Mathematical modeling of drug response

Current processes for creating cancer treatment plans are primarily based on empirical data from clinical trials, consensus expert panel guidelines, and limited laboratory-based testing, but physicians still face major challenges in successful before-treatment prediction of the effectiveness of any particular treatment plan for any given patient. Work done in physical oncology not only can help improve treatment outcomes (as examined in Chapter 3), but also could benefit many cancer patients if incorporated into the creation of treatment plans. Here, we will examine three published papers that provide evidence for improving cancer treatment plan efficacy through the inclusion of mathematical modeling techniques, based on our capability to consider the multiple scales of the human body (cellular, tissue, and organ) when predicting patient-specific treatment outcomes. More specifically, we first developed a mathematical model that is capable of predicting the response of cancer cells to any drug concentration in vitro based on the uptake rate of drug by the cells [10]. Next, we used a mathematical model to predict breast cancer growth inhibition in vitro and in vivo, in order to demonstrate the inhibitory effects of tissue-scale diffusion barriers on drug delivery [189]. Finally, we developed a more general model that includes a spatial variable at the tissue scale to predict tumor response, leading to the discovery of how the effectiveness of chemotherapy is dependent on the vasculature of the tumor microenvironment [154].