Modeling HIV-1 Within-Host Dynamics After Passive Infusion of the Broadly Neutralizing Antibody VRC01

Mechanism for evolution of diverse autologous antibodies upon broadly neutralizing antibody therapy of people with HIV

Antiretroviral therapy (ART) inhibits Human Immunodeficiency Virus (HIV) replication to maintain undetectable viral loads in people living with HIV, but does not result in a cure. Due to the significant challenges of lifelong ART for many, there is strong interest in therapeutic strategies that result in cure. Recent clinical trials have shown that administration of broadly neutralizing antibodies (bnAbs) when there is some viremia can lead to ART-free viral control in some people; however, the underlying mechanisms are unclear. Our computational modeling shows that bnAbs administered in the presence of some viremia promote the evolution of autologous antibodies (aAbs) that target diverse epitopes of HIV spike proteins. This "net" of polyclonal aAbs could confer control since evasion of this response would require developing mutations in multiple epitopes. Our results provide a common mechanistic framework underlying recent clinical observations upon bnAb/ART therapy, and they should also motivate and inform new trials.

Advances in the mathematical modeling of posttreatment control of HIV-1

PURPOSE OF REVIEW

Several new intervention strategies have shown significant improvements over antiretroviral therapy (ART) in eliciting lasting posttreatment control (PTC) of HIV-1. Advances in mathematical modelling have offered mechanistic insights into PTC and the workings of these interventions. We review these advances.

RECENT FINDINGS

Broadly neutralizing antibody (bNAb)-based therapies have shown large increases over ART in the frequency and the duration of PTC elicited. Early viral dynamics models of PTC with ART have been advanced to elucidate the underlying mechanisms, including the role of CD8+ T cells. These models characterize PTC as an alternative set-point, with low viral load, and predict routes to achieving it. Large-scale omic datasets have offered new insights into viral and host factors associated with PTC. Correspondingly, new classes of models, including those using learning techniques, have helped exploit these datasets and deduce causal links underlying the associations. Models have also offered insights into therapies that either target the proviral reservoir, modulate immune responses, or both, assessing their translatability.

SUMMARY

Advances in mathematical modeling have helped better characterize PTC, elucidated and quantified mechanisms with which interventions elicit it, and informed translational efforts.

Trade-off between the antiviral and vaccinal effects of antibody therapy in the humoral response to HIV

Antibody therapy for HIV-1 infection exerts two broad effects: a drug-like, antiviral effect, which rapidly lowers the viral load, and a vaccinal effect, which may control the viral load long-term by improving the immune response. Here, we elucidate a trade-off between these two effects as they pertain to the humoral response, which may compromise antibody therapy aimed at eliciting long-term HIV-1 remission. We developed a multi-scale computational model that combined within-host viral dynamics and stochastic simulations of the germinal centre (GC) reaction, enabling simultaneous quantification of the antiviral and vaccinal effects of antibody therapy. The model predicted that increasing antibody dosage or antibody-antigen affinity increased immune complex formation and enhanced GC output. Beyond a point, however, a strong antiviral effect reduced antigen levels substantially, extinguishing GCs and limiting the humoral response. We found signatures of this trade-off in clinical studies. Accounting for the trade-off could be important in optimizing antibody therapy for HIV-1 remission.

Modelling HIV-1 control and remission

Remarkable advances are being made in developing interventions for eliciting long-term remission of HIV-1 infection. The success of these interventions will obviate the need for lifelong antiretroviral therapy, the current standard-of-care, and benefit the millions living today with HIV-1. Mathematical modelling has made significant contributions to these efforts. It has helped elucidate the possible mechanistic origins of natural and post-treatment control, deduced potential pathways of the loss of such control, quantified the effects of interventions, and developed frameworks for their rational optimization. Yet, several important questions remain, posing challenges to the translation of these promising interventions. Here, we survey the recent advances in the mathematical modelling of HIV-1 control and remission, highlight their contributions, and discuss potential avenues for future developments.

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

High monoclonal neutralization titers reduced breakthrough HIV-1 viral loads in the Antibody Mediated Prevention trials

The Antibody Mediated Prevention (AMP) trials (NCT02716675 and NCT02568215) demonstrated that passive administration of the broadly neutralizing monoclonal antibody VRC01 could prevent some HIV-1 acquisition events. Here, we use mathematical modeling in a post hoc analysis to demonstrate that VRC01 influenced viral loads in AMP participants who acquired HIV. Instantaneous inhibitory potential (IIP), which integrates VRC01 serum concentration and VRC01 sensitivity of acquired viruses in terms of both IC50 and IC80, follows a dose-response relationship with first positive viral load (p = 0.03), which is particularly strong above a threshold of IIP = 1.6 (r = -0.6, p = 2e-4). Mathematical modeling reveals that VRC01 activity predicted from in vitro IC80s and serum VRC01 concentrations overestimates in vivo neutralization by 600-fold (95% CI: 300-1200). The trained model projects that even if future therapeutic HIV trials of combination monoclonal antibodies do not always prevent acquisition, reductions in viremia and reservoir size could be expected.

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

Combined Nano-Vector Mediated-Transfer to Suppress HIV-1 Infection with Targeted Antibodies in-vitro

Introduction

Broadly neutralizing antibodies (bNAbs) have the ability to neutralize a considerable breadth of genetically diverse human immunodeficiency virus (HIV) strains. Passive immunization can potentially provide protection against HIV infection in animal models. However, the direct antibody infusion effect is limited due to the short half-life and deficient immunogenicity of the antibody. As an alternative strategy, we propose the use of nano viral vectors, specifically the adeno-associated virus (AAV), to continuously and systematically produce bNAbs against HIV.

Methods

Plasmids expressing bNAbs PG9, PG16, 10E8, and NIH45-46 antibodies were constructed, targeting three different epitopes of HIV. Additionally, the bNAbs gene mediated by rAAV8 was administered to generate long-term expression with a single injection. We established both single and combined immunization groups. The neutralizing activity of antibodies expressed in mice sera was subsequently evaluated.

Results

The expression of bNAbs in BALB/c mice can last for >24 weeks after a single intramuscular injection of rAAV8. Further studies show that neutralization of the HIV pseudovirus by sera from co-immunized mice with rAAV8 expressing 10E8 and PG16 was enhanced compared with mice immunized with 10E8 or PG16 alone.

Conclusion

The prolonged expression of neutralizing antibodies can be maintained over long periods in BALB/c mice. This combined immunization is a promising candidate strategy for HIV treatment.

Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1

Infusion of broadly neutralizing antibodies (bNAbs) has shown promise as an alternative to anti-retroviral therapy against HIV. A key challenge is to suppress viral escape, which is more effectively achieved with a combination of bNAbs. Here, we propose a computational approach to predict the efficacy of a bNAb therapy based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we predict the distribution of rebound times in three clinical trials. We show that a cocktail of three bNAbs is necessary to effectively suppress viral escape, and predict the optimal composition of such bNAb cocktail. Our results offer a rational therapy design for HIV, and show how genetic data can be used to predict treatment outcomes and design new approaches to pathogenic control.