Insight into treatment of HIV infection from viral dynamics models

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.

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.

Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1

PGT121 is a broadly neutralizing antibody in clinical development for the treatment and prevention of HIV-1 infection via passive administration. PGT121 targets the HIV-1 V3-glycan and demonstrated potent antiviral activity in a phase I clinical trial. Resistance to PGT121 monotherapy rapidly occurred in the majority of participants in this trial with the sampled rebound viruses being entirely resistant to PGT121 mediated neutralization. However, two individuals experienced long-term ART-free viral suppression following antibody infusion and retained sensitivity to PGT121 upon viral rebound. Here, we develop mathematical models of the HIV-1 dynamics during this phase I clinical trial. We utilize these models to understand the dynamics leading to PGT121 resistance and to identify the mechanisms driving the observed long-term viral control. Our modeling highlights the importance of the relative fitness difference between PGT121 sensitive and resistant subpopulations prior to treatment. Specifically, by fitting our models to data, we identify the treatment-induced competitive advantage of previously existing or newly generated resistant population as a primary driver of resistance. Finally, our modeling emphasizes the high neutralization ability of PGT121 in both participants who exhibited long-term viral control.

Machine Learning Bolsters Evidence That D1, Nef, and Tat Influence HIV Reservoir Dynamics

Background

The primary hurdle to curing HIV is due to the establishment of a reservoir early in infection. In an effort to find new treatment strategies, we and others have focused on understanding the selection pressures exerted on the reservoir by studying how proviral sequences change over time.

Methods

To gain insights into the dynamics of the HIV reservoir we analyzed longitudinal near full-length sequences from 7 people living with HIV between 1 and 20 years following the initiation of antiretroviral treatment. We used this data to employ Bayesian mixed effects models to characterize the decay of the reservoir using single-phase and multiphasic decay models based on near full-length sequencing. In addition, we developed a machine-learning approach utilizing logistic regression to identify elements within the HIV genome most associated with proviral decay and persistence. By systematically analyzing proviruses that are deleted for a specific element, we gain insights into their role in reservoir contraction and expansion.

Results

Our analyses indicate that biphasic decay models of intact reservoir dynamics were better than single-phase models with a stronger statistical fit. Based on the biphasic decay pattern of the intact reservoir, we estimated the half-lives of the first and second phases of decay to be 18.2 (17.3 to 19.2, 95%CI) and 433 (227 to 6400, 95%CI) months, respectively.In contrast, the dynamics of defective proviruses differed favoring neither model definitively, with an estimated half-life of 87.3 (78.1 to 98.8, 95% CI) months during the first phase of the biphasic model. Machine-learning analysis of HIV genomes at the nucleotide level revealed that the presence of the splice donor site D1 was the principal genomic element associated with contraction. This role of D1 was then validated in an in vitro system. Using the same approach, we additionally found supporting evidence that HIV nef may confer a protective advantage for latently infected T cells while tat was associated with clonal expansion.

Conclusions

The nature of intact reservoir decay suggests that the long-lived HIV reservoir contains at least 2 distinct compartments. The first compartment decays faster than the second compartment. Our machine-learning analysis of HIV proviral sequences reveals specific genomic elements are associated with contraction while others are associated with persistence and expansion. Together, these opposing forces shape the reservoir over time.

Weight Gain After HIV Therapy Initiation: Pathophysiology and Implications

Rapid advances in the potency, safety, and availability of modern HIV antiretroviral therapy (ART) have yielded a near-normal life expectancy for most people living with HIV (PLWH). Ironically, considering the history of HIV/AIDS (initially called "slim disease" because of associated weight loss), the latest dilemma faced by many people starting HIV therapy is weight gain and obesity, particularly Black people, women, and those who commenced treatment with advanced immunodeficiency. We review the pathophysiology and implications of weight gain among PLWH on ART and discuss why this phenomenon was recognized only recently, despite the availability of effective therapy for nearly 30 years. We comprehensively explore the theories of the causes, from initial speculation that weight gain was simply a return to health for people recovering from wasting to comparative effects of newer regimens vs prior toxic agents, to direct effects of agents on mitochondrial function. We then discuss the implications of weight gain on modern ART, particularly concomitant effects on lipids, glucose metabolism, and inflammatory markers. Finally, we discuss intervention options for PLWH and obesity, from the limitations of switching ART regimens or specific agents within regimens, weight-gain mitigation strategies, and potential hope in access to emerging antiobesity agents, which are yet to be evaluated in this population.

