Vaidya Naveen K, Ribeiro Ruy M, Perelson Alan S, Kumar Anil
Department of Mathematics and Statistics, University of Missouri-Kansas City, Missouri, United States of America.
Division of Pharmacology, School of Pharmacy, University of Missouri-Kansas City, Missouri, United States of America.
PLoS Comput Biol. 2016 Sep 26;12(9):e1005127. doi: 10.1371/journal.pcbi.1005127. eCollection 2016 Sep.
Complications of HIV-1 infection in individuals who utilize drugs of abuse is a significant problem, because these drugs have been associated with higher virus replication and accelerated disease progression as well as severe neuropathogenesis. To gain further insight it is important to quantify the effects of drugs of abuse on HIV-1 infection dynamics. Here, we develop a mathematical model that incorporates experimentally observed effects of morphine on inducing HIV-1 co-receptor expression. For comparison we also considered viral dynamic models with cytolytic or noncytolytic effector cell responses. Based on the small sample size Akaike information criterion, these models were inferior to the new model based on changes in co-receptor expression. The model with morphine affecting co-receptor expression agrees well with the experimental data from simian immunodeficiency virus infections in morphine-addicted macaques. Our results show that morphine promotes a target cell subpopulation switch from a lower level of susceptibility to a state that is about 2-orders of magnitude higher in susceptibility to SIV infection. As a result, the proportion of target cells with higher susceptibility remains extremely high in morphine conditioning. Such a morphine-induced population switch not only has adverse effects on the replication rate, but also results in a higher steady state viral load and larger CD4 count drops. Moreover, morphine conditioning may pose extra obstacles to controlling viral load during antiretroviral therapy, such as pre-exposure prophylaxis and post infection treatments. This study provides, for the first time, a viral dynamics model, viral dynamics parameters, and related analytical and simulation results for SIV dynamics under drugs of abuse.
在滥用药物的个体中,HIV-1感染的并发症是一个重大问题,因为这些药物与更高的病毒复制、加速的疾病进展以及严重的神经病变相关。为了进一步深入了解,量化滥用药物对HIV-1感染动态的影响很重要。在此,我们开发了一个数学模型,该模型纳入了吗啡对诱导HIV-1共受体表达的实验观察效应。为了进行比较,我们还考虑了具有细胞溶解或非细胞溶解效应细胞反应的病毒动力学模型。基于小样本量的赤池信息准则,这些模型不如基于共受体表达变化的新模型。吗啡影响共受体表达的模型与吗啡成瘾猕猴中猿猴免疫缺陷病毒感染的实验数据非常吻合。我们的结果表明,吗啡促进了靶细胞亚群从较低易感性状态转变为对SIV感染易感性高约2个数量级的状态。因此,在吗啡预处理中,具有较高易感性的靶细胞比例仍然极高。这种由吗啡诱导的群体转变不仅对复制率有不利影响,还会导致更高的稳态病毒载量和更大的CD4细胞计数下降。此外,吗啡预处理可能会在抗逆转录病毒治疗(如暴露前预防和感染后治疗)期间对控制病毒载量造成额外障碍。本研究首次提供了一个关于滥用药物情况下SIV动态的病毒动力学模型、病毒动力学参数以及相关的分析和模拟结果。