Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption.
Ante Bing, Yuchen Hu, Melanie Prague, Alison L Hill, Jonathan Z Li, Ronald J Bosch, Victor De Gruttola, Rui Wang
Author Information
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.
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.
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.
