Optimal cancer therapy based on a tumor growth inhibition model

The most effective cancer treatments are the ones that prolong patients´ lives while offering a reasonable quality of life during and after treatment. The treatments must also carry out their actions rapidly and with high efficiency such that a very large percentage of tumor cells die or shift into a state where they stop proliferating. Due to biological and micro-environmental variabilities within tumor cells, the action period of an administered drug can vary among a population of patients. In this paper, based on a recently proposed model for tumor growth inhibition, we first characterize the variability of the length of drug action probabilistically. Then, we present a methodology to devise optimal intervention strategies for any Markovian genetic regulatory network governing the tumor when the antitumor drug has a random-length duration of action.