Brief but Efficient: Acute HIV Infection and the Sexual Transmission of HIV

Background. We examined whether viral dynamics in the genital tract during the natural history of acute human immunodeficiency virus type 1 (HIV-1) infection could explain efficient heterosexual transmission of HIV. Methods. We measured HIV-1 concentration in blood and semen samples from patients with acute and long-term HIV-1 infection. We explored the effect of changes in viral dynamics in semen on the probability of transmission per coital act, using a probabilistic model published elsewhere. Results. Considered over time from infection, semen HIV-1 concentrations, in men with acute infection, increase and decrease in approximate parallel with changes occurring in blood. Modeling suggests that these acute dynamics alone are sufficient to increase probability of heterosexual transmission by 8-10-fold between peak (day 20 after infection, based on the model) and virologic set points (day 54 and later after infection). Depending on the frequency of coitus, men with average semen HIV-1 loads and without sexually transmitted diseases (STDs) would be expected to infect 7%-24% of susceptible female sex partners during the first 2 months of infection. The predicted infection rate would be much higher when either partner has an STD. Conclusions. Empirical biological data strongly support the hypothesis that sexual transmission by acutely infected individuals has a disproportionate effect on the spread of HIV-1 infection. Acute hyperinfectiousness may, in part, explain the current pandemic in heterosexual individual

항레트로바이러스 감염 역학에 관한 모델링연구.

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2018. 2. 신동우.A focus of this thesis is to develop a mathematical modeling approach to analyze the clinical data of Human immunodeficiency virus(HIV) acute infection. From the several studies, a remarkable stability of the HIV latent reservoir is detected despite the long-term treatment and advances in anti–retroviral therapy, and it has been recognized as a major barrier to HIV cure. We analyze several nonlinear mathematical models including the one that contains latent reservoir effect which provides consecutive viral replication and derive reproductive number (R0) which is a key index on HIV dynamics. For a quantitative analysis, we estimated parameters best describe time-series viral load measurements, obtained from published clinical study. We implement an efficient estimation method for the relevant parameters and numerical algorithm to solve the HIV infection dynamics. By using a nonlinear least square method for parameter estimation, analysis on the sensitivity parameters are performed for each model. In addition, we can obtain the total contribution of the reservoir processes to the productively infected T lymphocyte cells is also examined. We also propose a new model for HIV infection dynamics. There has been some researches that some influencing fractions on the dynamics of blood flow have been associated with the severity of HIV infection. In order to explain the rheological behavior of HIV infection in T lymphocyte populations we attempt to modify Latent cell model with fractional order differentiation of order α ∈ (0, 1]. The hemorheological parameters and fractional-order derivative in HIV system embody essential features of influencing fractions on the dynamics of blood flow associated with the severity of HIV infection. We show that the modified model has non-negative, bounded solutions and stable equilibrium points. Optimal fractional order and kinetic parameters are estimated by using the nonlinear weighted least-square method, the Levenberg-Marquardt algorithm, and Adams-type predictor-corrector method is employed for the numerical solution. The numerical results confirm that a value of fractional order (α) representing the rheological behavior in plasma is significantly related with a density of lymphocyte population.Chapter 1 Introduction 1 1.1 Infection mechanism of HIV and Antiretroviral treatment 2 1.2 Latent reservoir and drug-resistant mutant in HIV infection 6 1.3 Modeling HIV infection dynamics in lymphocyte 9 1.4 Thesis overview 12 Chapter 2 Mathematical models for HIV infection dynamics 14 2.1 Models and their analysis 14 2.1.1 Three-component model 16 2.1.2 Chronical infection model 39 2.1.3 Latent infection model 46 2.2 Parameter estimation 52 2.2.1 Description of measurement data and their clinical result 53 2.2.2 An algorithm for parameter estimation 56 2.2.3 Initial guess for initial state density and model parameters 60 2.2.4 Analysis of parameter sensitivity 61 2.3 Numerical result 64 2.3.1 Sensitivity equations 64 2.3.2 Numerical simulation 66 2.4 Conclusion 70 Chapter 3 A fractional-order model for HIV infection 77 3.1 The fractional calculus 78 3.2 Motivation 80 3.3 Model derivation 83 3.4 Numerical methods 93 3.4.1 The fractional Adams method 94 3.4.2 Sensitivity equations 99 3.4.3 Initial guess for paramters and the fractional order 102 3.5 Numerical results: model fits and sample predictions 102 3.6 Conclusion 107Docto

Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study

A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This marker can be used to assess the ARV potency of therapies, but is easily affected by drug exposures, drug resistance and other factors during the long-term treatment evaluation process. HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and ARV treatment strategies. However, the models of these studies are used to quantify short-term HIV dynamics ($<$ 1 month), and are not applicable to describe long-term virological response to ARV treatment due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. Long-term therapy with ARV agents in HIV-infected patients often results in failure to suppress the viral load. Pharmacokinetics (PK), drug resistance and imperfect adherence to prescribed antiviral drugs are important factors explaining the resurgence of virus. To better understand the factors responsible for the virological failure, this paper develops the mechanism-based nonlinear differential equation models for characterizing long-term viral dynamics with ARV therapy. The models directly incorporate drug concentration, adherence and drug susceptibility into a function of treatment efficacy and, hence, fully integrate virologic, PK, drug adherence and resistance from an AIDS clinical trial into the analysis. A Bayesian nonlinear mixed-effects modeling approach in conjunction with the rescaled version of dynamic differential equations is investigated to estimate dynamic parameters and make inference. In addition, the correlations of baseline factors with estimated dynamic parameters are explored and some biologically meaningful correlation results are presented. Further, the estimated dynamic parameters in patients with virologic success were compared to those in patients with virologic failure and significantly important findings were summarized. These results suggest that viral dynamic parameters may play an important role in understanding HIV pathogenesis, designing new treatment strategies for long-term care of AIDS patients.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS192 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Combination interventions for Hepatitis C and Cirrhosis reduction among people who inject drugs: An agent-based, networked population simulation experiment