Mathematical modeling predicts pathways to successful implementation of combination TRAIL-producing oncolytic virus and PAC-1 to treat granulosa cell tumors of the ovary

The development of new cancer therapies requires multiple rounds of validation from in vitro and in vivo experiments before they can be considered for clinical trials. Mathematical models assist in this preclinical phase by combining experimental data with human parameters to provide guidance about potential therapeutic regimens to bring forward into trials. However, granulosa cell tumors of the ovary lack a relevant mouse model, complexifying preclinical drug development for this rare tumor. To bridge this gap, we established a mathematical model as a framework to explore the potential of using a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-producing oncolytic vaccinia virus in combination with the chemotherapeutic agent first procaspase activating compound (PAC-1). We have previously shown that TRAIL and PAC-1 act synergistically on granulosa tumor cells. In line with our previous results, our current model predicts that, although it is unable to stop the tumor from growing in its current form, combination oral PAC-1 with oncolytic virus (OV) provides the best result compared to monotherapies. Encouragingly, our results suggest that increases to the OV infection rate can lead to the success of this combination therapy within a year. The model developed here can continue to be improved as more data become available, allowing for regimen-tailoring via virtual clinical trials, ultimately shepherding effective regimens into trials.

Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity

Objectives

Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning.

Methods

In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19+ patients.

Results

Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients.

Conclusion

Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.

A Continuation Technique for Maximum Likelihood Estimators in Biological Models

Estimating model parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while producing acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this technique to select between model fits with similar information criteria, a priori determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.

Mechanistic modeling of viral particle production

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Antiretroviral therapy adherence patterns, virological suppression, and emergence of drug resistance: A nested case–control study from Uganda and South Africa

Background

Relationships between distinct antiretroviral therapy (ART) adherence patterns and risk of drug resistance are not well understood.

Methods

We conducted a nested case–control analysis within a longitudinal cohort study of individuals initiating efavirenz-based ART. Primary outcomes of interest, measured at 6 and 12 months after treatment initiation, were: 1) virologic suppression, 2) virologic failure with resistance, and 3) virologic failure without resistance. Our primary exposure of interest was ART adherence, measured over the 6 months before each visit with electronic pill monitors, and categorized in three ways: 1) 6 months average adherence; 2) running adherence, defined as the proportion of days with average adherence over 9 days of less than or equal to 10%, 20%, and 30%; and 3) number of 3-, 7-, and 28-day treatment gaps in the prior 6 months

Results

We analyzed data from 166 individuals (107 had virologic failure during observation and 59 had virologic suppression at 6 and 12 months). Average adherence was higher among those with virologic suppression (median 83%, IQR 58–96%) versus those with virologic failure with resistance (median 35%, IQR 20–77%, pairwise P < 0.01) and those with virologic failure without resistance (median 21%, IQR 2–54%, pairwise P < 0.01). Although treatment gaps generally predicted virologic failure ( P < 0.01), they did not differentiate failure with and without drug resistance ( P > 0.6).

Conclusions

Average adherence patterns, but not the assessed frequency of treatment gaps, differentiated failure with versus without drug resistance among individuals initiating efavirenz-based ART. Future work should explore adherence-resistance relationships for integrase inhibitor-based regimens.

