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

Motivation: Highly Active AntiRetroviral Therapies (HAART) can pro-
long 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 paper we describe an application of genetic algo-
rithms (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 therapeu-
tic 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 oppor tunistic diseases and
found that the selected therapy shows the best survival curve among
the different simulated control groups.