Model-Based Regional Control with Anomalous Diffusion of Multi-Drug Combined Cancer Therapy for Volume Predictions

Abstract

Symmetry breaking in the anatomical lung is triggered by tumorigenesis and disrupted by delivering single or multiple drugs to stop the progression of the tumor and treat cancer. In this study, a prior model of combined drug therapy is augmented to introduce tissue heterogeneity when the drug is applied in multi-drug therapy of lung cancer. Patient-related drug resistance and synergy are investigated as a function of diffusion intensity as drug molecules reach the tumor site. The results indicate that diffusion of drug molecules plays an important role next to other factors such as patient sensitivity to the drug and drug synergy effects. We conclude that the minimal model provides meaningful predictions on tumor growth at the intermediate mesoscale level. With such models at hand, it is now possible to employ model-based control algorithms to optimize the dose profiles in terms of time and amount. In this paper, we present a theoretical framework for control employing networked game theory optimality. Specific situations are discussed in terms of finding optimality at Nash equilibrium in relation to patient response and drug synergy effects.

Keywords

• cancer therapy
• prediction model
• predictive control