Models of SIV rebound after treatment interruption that involve multiple reactivation events

Evaluation and Real-world Experience of a Neutralization Susceptibility Screening Assay for Broadly Neutralizing Anti-HIV-1 Antibodies

BACKGROUND

Development of a screening assay for the clinical use of broadly neutralizing antibodies (bnAbs) is a priority for HIV therapy and cure initiatives.

METHODS

We assessed the PhenoSense Monoclonal Antibody Assay (Labcorp-Monogram Biosciences), which is Clinical Laboratory Improvement Amendments (CLIA) validated and has been used prospectively and retrospectively in multiple recent bnAb clinical trials.

RESULTS

When performed on plasma and longitudinal peripheral blood mononuclear cell samples (before and during antiretroviral therapy, respectively), as sourced from a recent clinical trial, the PhenoSense assay produced robust reproducibility, concordance across sample types, and expected ranges in the susceptibility measures of bnAbs in clinical development. When applied retrospectively to baseline samples from 3 recent studies, the PhenoSense assay correlated with published laboratory-based study evaluations, but baseline bnAb susceptibility was not consistently predictive of durable virus suppression. Assessment of assay feasibility in 4 recent clinical studies provides estimates of assay success rate and processing time.

CONCLUSIONS

The PhenoSense Monoclonal Antibody Assay provides reproducible bnAb susceptibility measurements across relevant sample types yet is not consistently predictive of virus suppression. Logistical and operational assay requirements can affect timely clinical trial conduct. These results inform bnAb studies in development.

Assessing the impact of autologous virus neutralizing antibodies on viral rebound time in postnatally SHIV-infected ART-treated infant rhesus macaques

While the benefits of early antiretroviral therapy (ART) initiation in perinatally infected infants are well documented, early initiation is not always possible in postnatal pediatric HIV infections. The timing of ART initiation is likely to affect the size of the latent viral reservoir established, as well as the development of adaptive immune responses, such as the generation of neutralizing antibody responses against the virus. How these parameters impact the ability of infants to control viremia and the time to viral rebound after ART interruption is unclear and has never been modeled in infants. To investigate this question we used an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Infant Rhesus macaques (RMs) were orally challenged with SHIV.C.CH505 375H dCT and either given ART at 4-7 days post-infection (early ART condition), at 2 weeks post-infection (intermediate ART condition), or at 8 weeks post-infection (late ART condition). These infants were then monitored for up to 60 months post-infection with serial viral load and immune measurements. To gain insight into early after analytic treatment interruption (ATI), we constructed mathematical models to investigate the effect of time of ART initiation in delaying viral rebound when treatment is interrupted, focusing on the relative contributions of latent reservoir size and autologous virus neutralizing antibody responses. We developed a stochastic mathematical model to investigate the joint effect of latent reservoir size, the autologous neutralizing antibody potency, and CD4+ T cell levels on the time to viral rebound for RMs rebounding up to 60 days post-ATI. We find that the latent reservoir size is an important determinant in explaining time to viral rebound in infant macaques by affecting the growth rate of the virus. The presence of neutralizing antibodies can also delay rebound, but we find this effect for high potency antibody responses only. Finally, we discuss the therapeutic implications of our findings.

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.

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.

Viral rebound occurrence immediately after drug discontinuation involving neither drug resistance nor latent reservoir

Some viruses exhibit "rebound" when the administration of antiviral drugs is discontinued. Viral rebound caused by resistance mutations or latent reservoirs has been studied mathematically. In this study, we investigated the viral rebound due to other causes. Since immunity is weaker during antiviral treatment than without the treatment, drug discontinuation may lead to an increase in the viral load. We analyzed the dynamics of the number of virus-infected cells, cytotoxic T lymphocytes, and memory cells and identified the conditions under which the viral load increased upon drug discontinuation. If drug is administered for an extended period, a viral rebound occurs when the ratio of viral growth rate in the absence to that in the presence of the antiviral drug exceeds the "rebound threshold." We analyzed how the rebound threshold depended on the patient's conditions and the type of treatment. Mathematical and numerical analyses revealed that rebound after discontinuation was more likely to occur when the drug effectively reduced viral proliferation, drug discontinuation was delayed, and the processes activating immune responses directly were stronger than those occurring indirectly through immune memory formation. We discussed additional reasons for drugs to cause viral rebound more likely.

Unified model of short- and long-term HIV viral rebound for clinical trial planning

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS30, 343-353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials.