The MOEO algorithm for multi-objective optimization of the cancer immuno-chemotherapy

Area of Science:

• Computational biology
• Mathematical oncology
• Optimization algorithms

Background:

• Chemotherapy's non-specific cell killing necessitates improved cancer treatment strategies.
• Mixed-treatment approaches like chemo-immunotherapy offer potential but face dosage optimization challenges.
• Existing metaheuristic algorithms show limitations in consistently solving complex optimization problems in cancer therapy.

Purpose of the Study:

• To introduce a novel multi-objective Equilibrium Optimizer (MOEO) algorithm for optimizing cancer treatment.
• To evaluate the MOEO's performance in chemotherapy and chemo-immunotherapy, considering tumor-immune dynamics and patient health.
• To demonstrate the algorithm's ability to find optimal drug dosages for personalized cancer treatment.

Main Methods:

• Extension of the single-objective Equilibrium Optimizer algorithm to a multi-objective version (MOEO).
• Application of MOEO to optimize drug dosages in chemotherapy and chemo-immunotherapy models.
• Consideration of tumor-immune dynamic system constraints and patient health levels.
• Validation using real clinical data from two distinct cancer patients.

Main Results:

• The MOEO algorithm successfully determined optimal treatment dosages for both chemotherapy and chemo-immunotherapy.
• Significant reductions in total chemotherapy drug doses were observed for both patients: Patient 1 (138.92, 5.84) and Patient 2 (16.9, 0.4384) for chemo and chemo-immunotherapy, respectively.
• The Pareto front generated by MOEO provides a range of treatment options based on different criteria.

Conclusions:

• The proposed multi-objective Equilibrium Optimizer (MOEO) algorithm demonstrates superior performance in optimizing cancer treatment strategies.
• MOEO is particularly effective for mixed-treatment approaches like chemo-immunotherapy, offering reduced drug toxicity.
• The algorithm provides a valuable tool for personalized cancer therapy by balancing treatment efficacy and patient health.