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用于临床试验规划的短期和长期 HIV 病毒反弹的统一模型。

Unified model of short- and long-term HIV viral rebound for clinical trial planning.

机构信息

Department of Mathematics and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA.

University of Pennsylvania School of Arts and Sciences, Philadephia, PA, USA.

出版信息

J R Soc Interface. 2021 Apr;18(177):20201015. doi: 10.1098/rsif.2020.1015. Epub 2021 Apr 14.

Abstract

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 (Li 2016 , 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.

摘要

抗逆转录病毒疗法 (ART) 可有效控制 HIV 感染,抑制 HIV 病毒载量。通常,一旦停止治疗,病毒载量会迅速反弹至治疗前的高水平。事实上,最近的一项研究表明,在大多数治疗中断研究参与者中,约有 90%在停止治疗最多几个月内就会出现病毒反弹,但剩下的 10%在停止 ART 后数月甚至数年才出现病毒反弹。有些人甚至从未出现反弹。我们研究并比较了分支过程模型,旨在深入了解这些病毒动力学。具体来说,我们提供了一个理论,通过具有时变率的多类型分支过程来解释短期和长期的病毒反弹以及治疗后控制,该理论通过 Li 的数据得到验证 (Li 2016, 343-353. (doi:10.1097/QAD.0000000000000953))。我们讨论了我们最佳拟合模型的相关生物学解释和意义。为了测试实验干预延迟或预防反弹的效果,标准做法是停止治疗并监测研究参与者的反弹情况。最后,我们讨论了我们的模型在设计此类临床试验中的一个重要应用。

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