Modeling Viral and T Lymphocyte Dynamics

总结 最近的多项研究提供了概念证明，即HIV-1感染的“功能性治愈”， 即长期控制艾滋病毒而不继续治疗是可以实现的。VISCONTI研究 确定了14名HIV+患者，他们在HIV-1初期接受了抗逆转录病毒治疗（ART）。 感染，并保持其病毒的治疗后控制（PTC）低于检测限 停止治疗后的中位时间为89个月。CHAMP（抗逆转录病毒治疗后的艾滋病毒控制） 药物治疗）研究已经从14个治疗组中确定了67个治疗后控制者。 研究涉及700多名参与者。 确定为什么有些人在治疗后将艾滋病毒控制在无法检测或低水平 停药后，我们需要更好地了解导致病毒感染的因素 水库，这决定了它的大小，其维护和可能的重新激活的动力学 导致治疗停止后病毒反弹。在这里，我们建议开发一套新的 模型，以帮助理解导致上述研究中功能性治愈的因素 并且更一般地理解如何可以实现功能性固化。我们将 与领先的实验科学家合作，他们将提供新的数据集，使我们能够 实现以下具体目标。目标1.了解HIV潜伏库的机制 决定水库急性期播种率的因素 感染我们将开发早期油藏形成的机理模型。我们将测试这些 根据合作者收集的丰富数据集建立模型，并估计关键参数值， 准确描述油藏形成的动态。有了这些见解，我们将 扩展模型，以解释最近的播种、周转和遗传组成数据39 的水库。目标二。为了定量地详细了解决定 ART中断后治疗后控制的持续时间。我们将研究新的模式， 解释患者特异性因素，如ART方案和细胞相关RNA水平 在预测ATI后病毒反弹的时间。当反弹时间较长时， 我们的目标是阐明新的因素，如时间依赖的水库重新激活率或免疫 导致PTC延长的控制。目标3：利用从数学建模中获得的见解 来推动HBV的治疗进程。我们将利用以前的艾滋病毒建模成功， 建立新一代的HBV感染模型，并研究HCV感染对HBV感染的影响。 不同的疗法单独和组合。

SUMMARY Multiple recent studies have provided proof-of-concept that a “functional cure” of HIV-1 infection, i.e. long-term control of HIV without continued treatment, is achievable. The VISCONTI study identified 14 HIV+ patients, who received antiretroviral treatment (ART) during primary HIV-1 infection, and maintained post-treatment control (PTC) of their virus below the limit of detection for a median of 89 months after stopping therapy. The CHAMP (Control of HIV after Antiretroviral Medication Pause) study has identified 67 post-treatment controllers from 14 treatment interruption studies involving more than 700 participants. To determine why some individuals control HIV to undetectable or low levels after treatment discontinuation, we need a better understanding of the factors that lead to establishment of viral reservoirs, that determine its size, the dynamics of its maintenance and its reactivation possibly leading to viral rebound after treatment cessation. Here we propose to develop a set of new models to help understand the factors that led to functional cure in the studies mentioned above and to understand more generally how functional cure can be achieved. We will collaborate with leading experimental scientists, who will provide novel datasets allowing us to fulfil the following specific aims. Aim 1. To understand the mechanism of HIV latent reservoir establishment and the factors determining the rate of reservoir seeding during acute infection. We will develop mechanistic models of early reservoir establishment. We will test these models against rich datasets collected by collaborators and estimate key parameter values to accurately describe the dynamics of reservoir establishment. With the insights gained, we will extend the model to interpret recent data39 on the seeding, turnover and the genetic composition of the reservoir. Aim 2. To understand in quantitative detail the factors that determine the duration of post-treatment control after ART interruption. We will study new models that account for patient specific factors such as the ART regime and the level of cell-associated RNA at the time of ATI in predicting time to viral rebound after ATI. When the time to rebound is long, we aim to elucidate new factors such as a time-dependent rate of reservoir reactivation or immune control that lead to prolonged PTC. Aim 3. Using insights gained from mathematical modeling to propel the cure agenda for HBV. We will leverage previous modeling successes of HIV and HCV infection to develop a new generation of models of HBV infection and study the effects of different therapies singly and in combination.