Drug dosing for cancer therapy: A stochastic model predictive control perspective

Abstract

Stochastic Model Predictive Control (SMPC) is an effective decision-making method in applications where uncertainties play a significant role. This work introduces a non-linear formulation of SMPC specifically designed for cancer therapy. The proposed method considers the stochastic nature of tumor growth, non-linear dynamics, and a potential side effect of the treatment. Through one-year simulations, the results showcase the effectiveness of this strategy in controlling drug dosing.