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Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

AbstractMOTIVATION:
Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection.
RESULTS:
The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups.
AVAILABILITY:
A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html
AuthorsF Castiglione, F Pappalardo, M Bernaschi, S Motta
JournalBioinformatics (Oxford, England) (Bioinformatics) Vol. 23 Issue 24 Pg. 3350-5 (Dec 15 2007) ISSN: 1367-4811 [Electronic] England
PMID17942443 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Chemical References
  • Anti-HIV Agents
Topics
  • Algorithms
  • Anti-HIV Agents (administration & dosage)
  • Antiretroviral Therapy, Highly Active (methods)
  • Computer Simulation
  • Drug Administration Schedule
  • Drug Therapy, Computer-Assisted (methods)
  • HIV Infections (drug therapy)
  • Humans
  • Immunity, Innate (drug effects, immunology)
  • Models, Immunological

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