Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption

Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion

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.

Vesicular MicroRNA as Potential Biomarkers of Viral Rebound

Changes in the cellular microRNA (miRNA) expression profile in response to HIV infection, replication or latency have been reported. Nevertheless, little is known concerning the abundance of miRNA in extracellular vesicles (EVs). In the search for a reliable predictor of viral rebound, we quantified the amount of miR-29a, miR-146a, and miR-155 in two types of plasma extracellular vesicles. Venous blood was collected from 235 ART-treated and ART-naive persons living with HIV (85 with ongoing viral replication, ≥20 copies/mL) and 60 HIV-negative participants at five HIV testing or treatment centers in Burkina Faso. Large and small plasma EVs were purified and counted, and mature miRNA miR-29a, miR-146a, and miR-155 were measured by RT-qPCR. Diagnostic performance of miRNA levels in large and small EVs was evaluated by a receiver operating characteristic curve analysis. The median duration of HIV infection was 36 months (IQR 14-117). The median duration of ART was 34 months (IQR 13-85). The virus was undetectable in 63.8% of these persons. In the others, viral load ranged from 108 to 33,978 copies/mL (median = 30,032). Large EVs were more abundant in viremic participants than aviremic. All three miRNAs were significantly more abundant in small EVs in persons with detectable HIV RNA, and their expression levels in copies per vesicle were a more reliable indicator of viral replication in ART-treated patients with low viremia (20-1000 copies/mL). HIV replication increased the production of large EVs more than small EVs. Combined with viral load measurement, quantifying EV-associated miRNA abundance relative to the number of vesicles provides a more reliable marker of the viral status. The expression level as copies per small vesicle could predict the viral rebound in ART-treated patients with undetectable viral loads.

Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring

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.

Analytical Treatment Interruption in HIV Trials: Statistical and Study Design Considerations

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.