Prosperi Mattia C F, D'Autilia Roberto, Incardona Francesca, De Luca Andrea, Zazzi Maurizio, Ulivi Giovanni
Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy.
Bioinformatics. 2009 Apr 15;25(8):1040-7. doi: 10.1093/bioinformatics/btn568. Epub 2008 Oct 31.
Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios.
The stochastic predator-prey modelling usefully predicted long-term virologic outcomes of evolved HIV-1 strains for selected antiretroviral therapy combinations. For a set of widely used combination therapies, results were consistent with findings reported in literature and with estimates coming from analysis on a large retrospective data base (EuResist).
已经研究了几种数学模型来描述人体中的病毒动力学：HIV-1感染是一种特殊且有趣的情况，因为该病毒攻击在抗体产生中起作用的免疫系统细胞，并且其高突变率使其能够逃避免疫反应以及在某些情况下的药物压力。病毒基因进化本质上是一个随机过程，最终由药物压力驱动，取决于药物组合和浓度：在本文中，病毒基因型耐药性的出现是主要关注的问题。理论基础是将HIV-1群体动力学建模为具有时间依赖性治疗效果项的微分方程的捕食者 - 猎物系统，而病毒基因组突变进化遵循泊松分布。耐药性的即时概率通过从体外表型训练的函数进行估计，并采用基于轮盘赌的耐药性选择机制。已经针对由一种和两种药物组成的治疗以及联合抗逆转录病毒疗法设计了模拟。还分析了治疗依从性有限的影响。还利用了序贯治疗变化事件，以评估最佳的综合治疗方案。
随机捕食者 - 猎物模型有效地预测了所选抗逆转录病毒治疗组合下进化的HIV-1菌株的长期病毒学结果。对于一组广泛使用的联合疗法，结果与文献报道的结果以及来自大型回顾性数据库（EuResist）分析的估计结果一致。
