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.

Assessing the impact of autologous neutralizing antibodies on rebound dynamics in postnatally SHIV-infected ART-treated infant rhesus macaques.

The presence of antibodies against HIV in infected children is associated with a greater capacity to control viremia in the absence of therapy. While the benefits of early antiretroviral treatment (ART) in infants are well documented, early ART may interfere with the development of antibody responses. In contrast to adults, early treated children lack detectable HIV-specific antibodies, suggesting a fundamental difference in HIV pathogenesis. Despite this potential adverse effect, early ART may decrease the size of the latent reservoir established early in infection in infants, which can be beneficial in viral control. Understanding the virologic and immunologic aspects of pediatric HIV is crucial to inform innovative targeted strategies for treating children living with HIV. In this study, we investigate how ART initiation time sets the stage for trade-offs in the latent reservoir establishment and the development of humoral immunity and how these, in turn, affect posttreatment dynamics. We also elucidate the biological function of antibodies in pediatric HIV. We employ mathematical modeling coupled with experimental data from an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Infant Rhesus macaques (RMs) were orally challenged with SHIV.C.CH505 375H dCT four weeks after birth and started treatment at different times after infection. In addition to viral load measurements, antibody responses and latent reservoir sizes were measured. We estimate model parameters by fitting viral load measurements to the standard HIV viral dynamics model within a nonlinear fixed effects framework. This approach allows us to capture differences between rhesus macaques (RMs) that develop antibody responses or exhibit high latent reservoir sizes compared to those that do not. We find that neutralizing antibody responses are associated with increased viral clearance and decreased viral infectivity but decreased death rate of infected cells. In addition, the presence of detectable latent reservoir is associated with less robust immune responses. These results demonstrate that both immune response and latent reservoir dynamics are needed to understand post-rebound dynamics and point to the necessity of a comprehensive approach in tailoring personalized medical interventions.

Comparative analysis of within-host dynamics of acute infection and viral rebound dynamics in postnatally SHIV-infected ART-treated infant rhesus macaques.

Viral dynamics of acute HIV infection and HIV rebound following suspension of antiretroviral therapy may be qualitatively similar but must differ given, for one, development of adaptive immune responses. Understanding the differences of acute HIV infection and viral rebound dynamics in pediatric populations may provide insights into the mechanisms of viral control with potential implications for vaccine design and the development of effective targeted therapeutics for infants and children. Mathematical models have been a crucial tool to elucidate the complex processes driving viral infections within the host. Traditionally, acute HIV infection has been modeled with a standard model of viral dynamics initially developed to explore viral decay during treatment, while viral rebound has necessitated extensions of that standard model to incorporate explicit immune responses. Previous efforts to fit these models to viral load data have underscored differences between the two infection stages, such as increased viral clearance rate and increased death rate of infected cells during rebound. However, these findings have been predicated on viral load measurements from disparate adult individuals. In this study, we aim to bridge this gap, in infants, by comparing the dynamics of acute infection and viral rebound within the same individuals by leveraging an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Ten infant Rhesus macaques (RMs) orally challenged with SHIV.C.CH505 375H dCT and given ART at 8 weeks post-infection. These infants were then monitored for up to 60 months post-infection with serial viral load and immune measurements. We use the HIV standard viral dynamics model fitted to viral load measurements in a nonlinear mixed effects framework. We find that the primary difference between acute infection and rebound is the increased death rate of infected cells during rebound. We use these findings to generate hypotheses on the effects of adaptive immune responses. We leverage these findings to formulate hypotheses to elucidate the observed results and provide arguments to support the notion that delayed viral rebound is characterized by a stronger CD8+ T cell response.

Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection.

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models-1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)-to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

Incorporating Intracellular Processes in Virus Dynamics Models.

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

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.

Assessing the impact of autologous neutralizing antibodies on viral rebound 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 ART initiation is not always possible in postnatal pediatric HIV infections, which account for the majority of pediatric HIV cases worldwide. The timing of onset 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. To gain insight into the dynamics, we utilized mathematical models to investigate the effect of time of ART initiation via latent reservoir size and autologous virus neutralizing antibody responses in delaying viral rebound when treatment is interrupted. We used an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model that mimics breast milk HIV transmission in human infants. 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. We develop 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 and control of post-rebound viral loads. We find that the latent reservoir size is an important determinant in explaining time to viral rebound by affecting the growth rate of the virus. The presence of neutralizing antibodies also can delay rebound, but we find this effect for high potency antibody responses only.

Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?

The antiviral remdesivir has been approved by regulatory bodies such as the European Medicines Agency (EMA) and the US Food and Drug administration (FDA) for the treatment of COVID-19. However, its efficacy is debated and toxicity concerns might limit the therapeutic range of this drug. Computational models that aid in balancing efficacy and toxicity would be of great help. Parametrizing models is difficult because the prodrug remdesivir is metabolized to its active form (RDV-TP) upon cell entry, which complicates dose-activity relationships. Here, we employ a computational model that allows drug efficacy predictions based on the binding affinity of RDV-TP for its target polymerase in SARS-CoV-2. We identify an optimal infusion rate to maximize remdesivir efficacy. We also assess drug efficacy in suppressing both wild-type and resistant strains, and thereby describe a drug regimen that may select for resistance. Our results differ from predictions using prodrug dose-response curves (pseudo-EC50s). We expect that reaching 90% inhibition (EC90) is insufficient to suppress SARS-CoV-2 in the lungs. While standard dosing mildly inhibits viral polymerase and therefore likely reduces morbidity, we also expect selection for resistant mutants for most realistic parameter ranges. To increase efficacy and safeguard against resistance, we recommend more clinical trials with dosing regimens that substantially increase the levels of RDV-TP and/or pair remdesivir with companion antivirals.

Viral Load Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospitalized Individuals With Coronavirus Disease 2019.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kinetics remain understudied, including the impact of remdesivir. In hospitalized individuals, peak sputum viral load occurred in week 2 of symptoms, whereas viremia peaked within 1 week of symptom-onset, suggesting early systemic seeding of SARS-CoV-2. Remdesivir treatment was associated with faster viral decay.

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.

Importance of COVID-19 vaccine efficacy in older age groups.

IMPORTANCE: An effective vaccine against SARS-CoV-2 will reduce morbidity and mortality and allow substantial relaxation of physical distancing policies. However, the ability of a vaccine to prevent infection or disease depends critically on protecting older individuals, who are at highest risk of severe disease. OBJECTIVE: We quantitatively estimated the relative benefits of COVID-19 vaccines, in terms of preventing infection and death, with a particular focus on effectiveness in elderly people. DESIGN: We applied compartmental mathematical modelling to determine the relative effects of vaccines that block infection and onward transmission, and those that prevent severe disease. We assumed that vaccines showing high efficacy in adults would be deployed, and examined the effects of lower vaccine efficacy among the elderly population. SETTING AND PARTICIPANTS: Our mathematical model was calibrated to simulate the course of an epidemic among the entire population of British Columbia, Canada. Within our model, the population was structured by age and levels of contact. MAIN OUTCOME(S) AND MEASURE(S): We assessed the effectiveness of possible vaccines in terms of the predicted number of infections within the entire population, and deaths among people aged 65 years and over. RESULTS: In order to reduce the overall rate of infections in the population, high rates of deployment to all age groups will be critical. However, to substantially reduce mortality among people aged 65 years and over, a vaccine must directly protect a high proportion of people in that group. CONCLUSIONS AND RELEVANCE: Effective vaccines deployed to a large fraction of the population are projected to substantially reduce infection in an otherwise susceptible population. However, even if transmission were blocked highly effectively by vaccination of children and younger adults, overall mortality would not be substantially reduced unless the vaccine is also directly protective in elderly people. We strongly recommend: (i) the inclusion of people aged 65 years and over in future trials of COVID-19 vaccine candidates; (ii) careful monitoring of vaccine efficacy in older age groups following vaccination.

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

In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate (95% credible interval: [18%, 62%]), and a 11% decrease of the viral growth rate (95% credible interval: [4%, 20%]). We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event.

Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Suspension of therapy is followed by rebound of viral loads to high, pre-therapy levels. However, there is significant heterogeneity in speed of rebound, with some rebounds occurring within days, weeks, or sometimes years. We present a stochastic mathematical model to gain insight into these post-treatment dynamics, specifically characterizing the dynamics of short term viral rebounds (≤ 60 days). Li et al. (2016) report that the size of the expressed HIV reservoir, i.e., cell-associated HIV RNA levels, and drug regimen correlate with the time between ART suspension and viral rebound to detectable levels. We incorporate this information and viral rebound times to parametrize our model. We then investigate insights offered by our model into the underlying dynamics of the latent reservoir. In particular, we refine previous estimates of viral recrudescence after ART interruption by accounting for heterogeneity in infection rebound dynamics, and determine a recrudescence rate of once every 2-4 days. Our parametrized model can be used to aid in design of clinical trials to study viral dynamics following analytic treatment interruption. We show how to derive informative personalized testing frequencies from our model and offer a proof-of-concept example. Our results represent first steps towards a model that can make predictions on a person living with HIV (PLWH)'s rebound time distribution based on biomarkers, and help identify PLWH with long viral rebound delays.

Defining early SIV replication and dissemination dynamics following vaginal transmission.

Understanding HIV transmission is critical to guide the development of prophylactic interventions to prevent infection. We used a nonhuman primate (NHP) model with a synthetic swarm of sequence-tagged variants of SIVmac239 ("SIVmac239X") and scheduled necropsy during primary infection (days 3 to 14 after challenge) to study viral dynamics and host responses to the establishment and dissemination of infection following vaginal challenge. We demonstrate that local replication was initiated at multiple sites within the female genital tract (FGT), with each site having multiple viral variants. Local replication and spread in the FGT preceded lymphatic dissemination. Innate viral restriction factors were observed but appeared to follow viral replication and were ineffective at blocking initial viral establishment and dissemination. However, major delays were observed in time to dissemination in animals and among different viral variants within the same animal. It will be important to assess how phenotypic differences affect early viral dynamics.

Determinants of the efficacy of HIV latency-reversing agents and implications for drug and treatment design.

HIV eradication studies have focused on developing latency-reversing agents (LRAs). However, it is not understood how the rate of latent reservoir reduction is affected by different steps in the process of latency reversal. Furthermore, as current LRAs are host-directed, LRA treatment is likely to be intermittent to avoid host toxicities. Few careful studies of the serial effects of pulsatile LRA treatment have yet been done. This lack of clarity makes it difficult to evaluate the efficacy of candidate LRAs or predict long-term treatment outcomes. We constructed a mathematical model that describes the dynamics of latently infected cells under LRA treatment. Model analysis showed that, in addition to increasing the immune recognition and clearance of infected cells, the duration of HIV antigen expression (i.e., the period of vulnerability) plays an important role in determining the efficacy of LRAs, especially if effective clearance is achieved. Patients may benefit from pulsatile LRA exposures compared with continuous LRA exposures if the period of vulnerability is long and the clearance rate is high, both in the presence and absence of an LRA. Overall, the model framework serves as a useful tool to evaluate the efficacy and the rational design of LRAs and combination strategies.

A New Age-Structured Multiscale Model of the Hepatitis C Virus Life-Cycle During Infection and Therapy With Direct-Acting Antiviral Agents.

The dynamics of hepatitis C virus (HCV) RNA during translation and replication within infected cells were added to a previous age-structured multiscale mathematical model of HCV infection and treatment. The model allows the study of the dynamics of HCV RNA inside infected cells as well as the release of virus from infected cells and the dynamics of subsequent new cell infections. The model was used to fit in vitro data and estimate parameters characterizing HCV replication. This is the first model to our knowledge to consider both positive and negative strands of HCV RNA with an age-structured multiscale modeling approach. Using this model we also studied the effects of direct-acting antiviral agents (DAAs) in blocking HCV RNA intracellular replication and the release of new virions and fit the model to in vivo data obtained from HCV-infected subjects under therapy.

Modeling the immune response to HIV infection.

The interplay between immune response and HIV is intensely studied via mathematical modeling, with significant insights but few direct answers. In this short review, we highlight advances and knowledge gaps across different aspects of immunity. In particular, we identify the innate immune response and its role in priming the adaptive response as ripe for modeling. The latter have been the focus of most modeling studies, but we also synthesize key outstanding questions regarding effector mechanisms of cellular immunity and development of broadly neutralizing antibodies. Thus far, most modeling studies aimed to infer general immune mechanisms; we foresee that significant progress will be made next by detailed quantitative fitting of models to data, and prediction of immune responses.

