  Recent clinical studies suggest that the efficacy of hormone therapy for
prostate cancer depends on the characteristics of individual patients. In this
paper, we develop a computational framework for identifying patient-specific
androgen ablation therapy schedules for postponing the potential cancer
relapse. We model the population dynamics of heterogeneous prostate cancer
cells in response to androgen suppression as a nonlinear hybrid automaton. We
estimate personalized kinetic parameters to characterize patients and employ
$\delta$-reachability analysis to predict patient-specific therapeutic
strategies. The results show that our methods are promising and may lead to a
prognostic tool for personalized cancer therapy.
