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.