Health-Constrained Nonlinear Model Predictive Control for Subject-Specific Chemotherapy Dosing Under Parameter Variability and Model Mismatch: A Simulation-Based Proof of Concept

Selecting an appropriate chemotherapy dose is challenging because tumor growth is nonlinear, treatment can affect physiological condition, and tumor–drug responses vary across subjects. This study extends the third-order tumor–drug model of Siket et al. with a normalized, phenomenological physiological-health state and formulates a health-constrained nonlinear model predictive control (NMPC) framework that combines tumor suppression, health preservation, and control smoothness under the simulation-defined constraint xh≥80. Simulations were conducted on eight virtual Siket parameter profiles. At λ=0.50, PLD3, PLD4, and PLD6 achieved tumor-volume reductions of approximately 74.7%, 73.4%, and 77.6%, respectively, whereas the other five profiles showed tumor progression. The structural indicator ρ=a−b−n was strongly negatively associated with tumor reduction (Pearson correlation of approximately −0.91 for all profiles and −0.98 excluding PLD1); this is a descriptive observation based on only eight profiles. A dose-matched comparison with a simple threshold-based strategy favored NMPC for some profiles, although the health constraint was not imposed on the comparator. The health bound was satisfied in all 15 one-at-a-time mismatch cases, but joint parameter perturbations produced threshold violations and optimization difficulties for PLD1 and PLD9, and health-model sensitivity analyses revealed further difficulties for PLD1. The health model and its threshold are not calibrated against clinical data. This work is a simulation-based proof of concept and does not establish clinical safety, clinical efficacy, or general robustness.