Healthy dynamics of CD4 T cells may drive HIV resurgence in perinatally-infected infants on antiretroviral therapy

In 2019 there were 490,000 children under five living with HIV. Understanding the dynamics of HIV suppression and rebound in this age group is crucial to optimizing treatment strategies and increasing the likelihood of infants achieving and sustaining viral suppression. Here we studied data from a cohort of 122 perinatally-infected infants who initiated antiretroviral treatment (ART) early after birth and were followed for up to four years. These data included longitudinal measurements of viral load (VL) and CD4 T cell numbers, together with information regarding treatment adherence. We previously showed that the dynamics of HIV decline in 53 of these infants who suppressed VL within one year were similar to those in adults. However, in extending our analysis to all 122 infants, we find that a deterministic model of HIV infection in adults cannot explain the full diversity in infant trajectories. We therefore adapt this model to include imperfect ART adherence and natural CD4 T cell decline and reconstitution processes in infants. We find that individual variation in both processes must be included to obtain the best fits. We also find that infants with faster rates of CD4 reconstitution on ART were more likely to experience resurgences in VL. Overall, our findings highlight the importance of combining mathematical modeling with clinical data to disentangle the role of natural immune processes and viral dynamics during HIV infection.

For infants infected with HIV at or near birth, early and continued treatment with antiretroviral therapy (ART) can lead to sustained suppression of virus and a healthy immune system. However many treated infants experience viral rebound and associated depletion of CD4 T cells. Mathematical models can successfully capture the dynamics of HIV infection in treated adults, but many of the assumptions encoded in these models do not apply early in life. Here we study data from a cohort of HIV-positive infants exhibiting diverse trajectories in response to ART. We show that wide-ranging outcomes can be explained by a modified, but still remarkably simple, model that includes both the natural dynamics of their developing immune systems and variation in treatment adherence. Strikingly, we show that infants with strong rates of recovery of CD4 T cells while on ART may be most at risk of virus resurgence.

Virus-Mimicking Polymer Nanoparticles Targeting CD169+ Macrophages as Long-Acting Nanocarriers for Combination Antiretrovirals

Monosialodihexosylganglioside (GM3)-presenting lipid-coated polymer nanoparticles (NPs) that recapitulate the sequestration of human immunodeficiency virus-1 (HIV-1) particles in CD169+ virus-containing compartments (VCCs) of macrophages were developed as carriers for delivery and sustained release of a combination of two antiretrovirals (ARVs), rilpivirine (RPV) and cabotegravir (CAB). RPV and CAB were co-loaded into GM3-presenting lipid-coated polylactic acid (PLA) and poly(lactic-co-glycolic acid) (PLGA) NPs without loss in potency of the drugs. GM3-presenting PLA NPs demonstrated the most favorable release properties and achieved inhibition of HIV-1 infection of primary human macrophages for up to 35 days. Intracellular localization of GM3-presenting PLA NPs in VCCs correlated with retention of intracellular ARV concentrations and sustained inhibition of HIV-1 infection. This work elucidates the design criteria of lipid-coated polymer NPs to utilize CD169+ macrophages as cellular drug depots for eradicating the viral reservoir sites or to achieve long-acting prophylaxis against HIV-1 infection.

Estimation of the in vivo neutralization potency of eCD4Ig and conditions for AAV-mediated production for SHIV long-term remission

The engineered protein eCD4Ig has emerged as a promising approach to achieve HIV remission in the absence of antiviral therapy. eCD4Ig neutralizes nearly all HIV-1 isolates and induces antibody-dependent cell-mediated cytotoxicity (ADCC) in vitro. To characterize the in vivo antiviral neutralization and possible ADCC effects of eCD4Ig, we fit mathematical models to eCD4Ig, anti–eCD4Ig-drug antibody (ADA), and viral load kinetics from healthy and simian-human immunodeficiency virus AD8 (SHIV-AD8) infected nonhuman primates that were treated with single or sequentially dosed eCD4Ig passive administrations. Our model predicts that eCD4Ig transiently decreases SHIV viral loads due to neutralization only with an in vivo IC₅₀ of ~25 μg/ml but with limited effect due to ADA. Simulations suggest that endogenous, continuous expression of eCD4Ig at levels greater than 10⁵ μg/day, as is possible with Adeno-associated virus (AAV) vector-based production, could overcome the diminishing effects of ADA and allow for long-term remission of SHIV viremia in nonhuman primates.

