Overcoming Channel Uncertainties in Molecular-Communication-Inspired Direct Drug Targeting

Recent progress in the development of new methods for cancer treatment has shown advantages of multiple therapies over mono-therapy. In particular, direct drug targeting (DDT) combined with mixed immunotherapy and chemotherapy has the potential to mitigate the undesired side-effects allied with conventional therapies, where nanorobots in DDT carry therapeutic agents through the blood vessel channel in order to localize and target diseased tissue with a safe drug interaction. This process can be modeled by a "touchable" (i.e., externally controllable and trackable) molecular communication (MC) system. However, in such a complex process overcoming unavoidable vascular channel uncertainties remains a great challenge. In this paper a multiple model predictive controller (MMPC) is proposed, which is robust against random channel uncertainties. The efficacy of the proposed method is illustrated through identification of globally optimized drug administration schedules. Furthermore, we introduce upper and lower bounds on the inputs and outputs which lead to clinically realistic constraints on the system. Simulation results demonstrate the promising performance of proposed MMPC to control tumor growth in presence of vascular channel uncertainties in MC-inspired DDT for cancer treatment.