Hepatitis C virus (HCV) infection is endemic in people who inject drugs (PWID), with prevalence estimates above 60 percent for PWID in the United States. Previous modeling studies suggest that direct acting antiviral (DAA) treatment can lower overall prevalence in this population, but treatment is often delayed until the onset of advanced liver disease (fibrosis stage 3 or later) due to cost. Lower cost interventions featuring syringe access (SA) and medically assisted treatment (MAT) for addiction are known to be less costly, but have shown mixed results in lowering HCV rates below current levels. Little is known about the potential synergistic effects of combining DAA and MAT treatment, and large-scale tests of combined interventions are rare. While simulation experiments can reveal likely long-term effects, most prior simulations have been performed on closed populations of model agents--a scenario quite different from the open, mobile populations known to most health agencies. This paper uses data from the Centers for Disease Control's National HIV Behavioral Surveillance project, IDU round 3, collected in New York City in 2012 by the New York City Department of Health and Mental Hygiene to parameterize simulations of open populations. Our results show that, in an open population, SA/MAT by itself has only small effects on HCV prevalence, while DAA treatment by itself can significantly lower both HCV and HCV-related advanced liver disease prevalence. More importantly, the simulation experiments suggest that cost effective synergistic combinations of the two strategies can dramatically reduce HCV incidence. We conclude that adopting SA/MAT implementations alongside DAA interventions can play a critical role in reducing the long-term consequences of ongoing infection

The Stochastic Dance of Early HIV Infection

The stochastic nature of early HIV infection is described in a series of models, each of which captures aspects of the dance of HIV during the early stages of infection. It is to this highly variable target that the immune response must respond. The adaptability of the various components of the immune response is an important aspect of the system\u27s operation, as the nature of the pathogens that the response will be required to respond to and the order in which those responses must be made cannot be known beforehand. As HIV infection has direct influence over cells responsible for the immune response, the dance predicts that the immune response will be also in a variable state of readiness and capability for this task of adaptation. The description of the stochastic dance of HIV here will use the tools of stochastic models, and for the most part, simulation. The justification for this approach is that the early stages and the development of HIV diversity require that the model to be able to describe both individual sample path and patient-to-patient variability. In addition, as early viral dynamics are best described using branching processes, the explosive growth of these models both predicts high variability and rapid response of HIV to changes in system parameters. In this paper, a basic viral growth model based on a time dependent continuous-time branching process is used to describe the growth of HIV infected cells in the macrophage and lymphocyte populations. Immigration from the reservoir population is added to the basic model to describe the incubation time distribution. This distribution is deduced directly from the modeling assumptions and the model of viral growth. A system of two branching processes, one in the infected macrophage population and one in the infected lymphocyte population is used to provide a description of the relationship between the development of HIV diversity as it relates to tropism (host cell preference). The role of the immune response to HIV and HIV infected cells is used to describe the movement of the infection from a few infected macrophages to a disease of infected CD4+ role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 14.4px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; CD4+ T lymphocytes

The utility of efavirenz-based prophylaxis against HIV infection. A systems pharmacological analysis

Pre-exposure prophylaxis (PrEP) is considered one of the five “pillars” by UNAIDS to reduce HIV transmission. Moreover, it is a tool for female self-protection against HIV, making it highly relevant to sub-Saharan regions, where women have the highest infection burden. To date, Truvada is the only medication for PrEP. However, the cost of Truvada limits its uptake in resource-constrained countries. Similarly, several currently investigated, patent-protected compounds may be unaffordable in these regions. We set out to explore the potential of the patent-expired antiviral efavirenz (EFV) as a cost-efficient PrEP alternative. A population pharmacokinetic model utilizing data from the ENCORE1 study was developed. The model was refined for metabolic autoinduction. We then explored EFV cellular uptake mechanisms, finding that it is largely determined by plasma protein binding. Next, we predicted the prophylactic efficacy of various EFV dosing schemes after exposure to HIV using a stochastic simulation framework. We predicted that plasma concentrations of 11, 36, 1287 and 1486ng/mL prevent 90% sexual transmissions with wild type and Y181C, K103N and G190S mutants, respectively. Trough concentrations achieved after 600 mg once daily dosing (median: 2017 ng/mL, 95% CI:445–9830) and after reduced dose (400 mg) efavirenz (median: 1349ng/mL, 95% CI: 297–6553) provided complete protection against wild-type virus and the Y181C mutant, and median trough concentrations provided about 90% protection against the K103N and G190S mutants. As reduced dose EFV has a lower toxicity profile, we predicted the reduction in HIV infection when 400 mg EFV-PrEP was poorly adhered to, when it was taken “on demand” and as post-exposure prophylaxis (PEP). Once daily EFV-PrEP provided 99% protection against wild-type virus, if ≥50% of doses were taken. PrEP “on demand” provided complete protection against wild-type virus and prevented ≥81% infections in the mutants. PEP could prevent >98% infection with susceptible virus when initiated within 24 h after virus exposure and continued for at least 9 days. We predict that 400 mg oral EFV may provide superior protection against wild-type HIV. However, further studies are warranted to evaluate EFV as a cost-efficient alternative to Truvada. Predicted prophylactic concentrations may guide release kinetics of EFV long-acting formulations for clinical trial design

A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org