Optimization framework for overcoming tyrosine kinase inhibitor resistance: Multi-objective selection, scheduling, and adaptive therapy

Tyrosine kinase inhibitors are key drugs in targeted cancer therapy but often fail when resistance emerges. Many predictive methods focus on accuracy alone, while calibration and kinase selectivity, which matter for clinical use, receive less attention. We present a single framework that treats resistance prediction and dosing decisions as a three-objective problem: minimize misclassification, reduce calibration error, and increase selectivity. Using calibrated probabilities and tuned thresholds, baseline models improved in ROC–AUC and expected calibration error across stratified, scaffold, and mutation-cold splits. Pareto analysis with hypervolume and coverage showed that including selectivity changes the relative ranking of inhibitors and exposes trade-offs that accuracy alone cannot capture. On the treatment side, we compared continuous dosing, hysteresis switching, and adaptive model predictive control in a two-compartment tumor model. Adaptive control lowered total dose by about 18% and extended simulated survival by more than 25 weeks. These results provide a clear proof of concept that combining machine learning, multi-objective optimization, and adaptive therapy can improve prediction quality and guide personalized dosing to better manage resistance. • Resistance to tyrosine kinase inhibitors (TKIs) was reformulated as a multi-objective optimization problem that integrates discrimination, cali- bration, and kinase selectivity. • A unified computational framework was developed, combining machine learning models with evolutionary algorithms and reinforcement learning to optimize dosing strategies. • Adaptive therapy was incorporated using model predictive control, en- abling dynamic treatment schedules that balance disease control and drug burden. • In silico experiments demonstrated that optimized dosing policies can pro- long progression-free survival while reducing cumulative exposure to TKIs. • The proposed framework establishes a proof-of-concept for integrating pre- dictive modeling and adaptive control into resistance-aware treatment de- sign with translational potential.