS. Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592318. [PMID: 38746144 PMCID: PMC11092759 DOI: 10.1101/2024.05.03.592318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jessica M Conway
- Department of Mathematics, Pennsylvania State University, College Township, PA, USA
- Department of Biology, Pennsylvania State University, College Township, PA, USA
| | - Nicole Pagane
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard; Cambridge, MA, USA
| | - Jasmine Kreig
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Narmada Sambaturu
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Sarafa Iyaniwura
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jonathan Z Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
- Santa Fe Institute, Santa Fe, NM, USA
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Song Y, Wang R. Smoothed simulated pseudo-maximum likelihood estimation for nonlinear mixed effects models with censored responses. Stat Methods Med Res 2023; 32:1559-1575. [PMID: 37325816 PMCID: PMC10527368 DOI: 10.1177/09622802231181225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Nonlinear mixed effects models have been widely applied to analyses of data that arise from biological, agricultural, and environmental sciences. Estimation of and inference on parameters in nonlinear mixed effects models are often based on the specification of a likelihood function. Maximizing this likelihood function can be complicated by the specification of the random effects distribution, especially in the presence of multiple random effects. The implementation of nonlinear mixed effects models can be further complicated by left-censored responses, representing measurements from bioassays where the exact quantification below a certain threshold is not possible. Motivated by the need to characterize the nonlinear human immunodeficiency virus RNA viral load trajectories after the interruption of antiretroviral therapy, we propose a smoothed simulated pseudo-maximum likelihood estimation approach to fit nonlinear mixed effects models in the presence of left-censored observations. We establish the consistency and asymptotic normality of the resulting estimators. We develop testing procedures for the correlation among random effects and for testing the distributional assumptions on random effects against a specific alternative. In contrast to the existing variants of expectation-maximization approaches, the proposed methods offer flexibility in the specification of the random effects distribution and convenience in making inference about higher-order correlation parameters. We evaluate the finite-sample performance of the proposed methods through extensive simulation studies and illustrate them on a combined dataset from six AIDS Clinical Trials Group treatment interruption studies.
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Affiliation(s)
- Yue Song
- Department of Biostatistics, Harvard T. H. Chan School of Public Health,Boston, MA, 02115, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA
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Gao S, Wu L, Yu T, Kouyos R, Günthard HF, Wang R. Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring. STATISTICAL COMMUNICATIONS IN INFECTIOUS DISEASES 2022; 14:20210001. [PMID: 35880974 PMCID: PMC9204768 DOI: 10.1515/scid-2021-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 01/28/2022] [Accepted: 02/28/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. We investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. METHODS Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement three-step method. RESULTS We evaluate the performance of the proposed method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point). CONCLUSIONS The proposed three-step method works well. We have shown that key features of viral decay during ART may be associated with important features of viral rebound following ART interruption.
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Affiliation(s)
- Sihaoyu Gao
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Lang Wu
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Tingting Yu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
| | - Roger Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Huldrych F. Günthard
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Manandhar B, Zhang H. Random Change-Point Non-linear Mixed Effects Model for left-censored longitudinal data: An application to HIV surveillance. PROCEEDINGS. AMERICAN STATISTICAL ASSOCIATION. ANNUAL MEETING 2021; 2021:1320-1327. [PMID: 38855090 PMCID: PMC11162255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
A change-point model is essential in longitudinal data to infer an individual specific time to an event that induces a change of trend. However, in general, change points are not known for population-based data. We present an unknown change-point model that fits the linear and non-linear mixed effects for pre- and post-change points. We address the left-censored observations. Through stochastic approximation expectation maximization (SAEM) with the Metropolis Hasting sampler, we fit a random change-point non-linear mixed effects model. We apply our method on the longitudinal viral load (VL) data reported to the HIV surveillance registry from New York City.
