Optimal control of two cytotoxic drug maximum tolerated dose steers and exploits cancer adaptive resistance in a cell-based framework

Aggressive cancers are typically incurable because of drug resistance development. We model cancer growth and adaptive genetic response in a cell-based (CB) setting, displaying how optimal maximum tolerated dose administration protocols of two drugs counteract drug resistance and turn it into an exploitable weakness. Our CB model is a spatial extension of the population-based model proposed by Orlando et al. [1], where a homogeneous population of cancer cells evolves according to a fitness landscape. To make a first feasibly test of the optimal drug administration control problem in the CB framework, we add only the elements we consider most relevant for describing cancer growth and evolution: phenotypic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. We calibrate our model on Orlando et al.'s one and find that dynamical protocols switching between the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results differ from those of Orlando and colleagues, which suggest static protocols under generalizing and neutral allocation trade-offs.