Why the HIV Reservoir Never Runs Dry: Clonal Expansion and the Characteristics of HIV-Infected Cells Challenge Strategies to Cure and Control HIV Infection

Antiretroviral therapy (ART) effectively reduces cycles of viral replication but does not target proviral populations in cells that persist for prolonged periods and that can undergo clonal expansion. Consequently, chronic human immunodeficiency virus (HIV) infection is sustained during ART by a reservoir of long-lived latently infected cells and their progeny. This proviral landscape undergoes change over time on ART. One of the forces driving change in the landscape is the clonal expansion of infected CD4 T cells, which presents a key obstacle to HIV eradication. Potential mechanisms of clonal expansion include general immune activation, antigenic stimulation, homeostatic proliferation, and provirus-driven clonal expansion, each of which likely contributes in varying, and largely unmeasured, amounts to maintaining the reservoir. The role of clinical events, such as infections or neoplasms, in driving these mechanisms remains uncertain, but characterizing these forces may shed light on approaches to effectively eradicate HIV. A limited number of individuals have been cured of HIV infection in the setting of bone marrow transplant; information from these and other studies may identify the means to eradicate or control the virus without ART. In this review, we describe the mechanisms of HIV-1 persistence and clonal expansion, along with the attempts to modify these factors as part of reservoir reduction and cure strategies.

Stability and bifurcation analysis of an HIV-1 infection model with a general incidence and CTL immune response

In this paper, with eclipse stage in consideration, we propose an HIV-1 infection model with a general incidence rate and CTL immune response. We first study the existence and local stability of equilibria, which is characterized by the basic infection reproduction number R0 and the basic immunity reproduction number R1. The local stability analysis indicates the occurrence of transcritical bifurcations of equilibria. We confirm the bifurcations at the disease-free equilibrium and the infected immune-free equilibrium with transmission rate and the decay rate of CTLs as bifurcation parameters, respectively. Then we apply the approach of Lyapunov functions to establish the global stability of the equilibria, which is determined by the two basic reproduction numbers. These theoretical results are supported with numerical simulations. Moreover, we also identify the high sensitivity parameters by carrying out the sensitivity analysis of the two basic reproduction numbers to the model parameters.

Th17 cell master transcription factor RORC2 regulates HIV-1 gene expression and viral outgrowth

HIV-1 infects CD4 T cells, and, among these, T helper 17 (Th17) cells are known to be particularly permissive for virus replication. The infection of Th17 cells is critical for AIDS pathogenesis and viral persistence. It is, however, not clear why these cells are highly permissive to HIV-1. We found that Th17 cell permissiveness depends on the expression of the hormone receptor RORC2, which is the master transcriptional regulator of Th17 cell differentiation. We identify RORC2 as a cell-specific host-dependency factor that can be targeted by small molecules. Our results suggest that RORC2 may be a cell-specific target to mitigate the loss of Th17 cells as a consequence of their preferential HIV-1 infection.

Among CD4⁺ T cells, T helper 17 (Th17) cells are particularly susceptible to HIV-1 infection and are depleted from mucosal sites, which causes damage to the gut barrier, resulting in a microbial translocation-induced systemic inflammation, a hallmark of disease progression. Furthermore, a proportion of latently infected Th17 cells persist long term in the gastrointestinal lymphatic tract where a low-level HIV-1 transcription is observed. This residual viremia contributes to chronic immune activation. Thus, Th17 cells are key players in HIV pathogenesis and viral persistence. It is, however, unclear why these cells are highly susceptible to HIV-1 infection. Th17 cell differentiation depends on the expression of the master transcriptional regulator RORC2, a retinoic acid-related nuclear hormone receptor that regulates specific transcriptional programs by binding to promoter/enhancer DNA. Here, we report that RORC2 is a key host cofactor for HIV replication in Th17 cells. We found that specific inhibitors that bind to the RORC2 ligand-binding domain reduced HIV replication in CD4⁺ T cells. The depletion of RORC2 inhibited HIV-1 infection, whereas its overexpression enhanced it. RORC2 was also found to promote HIV-1 gene expression by binding to the nuclear receptor responsive element in the HIV-1 long terminal repeats (LTR). In treated HIV-1 patients, RORC2⁺ CD4 T cells contained more proviral DNA than RORC2⁻ cells. Pharmacological inhibition of RORC2 potently reduced HIV-1 outgrowth in CD4⁺ T cells from antiretroviral-treated patients. Altogether, these results provide an explanation as to why Th17 cells are highly susceptible to HIV-1 infection and suggest that RORC2 may be a cell-specific target for HIV-1 therapy.

