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Modeling the Effects of Morphine on Simian Immunodeficiency Virus Dynamics

View Article: PubMed Central - PubMed

ABSTRACT

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.

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Long term dynamics of viral load and T cell subpopulations.Long term dynamics, predicted by the model, of the viral load (left) and the % of target cells that are Th cells (right) for the morphine group (dashed-dot curve) and the control group (solid curve). Parameters given in Table 2 are used for model simulations. Small circles indicate the available data, which is restricted to time points early in infection.
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pcbi.1005127.g003: Long term dynamics of viral load and T cell subpopulations.Long term dynamics, predicted by the model, of the viral load (left) and the % of target cells that are Th cells (right) for the morphine group (dashed-dot curve) and the control group (solid curve). Parameters given in Table 2 are used for model simulations. Small circles indicate the available data, which is restricted to time points early in infection.

Mentions: We then carried out simulations of the full model to analyze effects of morphine on the dynamics as well as the infected-steady state level. For about 5–6 weeks post-infection, the plasma viral load dynamics remained generally comparable in both the morphine and control groups. However, after 6 weeks of infection the viral load in the morphine group increases and eventually approaches a higher steady state level (Fig 3). In contrast to the morphine group, the viral load in the control group did not increase and leveled off with about a 1.0 log10 lower steady state value. We also observed how the percentage of target cells in Th compartment changes over time (Fig 3). In both groups, the percentage of target cells in the Th compartment decreases after infection following a brief delay, reaches a minimum, and again increases to a steady state level. Throughout the dynamics, this percentage in the morphine group almost always remained higher than in the control group, with a significantly higher steady state level in the morphine group (Fig 3).


Modeling the Effects of Morphine on Simian Immunodeficiency Virus Dynamics
Long term dynamics of viral load and T cell subpopulations.Long term dynamics, predicted by the model, of the viral load (left) and the % of target cells that are Th cells (right) for the morphine group (dashed-dot curve) and the control group (solid curve). Parameters given in Table 2 are used for model simulations. Small circles indicate the available data, which is restricted to time points early in infection.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5036892&req=5

pcbi.1005127.g003: Long term dynamics of viral load and T cell subpopulations.Long term dynamics, predicted by the model, of the viral load (left) and the % of target cells that are Th cells (right) for the morphine group (dashed-dot curve) and the control group (solid curve). Parameters given in Table 2 are used for model simulations. Small circles indicate the available data, which is restricted to time points early in infection.
Mentions: We then carried out simulations of the full model to analyze effects of morphine on the dynamics as well as the infected-steady state level. For about 5–6 weeks post-infection, the plasma viral load dynamics remained generally comparable in both the morphine and control groups. However, after 6 weeks of infection the viral load in the morphine group increases and eventually approaches a higher steady state level (Fig 3). In contrast to the morphine group, the viral load in the control group did not increase and leveled off with about a 1.0 log10 lower steady state value. We also observed how the percentage of target cells in Th compartment changes over time (Fig 3). In both groups, the percentage of target cells in the Th compartment decreases after infection following a brief delay, reaches a minimum, and again increases to a steady state level. Throughout the dynamics, this percentage in the morphine group almost always remained higher than in the control group, with a significantly higher steady state level in the morphine group (Fig 3).

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus