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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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CD4 count dynamics.Model prediction for the long term dynamics of CD4 count (left) and the total loss of CD4 count during a year of SIV infection (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.
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pcbi.1005127.g004: CD4 count dynamics.Model prediction for the long term dynamics of CD4 count (left) and the total loss of CD4 count during a year of SIV infection (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.

Mentions: Since morphine conditioning results in a higher steady state level of target cells in the Th compartment, which have a higher infection rate, morphine was expected to cause a greater loss of CD4 cells. To quantify this, we simulated the dynamics of CD4 cells (Fig 4) and computed the total loss of CD4 cells during one year of SIV infection. Our model predicts a rapid decrease of the CD4 count in the morphine group compared to the control group at the beginning of the infection, consistent with the experimental data. At the end of a year, the predicted total CD4 count drop is 90% in the morphine group while it is 82% in the control group (Fig 4). The difference in CD4 loss between the two groups is not statistically significant (t-test, p > 0.05). However, animal M4 has an extremely high CD4 count throughout the infection (see Table 1, S1 Fig); and its set point CD4 count remains higher than 700 cells/μL while the maximum set point CD4 count of all other animals in the morphine group is 42 cells/μL. Excluding animal M4, the loss of CD4 cells in the morphine group is significantly higher than that in the control group (t-test, p < 0.001).


Modeling the Effects of Morphine on Simian Immunodeficiency Virus Dynamics
CD4 count dynamics.Model prediction for the long term dynamics of CD4 count (left) and the total loss of CD4 count during a year of SIV infection (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.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005127.g004: CD4 count dynamics.Model prediction for the long term dynamics of CD4 count (left) and the total loss of CD4 count during a year of SIV infection (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.
Mentions: Since morphine conditioning results in a higher steady state level of target cells in the Th compartment, which have a higher infection rate, morphine was expected to cause a greater loss of CD4 cells. To quantify this, we simulated the dynamics of CD4 cells (Fig 4) and computed the total loss of CD4 cells during one year of SIV infection. Our model predicts a rapid decrease of the CD4 count in the morphine group compared to the control group at the beginning of the infection, consistent with the experimental data. At the end of a year, the predicted total CD4 count drop is 90% in the morphine group while it is 82% in the control group (Fig 4). The difference in CD4 loss between the two groups is not statistically significant (t-test, p > 0.05). However, animal M4 has an extremely high CD4 count throughout the infection (see Table 1, S1 Fig); and its set point CD4 count remains higher than 700 cells/μL while the maximum set point CD4 count of all other animals in the morphine group is 42 cells/μL. Excluding animal M4, the loss of CD4 cells in the morphine group is significantly higher than that in the control group (t-test, p < 0.001).

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