Iterative community-driven development of a SARS-CoV-2 tissue simulator

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Virological measures and factors associated with outcomes, and missing outcome data in HIV clinical trials: a methodological study

BACKGROUND

To evaluate the definition of HIV virological outcomes in the literature and factors associated with outcomes and missing outcome data.

METHODS

We conducted a methodological review of HIV RCTs using a search (2009-2019) of PubMed, Embase and the Cochrane Central Register of Controlled Trials.Only full-text, peer-reviewed, randomised controlled trials (RCTs) that measured virological outcomes in people living with HIV, and published in English were included.We extracted study details and outcomes. We used logistic regression to identify factors associated with a viral threshold ≤50 copies/mL and linear regression to identify factors associated with missing outcome data.

RESULTS

Our search yielded 5847 articles; 180 were included. A virological outcome was the primary outcome in 73.5% of studies. 89 studies (49.4%) used virological success. The remaining used change in viral load (VL) (33 studies, 18.3%); virological failure (59 studies, 32.8%); or virological rebound (9 studies, 5.0%). 96 studies (53.3%) set the threshold at ≤50 copies/mL; and 33.1% used multiple measures.Compared with government and privately funded studies, RCTs with industry funding (adjusted OR 6.39; 95% CI 2.15 to 19.00; p<0.01) were significantly associated with higher odds of using a VL threshold of ≤50 copies/mL. Publication year, intervention type, income level and number of patients were not associated with a threshold of ≤50 copies/mL. Trials with pharmacological interventions had less missing data (β=-11.04; 95% CI -20.02 to -1.87; p=0.02).

DISCUSSION

Country source of funding was associated with VL threshold choice and studies with pharmacological interventions had less missing data, which may in part explain heterogeneous virological outcomes across studies. Multiple measures of VL were not associated with missing data. The development of formal guidelines on virological outcome reporting in RCTs is needed.

Mathematical modeling of N-803 treatment in SIV-infected non-human primates

Immunomodulatory drugs could contribute to a functional cure for Human Immunodeficiency Virus (HIV). Interleukin-15 (IL-15) promotes expansion and activation of CD8⁺ T cell and natural killer (NK) cell populations. In one study, an IL-15 superagonist, N-803, suppressed Simian Immunodeficiency Virus (SIV) in non-human primates (NHPs) who had received prior SIV vaccination. However, viral suppression attenuated with continued N-803 treatment, partially returning after long treatment interruption. While there is evidence of concurrent drug tolerance, immune regulation, and viral escape, the relative contributions of these mechanisms to the observed viral dynamics have not been quantified. Here, we utilize mathematical models of N-803 treatment in SIV-infected macaques to estimate contributions of these three key mechanisms to treatment outcomes: 1) drug tolerance, 2) immune regulation, and 3) viral escape. We calibrated our model to viral and lymphocyte responses from the above-mentioned NHP study. Our models track CD8⁺ T cell and NK cell populations with N-803-dependent proliferation and activation, as well as viral dynamics in response to these immune cell populations. We compared mathematical models with different combinations of the three key mechanisms based on Akaike Information Criterion and important qualitative features of the NHP data. Two minimal models were capable of reproducing the observed SIV response to N-803. In both models, immune regulation strongly reduced cytotoxic cell activation to enable viral rebound. Either long-term drug tolerance or viral escape (or some combination thereof) could account for changes to viral dynamics across long breaks in N-803 treatment. Theoretical explorations with the models showed that less-frequent N-803 dosing and concurrent immune regulation blockade (e.g. PD-L1 inhibition) may improve N-803 efficacy. However, N-803 may need to be combined with other immune therapies to countermand viral escape from the CD8⁺ T cell response. Our mechanistic model will inform such therapy design and guide future studies.

