An agent-based stochastic tumor model for predicting mitotic arrest drug response

We present a 2D agent-based stochastic solid tumor model which incorporates cellular response to a chemotherapeutic drug such as paclitaxel (Taxol). First, we show that our model not only reproduces the findings of past mathematical models but also agrees qualitatively with previously observed experimental findings in vitro. Then, the model is used to study two contrasting methods of chemotherapeutic dosing: (1) maximum tolerated dose (MTD) and (2) metronomic dosing. Results of our simulations confirm the typical clinical observation that a single front-loaded dosing to MTD leads to a period of remission followed by recurrence, while metronomic dosing maintains or reduces the tumor size. These results suggest patient treatment strategies should favor the more recently proposed metronomic dosing paradigm over the traditionally used MTD therapy. The model proposed here is adaptable to different solid tumor types as well as different chemotherapeutic drug pharmacokinetic and pharmacodynamic properties, making it ideal for similar studies on different clinical problems.