Physics-based models and simulations of cancer drug response in solid tumors

Over the past few decades, cancer related deaths have fallen significantly as noted by the National Cancer Institute. However, assessing cancer treatments is still predominantly a trial and error process. This approach may result in delays to administer the correct treatment, the use of more invasive procedures than necessary, or an increase in toxicity due to superfluous treatments. Although these procedures may end up saving the patient, the treatment may also have an adverse effect on their quality of life. Relaible mechanistic models of drug response can potentially be used to aid oncologists and doctors in deciding on an optimal treatment strategy for the patient. We develop a modeling framework for tumor ablation, and present coupled transport - population models of varying complexity. First, we present a radially symmetric drug diffusion and binary cell death model, which produces a theoretical dose for optimal effiacy to toxicity ratios. Further, we investigate inhomogeneous - anisotropic drug diffusion, and develop an algorithm to locate the optimal injection points. Finally, we derive stochastic tumor population models that can be coupled to transport models in our framework. Importantly, the mechanistic models outperform data-driven models in statistical tests.