Immune therapy may be a critical component in the functional cure for Human Immunodeficiency Virus (HIV). N-803 is an immunotherapeutic drug that activates antigen-specific CD8⁺ T cells of the immune system. These CD8⁺ T cells eliminate HIV-infected cells in order to limit the spread of infection in the body. In one study, N-803 reduced plasma viremia in macaques that were infected with Simian Immunodeficiency Virus, an analog of HIV. Here, we used mathematical models to analyze the data from this study to better understand the effects of N-803 therapy on the immune system. Our models indicated that inhibitory signals may be reversing the stimulatory effect of N-803. Results also suggested the possibilities that tolerance to N-803 could build up within the CD8⁺ T cells themselves and that the treatment may be selecting for virus strains that are not targeted by CD8⁺ T cells. Our models predict that N-803 therapy may be made more effective if the time between doses is increased or if inhibitory signals are blocked by an additional drug. Also, N-803 may need to be combined with other immune therapies to target virus that would otherwise evade CD8⁺ T cells.

Hybrid physiologically-based pharmacokinetic model for remdesivir: Application to SARS-CoV-2

A novel coronavirus, severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) or coronavirus disease 2019 (COVID‐19), has caused a pandemic that continues to cause catastrophic health and economic carnage and has escalated the identification and development of antiviral agents. Remdesivir (RDV), a prodrug and requires intracellular conversions to the active triphosphate nucleoside (TN) has surfaced as an active anti‐SARS‐CoV‐2 drug. To properly design therapeutic treatment regimens, it is imperative to determine if adequate intracellular TN concentrations are achieved in target tissues, such as the lungs. Because measurement of such concentrations is unrealistic in patients, a physiologically‐based pharmacokinetic (PBPK) model was developed to characterize RDV and TN disposition. Specifically, a hybrid PBPK model was developed based on previously reported data in humans. The model represented each tissue as a two‐compartment model—both extracellular and intracellular compartment wherein each intracellular compartment contained a comprehensive metabolic model to the ultimate active metabolite TN. Global sensitivity analyses and Monte‐Carlo simulations were conducted to assess which parameters and how highly sensitive ones impacted peripheral blood mononuclear cells and intracellular lung TN profiles. Finally, clinical multiple‐dose regimens indicated that minimum lung intracellular TN concentrations ranged from ~ 9 uM to 4 uM, which suggest current regimens are effective based on in vitro half‐maximal effective concentration values. The model can be used to explore tissue drug disposition under various conditions and regimens, and expanded to pharmacodynamic models.

SARS-CoV-2 viral dynamics in non-human primates

Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large burst size (>104 virus) and a within-host reproductive basic number of approximately 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly lost with a half-life of 9 hours, with no significant association between cytokine elevation and clearance, leading to a median time to viral clearance of 10 days, consistent with observations in mild human infections. Given these parameter estimates, we predict that a prophylactic treatment blocking 90% of viral production or viral infection could prevent viral growth. In conclusion, our results provide estimates of SARS-CoV-2 viral kinetic parameters in an experimental model of mild infection and they provide means to assess the efficacy of future antiviral treatments.

HIV Drug Resistance Mutations Detection by Next-Generation Sequencing during Antiretroviral Therapy Interruption in China

