HIV Post-Treatment: Stochastic Modeling Insights on Rebound and Control

抗逆转录病毒药物治疗(ART)可以有效地控制HIV感染，并将血浆病毒载量从非常高的水平降低到难以检测到的低水平。ART停止后通常会出现病毒快速反弹(VR)，达到ART前的高病毒载量。然而，最近的观察给出了这种模式的细微差别。2013年，关于“密西西比婴儿”的报道浮出水面，这名婴儿出生时携带艾滋病毒，出生后不久就接受了抗逆转录病毒治疗。她在18个月大的时候停止了抗逆转录病毒治疗，似乎已经痊愈，但艾滋病毒在27个月后再次出现并反弹。第二年，一项研究显示，尽管在研究发表前4-10年停止抗逆转录病毒治疗，但仍有14名艾滋病毒感染者--“维斯康蒂队列”--继续控制自己的艾滋病毒感染。这些例子和其他例子表明，在一些艾滋病毒感染者中，暂停抗逆转录病毒治疗和虚拟现实之间可能会有明显的延迟。延迟的VR和治疗后控制(PTC)的可能性人们知之甚少，但暗示了在不使用ART的情况下治疗HIV的令人兴奋的可能性，从而避免了副作用和高昂的药物成本。此外，将艾滋病毒控制到无法检测到的水平的人不太可能感染其他人，从而降低了艾滋病毒在整个人口中传播的可能性。本项目的目标是通过分析以下三个问题来更好地理解延迟VR和PTC：在什么情况下，ART暂停会导致延迟VR或PTC？如果艺术暂停导致VR，它会在什么时候发生？人们能预测患者何时可以永久地控制ART后的感染吗？该项目的核心是开发数学病毒动力学模型，以研究停止ART后HIV患者的VR。这些模型将利用随机的多类型分支过程，因为接受治疗的患者中的病毒种群很小。虽然随机方法被用来对病毒动力学进行建模，但标准的方法是广泛的模拟；在这个项目中，首席调查员将强调分析结果的推导，从而引入从应用概率到病毒动力学问题的新工具。这些目标将通过随机模型来解决，这些模型将通过侧重于艾滋病毒控制的不同生物学方面(如免疫反应)的项目来增加复杂性和现实性。这些模型将使用来自实验合作者的艾滋病毒动力学和VR数据以及发布的数据进行验证。该模型的目的是更好地描述虚拟现实的动态特性，对虚拟现实的时间做出可测试的预测，并获得对艾滋病毒治疗后控制的洞察。在医疗环境中，开发的模型可以为有关ART暂停的临床指南提供信息，导致在ART中断期间优化测试频率以检测VR，并预测VR不太可能出现的PTC或“HIV缓解”。

Antiretroviral drug therapy (ART) can effectively control HIV infection and reduce plasma virus load from very high, to undetectably low, levels. Cessation of ART is typically followed by rapid viral rebound (VR) to the high, pre-ART viral loads. However, recent observations give nuance to this pattern. In 2013, reports of the "Mississippi baby" emerged, a child born with HIV and treated with ART shortly after birth. Taken off ART at 18 months of age, she appeared to be cured, only for HIV to reappear and rebound after 27 months. The following year, a study revealed 14 HIV+ individuals - "the VISCONTI cohort" - who continued to control their HIV infection in spite of having stopped ART 4-10 years before study publication. These and other examples show that in some HIV-infected individuals, there can be significant delays between the suspension of ART and VR. Delayed VR and the potential for post-treatment control (PTC) are poorly understood but suggest exciting possibilities for treatment of HIV without ART, thereby avoiding side effects and high drug costs. Further, people who control HIV to undetectable levels are unlikely to infect others, thus reducing the possibility of HIV transmission in the population as a whole. The goal of this project is to better understand delayed VR and PTC by analyzing the following three questions: Under what circumstances will ART suspension lead to delayed VR or PTC? If ART suspension leads to VR, when will it occur? Can one predict when a patient can control infection off ART, permanently?Central to this project is the development of mathematical viral dynamics models to investigate VR in HIV+ individuals following cessation of ART. The models will make use of stochastic multi-type branching processes, since viral populations in treated patients are small. While stochastic methods have been used to model viral dynamics, the standard approach is extensive simulation; in this project, the Principal Investigator will emphasize the derivation of analytic results instead, thereby introducing novel tools from applied probability to viral dynamics problems. The objectives will be addressed by stochastic models that will increase in complexity and realism through projects focusing on different biological aspects of HIV control such as immune responses. These models will be validated using data on HIV dynamics and VR from experimental collaborators and published data. The aim of this modeling is to better characterize VR dynamics, make testable predictions on times to VR, and gain insights into post-treatment control of HIV. In medical settings, the models developed may inform clinical guidelines with regards to ART suspension, lead to optimizing testing frequency during the ART interruption to detect VR and predicting PTC or "HIV remission" where VR will be unlikely.