Estimation of HIV dynamic parameters.
Estimation of HIV dynamic parameters.
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DOI：
10.1002/(sici)1097-0258(19981115)17:21
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发表时间：
1998-11
影响因子：
2
通讯作者：
H. Wu;A. Ding;V. De Gruttola
中科院分区：
文献类型：
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作者：
H. Wu;A. Ding;V. De Gruttola
研究HIV病毒动力学对了解HIV发病机制和制定治疗策略具有重要意义。Perelson等人证明，简单的病毒动力学模型与血浆HIV-RNA测量的病毒载量数据相吻合，可以估算出病毒和受感染CD4+ t淋巴细胞的清除率。在本文中，我们通过提出对药物活性限制较少的模型，扩展了Perelson等人的工作。我们的模型考虑了这样一个事实，即感染性和非感染性病毒粒子是由感染的t细胞在治疗前后产生的。我们还表明，直接测量感染病毒载量为估计抗逆转录病毒药物疗效参数提供了足够的信息。为了描述种群的病毒动力学和估计动态参数，我们提出了一个层次非线性模型。与其他方法（如Perelson等人使用的非线性最小二乘法）相比，我们表明，所提出的方法具有以下优点：(i)它更适合于建模患者内部和患者之间的变化，并表征群体动态；（ii）它足够灵活，可以处理丰富和稀疏的单个数据；(3)具有更强的模型错配检测能力；（iv）允许纳入病毒动态参数的协变量；(v)更有效地利用主体间信息，得到更好的参数估计。我们给出了两个仿真实例来说明所提出的方法及其优点。最后，我们讨论了有关病毒动力学研究的临床试验设计的实际问题。
Investigation of HIV viral dynamics is important for understanding the HIV pathogenesis and for development of treatment strategies. Perelson et al. demonstrated that simple viral dynamic models fit to data on viral load as measured by plasma HIV-RNA could produce estimates of rates of clearance of virus and of infected CD4+ T-lymphocytes. In this paper we extend the work of Perelson et al. by proposing models with less restrictive assumptions about drug activity. Our models take into account the fact that infectious and non-infectious virions are produced by infected T-cells both before and after the treatment. We also show that direct measurement of infectious virus load provides sufficient information for estimation of antiretroviral drug efficacy parameter. For characterizing viral dynamics of populations and estimation of dynamic parameters, we propose a hierarchical non-linear model. Compared to other methods such as the non-linear least square method used by Perelson et al., we show that the proposed approach has the following advantages: (i) it is more appropriate for modelling within-patient and between-patient variation and to characterize the population dynamics; (ii) it is flexible enough to deal with both rich and sparse individual data; (iii) it has more power to detect model misspecification; (iv) it allows incorporation of covariates for viral dynamic parameters; (v) it makes more efficient use of between-subject information to get better parameter estimates. We give two simulation examples to illustrate the proposed approach and its advantages. Finally, we discuss practical issues regarding the clinical trial design for viral dynamic studies.