Local Anomalous Drug Diffusion at Healthy-Cancer Tissue Surface and Data-Driven Tumor Growth Model Prediction *

This paper discusses reliable yet minimal computational models for predicting the patient’s response to anticancer multi-drug combined therapy. The distribution of the drugs into the local heterogeneity of healthy-tumor tissues can be translated into mathematical models. Ideally, these should best describe the physiological processes and physical mechanisms, together with the interactions between the contributing components of the tumor growth dynamic system. Our previously proposed pharmacokinetic-pharmacodynamic (PKPD) mathematical model is revisited for different spatiotemporal fractional drug diffusion patterns. In particular, we examine the specific diffusion-related factors that limit drug effect through the tumor’s surface. The ability of the tumor growth PKPD model to predict patient responsiveness was evaluated using prior radiation therapy data in a patient with lung cancer. This study shows that the effect of anomalous diffusion mechanisms within tumor tissue should be considered while modeling the dose-response relationship for optimal results of cancer therapies.