Early HIV infection predictions: role of viral replication errors.

In order to prevent and/or control infections it is necessary to understand their early-time dynamics. However this is precisely the phase of HIV about which the least is known. To investigate the initial stages of HIV infection within a host we have developed a multi-type, continuous-time branching process model. This model is a stochastic extension of the standard viral dynamics model, under the assumption that the number of cell targets for viral infection is constant, biologically reasonable since, during the earliest stages of HIV infection, very few cells are infected relative to their total population size. We use our model to investigate three important clinical characteristics of early HIV infection following intravenous challenge: risk of infection, time to infection clearance (assuming failed infection), and time to infection detection. Our focus is on the impact of errors in viral replication that result in non-infectious virus production on these characteristics. Only a small fraction of circulating virus in any chronically infected individual is capable of infecting susceptible cells: estimates range from 1/104 - 1/103. Characterization and quantification of the processes by which virus becomes defective remains incomplete. We consider two mechanisms that result in defective virus: (1) Copying errors, i.e., lethal errors in reverse transcription, which introduce mutations into the HIV-1 proviral genome, some of which may cripple the viral genome produced, and (2) Packaging errors, i.e., errors during viral packaging, at the end of the viral replication cycle, which cause defective virus by packaging new virions without, for example, viral RNA or key proteins required for infectivity. We show that assumptions on mechanisms of defective virus production can significantly impact early HIV infection model predictions. For example, the risk of infection is orders of magnitude higher if all defective virus is associated with packaging errors, but infection is predicted to be detectable sooner following HIV exposure if all defective virus is associated with copying errors. Thus, in order to make reliable predictions of risk, clearance time, and detection time, better characterization of viral replication is required.

On the duration of the period between exposure to HIV and detectable infection.

HIV infection cannot be detected immediately after exposure because plasma viral loads are too small initially. The duration of this phase of infection (the "eclipse period") is difficult to estimate because precise dates of exposure are rarely known. Therefore, the reliability of clinical HIV testing during the first few weeks of infection is unknown, creating anxiety among HIV-exposed individuals and their physicians. We address this by fitting stochastic models of early HIV infection to detailed viral load records for 78 plasma donors, taken during the period of exposure and infection. We first show that the classic stochastic birth-death model does not satisfactorily describe early infection. We therefore apply a different stochastic model that includes infected cells and virions separately. Since every plasma donor in our data eventually becomes infected, we must condition the model to reflect this bias, before fitting to the data. Applying our best estimates of unknown parameter values, we estimate the mean eclipse period to be 8-10 days. We further estimate the reliability of a negative test t days after potential exposure.

Residual Viremia in Treated HIV+ Individuals.

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. However, some residual virus remains, below the level of detection, in HIV-infected patients on ART. The source of this viremia is an area of debate: does it derive primarily from activation of infected cells in the latent reservoir, or from ongoing viral replication? Observations seem to be contradictory: there is evidence of short term evolution, implying that there must be ongoing viral replication, and viral strains should thus evolve. However, phylogenetic analyses, and rare emergent drug resistance, suggest no long-term viral evolution, implying that virus derived from activated latent cells must dominate. We use simple deterministic and stochastic models to gain insight into residual viremia dynamics in HIV-infected patients. Our modeling relies on two underlying assumptions for patients on suppressive ART: that latent cell activation drives viral dynamics and that the reproductive ratio of treated infection is less than 1. Nonetheless, the contribution of viral replication to residual viremia in patients on ART may be non-negligible. However, even if the portion of viremia attributable to viral replication is significant, our model predicts (1) that latent reservoir re-seeding remains negligible, and (2) some short-term viral evolution is permitted, but long-term evolution can still be limited: stochastic analysis of our model shows that de novo emergence of drug resistance is rare. Thus, our simple models reconcile the seemingly contradictory observations on residual viremia and, with relatively few parameters, recapitulates HIV viral dynamics observed in patients on suppressive therapy.