Piecewise affine systems modelling for optimizing hormone therapy of prostate cancer.

Prostate cancer is one of the most common malignant neoplasms in men with an overall incidence of approximately 15 per cent during the normal life span. Androgen-deprivation therapy (hormone therapy) is an effective treatment of this disease when progressed to an advanced stage. Despite impressive responses, such treatment when applied on a continuous basis is not curative and eventually culminates in androgen-independent disease. On the other hand, intermittent androgen suppression (IAS) was first conceived as a potential way of delaying progression to androgen-independence, in addition offering the possibility of reducing adverse effects and improving the quality of life. Although the validity of this approach has been confirmed in several clinical studies, the optimal scheduling of the cycles of on- and off-treatment remains to be explored. In the present article, we show that IAS lends itself to mathematical modelling with hybrid dynamical systems and that the model we have developed can be used to select the best strategy for keeping prostate cancer in an androgen-dependent state as long as possible. Our results also suggest that the current way of using IAS exceeds what is necessary for optimal control; in fact, we have found that to achieve optimal control, the amount of therapy (dose and duration of drugs) can be reduced by a factor of one half.