Patients with antiretroviral therapy interruption have a high risk of virological failure when re-initiating antiretroviral therapy (ART), especially those with HIV drug resistance. Next-generation sequencing may provide close scrutiny on their minority drug resistance variant. A cross-sectional study was conducted in patients with ART interruption in five regions in China in 2016. Through Sanger and next-generation sequencing in parallel, HIV drug resistance was genotyped on their plasma samples. Rates of HIV drug resistance were compared by the McNemar tests. In total, 174 patients were included in this study, with a median 12 (interquartile range (IQR), 6–24) months of ART interruption. Most (86.2%) of them had received efavirenz (EFV)/nevirapine (NVP)-based first-line therapy for a median 16 (IQR, 7–26) months before ART interruption. Sixty-one (35.1%) patients had CRF07_BC HIV-1 strains, 58 (33.3%) CRF08_BC and 35 (20.1%) CRF01_AE. Thirty-four (19.5%) of the 174 patients were detected to harbor HIV drug-resistant variants on Sanger sequencing. Thirty-six (20.7%), 37 (21.3%), 42 (24.1%), 79 (45.4%) and 139 (79.9) patients were identified to have HIV drug resistance by next-generation sequencing at 20% (v.s. Sanger, p = 0.317), 10% (v.s. Sanger, p = 0.180), 5% (v.s. Sanger, p = 0.011), 2% (v.s. Sanger, p < 0.001) and 1% (v.s. Sanger, p < 0.001) of detection thresholds, respectively. K65R was the most common minority mutation, of 95.1% (58/61) and 93.1% (54/58) in CRF07_BC and CRF08_BC, respectively, when compared with 5.7% (2/35) in CRF01_AE (p < 0.001). In 49 patients that followed-up a median 10 months later, HIV drug resistance mutations at >20% frequency such as K103N, M184VI and P225H still existed, but with decreased frequencies. The prevalence of HIV drug resistance in ART interruption was higher than 15% in the survey. Next-generation sequencing was able to detect more minority drug resistance variants than Sanger. There was a sharp increase in minority drug resistance variants when the detection threshold was below 5%.

Thresholds for post-rebound SHIV control after CCR5 gene-edited autologous hematopoietic cell transplantation

Autologous, CCR5 gene-edited hematopoietic stem and progenitor cell (HSPC) transplantation is a promising strategy for achieving HIV remission. However, only a fraction of HSPCs can be edited ex vivo to provide protection against infection. To project the thresholds of CCR5-edition necessary for HIV remission, we developed a mathematical model that recapitulates blood T cell reconstitution and plasma simian-HIV (SHIV) dynamics from SHIV-1157ipd3N4-infected pig-tailed macaques that underwent autologous transplantation with CCR5 gene editing. The model predicts that viral control can be obtained following analytical treatment interruption (ATI) when: (1) transplanted HSPCs are at least fivefold higher than residual endogenous HSPCs after total body irradiation and (2) the fraction of protected HSPCs in the transplant achieves a threshold (76–94%) sufficient to overcome transplantation-dependent loss of SHIV immunity. Under these conditions, if ATI is withheld until transplanted gene-modified cells engraft and reconstitute to a steady state, spontaneous viral control is projected to occur.

Modeling HIV multiple infection

Multiple infection of target cells by human immunodeficiency virus (HIV) may lead to viral escape from host immune responses and drug resistance to antiretroviral therapy, bringing more challenges to the control of infection. The mechanisms underlying HIV multiple infection and their relative contributions are not fully understood. In this paper, we develop and analyze a mathematical model that includes sequential cell-free virus infection (i.e.one virus is transmitted each time in a sequential infection of target cells by virus) and cell-to-cell transmission (i.e.multiple viral genomes are transmitted simultaneously from infected to uninfected cells). By comparing model prediction with the distribution data of proviral genomes in HIV-infected spleen cells, we find that multiple infection can be well explained when the two modes of viral transmission are both included. Numerical simulation using the parameter estimates from data fitting shows that the majority of T cell infections are attributed to cell-to-cell transmission and this transmission mode also accounts for more than half of cell's multiple infections. These results suggest that cell-to-cell transmission plays a critical role in forming HIV multiple infection and thus has important implications for HIV evolution and pathogenesis.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Impact of fluctuation in frequency of human immunodeficiency virus/simian immunodeficiency virus reactivation during antiretroviral therapy interruption

Antiretroviral therapy (ART) provides effective control of human immunodeficiency virus (HIV) replication and maintains viral loads of HIV at undetectable levels. Interruption of ART causes rapid recrudescence of HIV plasma viremia due to reactivation of latently HIV-infected cells. Here, we characterize the timing of both the initial and subsequent successful viral reactivations following ART interruption in macaques infected with simian immunodeficiency virus (SIV). We compare these to previous results from HIV-infected patients. We find that on average the time until the first successful viral reactivation event is longer than the time between subsequent reactivations. Based on this result, we hypothesize that the reactivation frequency of both HIV and SIV may fluctuate over time, and that this may impact the treatment of HIV. We develop a stochastic model incorporating fluctuations in the frequency of viral reactivation following ART interruption that shows behaviours consistent with the observed data. Furthermore, we show that one of the impacts of a fluctuating reactivation frequency would be to significantly reduce the efficacy of 'anti-latency' interventions for HIV that aim to reduce the frequency of reactivation. It is therefore essential to consider the possibility of a fluctuating reactivation frequency when assessing the impact of such intervention strategies.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections

It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.

