Optimal adaptive cancer therapy based on evolutionary game theory.

Area of Science:

• Oncology
• Mathematical Biology
• Pharmacokinetics

Background:

• Cancer evolution drives drug resistance, limiting traditional treatment efficacy.
• Adaptive therapy is an emerging strategy to manage cancer treatment resistance.
• Integrating pharmacokinetics into evolutionary game theory models can optimize cancer treatment.

Purpose of the Study:

• To develop an optimal adaptive therapy strategy by incorporating pharmacokinetics into a cancer evolutionary game theory model.
• To design a treatment plan constrained by maximum drug concentration and tumor burden.
• To compare the efficacy of the optimized adaptive therapy with existing strategies.

Main Methods:

• Formulated an optimal control problem using pharmacokinetics and evolutionary game theory.
• Applied Pontryagin's minimum principle to determine the optimal control structure.
• Conducted numerical simulations to compare treatment strategies and develop personalized plans.

Main Results:

• Demonstrated the existence of an optimal control for adaptive cancer therapy.
• The optimized adaptive therapy preserves healthy cell survival.
• The strategy effectively controls drug-resistant cell proliferation and reduces tumor burden.

Conclusions:

• The optimized adaptive therapy strategy significantly improves patient survival.
• This approach offers a promising method for personalized cancer treatment.
• Maintaining low-level competition between cell populations is key to preventing resistance.