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Title: Modeling the Effects of Morphine on Simian Immunodeficiency Virus Dynamics

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 steadymore » 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. In conclusion, 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.« less
Authors:
 [1] ;  [2] ;  [2] ;  [3]
  1. Univ. of Missouri-Kansas City (UMKC), MO (United States). Dept. of Mathematics and Statistics; Univ. of Missouri-Kansas City (UMKC), MO (United States). School of Pharmacy, Division of Pharmacology
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Univ. of Missouri-Kansas City (UMKC), MO (United States). School of Pharmacy, Division of Pharmacology
Publication Date:
Report Number(s):
LA-UR-15-21504
Journal ID: ISSN 1553-7358
Grant/Contract Number:
AC52-06NA25396; KDA-91; DA015013; DMS-1616299; R01-AI028433; R01-OD011095; R01-AI104373
Type:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 9; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; National Science Foundation (NSF); National Institutes of Health (NIH)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; Biological Science
OSTI Identifier:
1345288
Alternate Identifier(s):
OSTI ID: 1352443