A non-parametric analytic framework for within-host viral phylogenies and a test for HIV-1 founder multiplicity

Phylogenetics is a powerful tool for understanding the diversification dynamics of viral pathogens. Here we present an extension of the spectral density profile of the modified graph Laplacian, which facilitates the characterization of within-host molecular evolution of viruses and the direct comparison of diversification dynamics between hosts. This approach is non-parametric and therefore fast and model-free. We used simulations of within-host evolutionary scenarios to evaluate the efficiency of our approach and to demonstrate the significance of interpreting a viral phylogeny by its spectral density profile in terms of diversification dynamics. The key features that are captured by the profile are positive selection on the viral gene (or genome), temporal changes in substitution rates, mutational fitness, and time between sampling. Using sequences from individuals infected with HIV-1, we showed the utility of this approach for characterizing within-host diversification dynamics, for comparing dynamics between hosts, and for charting disease progression in infected individuals sampled over multiple years. We furthermore propose a heuristic test for assessing founder heterogeneity, which allows us to classify infections with single and multiple HIV-1 founder viruses. This non-parametric approach can be a valuable complement to existing parametric approaches.

Efficacy of the Post-Exposure Prophylaxis and of the HIV Latent Reservoir in HIV Infection

We propose a fractional order model to study the efficacy of the Post-Exposure Prophylaxis (PEP) in human immunodeficiency virus (HIV) within-host dynamics, in the presence of the HIV latent reservoir. Latent reservoirs harbor infected cells that contain a transcriptionally silent but reactivatable provirus. The latter constitutes a major difficulty to the eradication of HIV in infected patients. PEP is used as a way to prevent HIV infection after a recent possible exposure to HIV. It consists of the in-take of antiretroviral drugs for, usually, 28 days. In this study, we focus on the dosage and dosage intervals of antiretroviral therapy (ART) during PEP and in the role of the latent reservoir in HIV infected patients. We thus simulate the model for immunologically important parameters concerning the drugs and the fraction of latently infected cells. The results may add important information to clinical practice of HIV infected patients.

Mycobacterium avium complex infection in patients with human immunodeficiency virus: A systematic review and meta-analysis

BACKGROUND

Mycobacterium avium-intracellulare complex (MAC) is one of the leading causes of death among people living with human immunodeficiency virus (HIV). The current study was aimed to determine the frequency of MAC infection in patients infected with HIV.

METHODS

Embase, PubMed, and Web of Science were searched for relevant studies. All statistical analyses were performed by STATA version 14.

RESULTS

From 6,627 retrieved articles, 23 were included in the final analysis. A total of 18,463 patients with HIV were included in the analysis. The frequency of MAC infection in patients with HIV was found to be 10.6% (95% confidence interval, 6.9-14.2).

CONCLUSION

The relatively large fractions of HIV-infected patients were coinfected with MAC, which may poses significant public health problems. Continued progress in the development of rapid diagnostic methods and preventive therapy for MAC should lead to further improvements in survival and quality of life in patients with HIV.

Insight into treatment of HIV infection from viral dynamics models

The odds of living a long and healthy life with HIV infection have dramatically improved with the advent of combination antiretroviral therapy. Along with the early development and clinical trials of these drugs, and new field of research emerged called viral dynamics, which uses mathematical models to interpret and predict the time-course of viral levels during infection and how they are altered by treatment. In this review, we summarize the contributions that virus dynamics models have made to understanding the pathophysiology of infection and to designing effective therapies. This includes studies of the multiphasic decay of viral load when antiretroviral therapy is given, the evolution of drug resistance, the long-term persistence latently infected cells, and the rebound of viremia when drugs are stopped. We additionally discuss new work applying viral dynamics models to new classes of investigational treatment for HIV, including latency-reversing agents and immunotherapy.