Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer

Mathematical modeling of prostate cancer under intermittent androgen suppression revealed that we may be able to delay relapse by optimally scheduling the hormone therapy for each patient. However, our previous study showed the difficulty of the scheduling by minimizing the maximal tumor growth rate because the transient dynamics is also important and can help to delay the relapse for a finite time. Here, we propose to use model predictive control for scheduling intermittent androgen suppression. We find that model predictive control tends to delay the relapse of prostate specific antigen more than the method with minimizing the maximal tumor growth rate. Therefore, model predictive control is a promising approach for practically applying the mathematical model to optimally schedule intermittent androgen suppression.