Personalized optimal dosing strategies for anticancer chemotherapy using mathematical modeling

Cancer is one of the leading causes of death worldwide, and chemotherapy is widely used as a treatment for cancer. Although maximizing therapeutic efficacy is a crucial strategy for chemotherapy, its efficacy is limited due to the empirical trial-and-error approach to determining dosage and schedule. In this work, we develop an optimal control problem for dosing schedules under some restrictions using a pharmacokinetic/pharmacodynamic model. The purpose of this article is to design some optimal dosing schedules and to investigate the impact of these optimal schedules on controlling cancer growth. Optimal control analysis reveals that mid-treatment concentrated dosing substantially improves therapeutic outcomes, with treatment timing proving robust across both pharmacokinetic and pharmacodynamic heterogeneity, while efficacy remains patient-specific, suggesting a practical optimization strategy combining robust population-level scheduling with individualized benefit prediction.