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Affiliation(s)
- Binod Manandhar
- City University of New York, Graduate School of Public Health, 55 W 125th St,New York, NY 10027
| | - Hongbin Zhang
- City University of New York, Graduate School of Public Health, 55 W 125th St,New York, NY 10027
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Analytical Treatment Interruption in HIV Trials: Statistical and Study Design Considerations. Curr HIV/AIDS Rep 2021; 18:475-482. [PMID: 34213731 PMCID: PMC8251690 DOI: 10.1007/s11904-021-00569-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 12/17/2022]
Abstract
Purpose of Review Analytical treatment interruption (ATI) remains an essential component in clinical studies investigating novel agents or combination treatment strategies aiming to induce HIV treatment-free remission or long-term viral control. We provide an overview on key study design aspects of ATI trials from the perspective of statisticians. Recent Findings ATI trial designs have evolved towards shorter treatment interruption phases and more frequent viral load monitoring aiming to reduce prolonged viremia risks. Criteria for ART resumption have evolved as well. Common outcome measures in modern ATI trials include time to viral rebound, viral control, and viral set point. Summary Design of the ATI component in HIV clinical trials is driven by the scientific question and the mechanism of action of the intervention being investigated.
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Zhang H, Manandhar B. Stochastic Version of EM Algorithm for Nonlinear Random Change-Point Models. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON STATISTICS, THEORY AND APPLICATIONS (ICSTA ...) 2021; 2021:119. [PMID: 38919945 PMCID: PMC11198015 DOI: 10.11159/icsta21.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Random effect change-point models are commonly used to infer individual-specific time of event that induces trend change of longitudinal data. Linear models are often employed before and after the change point. However, in applications such as HIV studies, a mechanistic nonlinear model can be derived for the process based on the underlying data-generation mechanisms and such nonlinear model may provide better ``predictions". In this article, we propose a random change-point model in which we model the longitudinal data by segmented nonlinear mixed effect models. Inference wise, we propose a maximum likelihood solution where we use the Stochastic Expectation-Maximization (StEM) algorithm coupled with independent multivariate rejection sampling through Gibbs's sampler. We evaluate the method with simulations to gain insights.
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Affiliation(s)
- Hongbin Zhang
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, 55 West 125th Street, New York, United States
- Institute of Implementation Science for Population Health, City University of New York, 55 West 125th Street, New York, United States
| | - Binod Manandhar
- Institute of Implementation Science for Population Health, City University of New York, 55 West 125th Street, New York, United States
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Bing A, Hu Y, Prague M, Hill AL, Li JZ, Bosch RJ, De Gruttola V, Wang R. Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption. STATISTICAL COMMUNICATIONS IN INFECTIOUS DISEASES 2020; 12:20190021. [PMID: 34158910 PMCID: PMC8216669 DOI: 10.1515/scid-2019-0021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To compare empirical and mechanistic modeling approaches for describing HIV-1 RNA viral load trajectories after antiretroviral treatment interruption and for identifying factors that predict features of viral rebound process. METHODS We apply and compare two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. From each separate analysis, we identify predictors for viral set points and delay in rebound. Our empirical model postulates a parametric functional form whose parameters represent different features of the viral rebound process, such as rate of rise and viral load set point. The viral dynamics model augments standard HIV dynamics models-a class of mathematical models based on differential equations describing biological mechanisms-by including reactivation of latently infected cells and adaptive immune response. We use Monolix, which makes use of a Stochastic Approximation of the Expectation-Maximization algorithm, to fit non-linear mixed effects models incorporating observations that were below the assay limit of quantification. RESULTS Among the 346 participants, the median age at treatment interruption was 42. Ninety-three percent of participants were male and sixty-five percent, white non-Hispanic. Both models provided a reasonable fit to the data and can accommodate atypical viral load trajectories. The median set points obtained from two approaches were similar: 4.44 log10 copies/mL from the empirical model and 4.59 log10 copies/mL from the viral dynamics model. Both models revealed that higher nadir CD4 cell counts and ART initiation during acute/recent phase were associated with lower viral set points and identified receiving a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based pre-ATI regimen as a predictor for a delay in rebound. CONCLUSION Although based on different sets of assumptions, both models lead to similar conclusions regarding features of viral rebound process.
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Affiliation(s)
- Ante Bing
- Department of Mathematics and Statistics, Boston University, Boston, MA, 02215, USA
| | - Yuchen Hu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Melanie Prague
- University of Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
| | - Alison L Hill
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138
| | - Jonathan Z Li
- Brigham and Women's Hospital, Harvard Medical School, Boston MA 02215, USA
| | - Ronald J Bosch
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Victor De Gruttola
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
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