Safe optimal control of cancer using a Control Barrier Function technique.

Area of Science:

• Oncology
• Control Theory
• Computational Biology

Background:

• Cancer is a complex disease characterized by uncontrolled cell growth.
• Developing safe and effective cancer treatment strategies is a significant challenge.
• Existing methods may not fully balance treatment efficacy with patient safety.

Purpose of the Study:

• To design a safe and optimal control strategy for cancer treatment.
• To apply Control Barrier Function (CBF) techniques to cancer dynamics.
• To simultaneously optimize treatment efficacy and patient safety.

Main Methods:

• Formulating cancer dynamics as a control system.
• Developing a CBF-based controller to guide cancerous tissue behavior.
• Incorporating biological constraints into the control design.
• Simulating the controller's performance on tumor evolution.

Main Results:

• The CBF-based controller effectively guides cancerous tissue towards safe states.
• The strategy demonstrates simultaneous optimization of treatment efficacy and safety.
• Simulations confirm the controller's ability to respect predefined safety constraints.
• The approach shows potential for regulating tumor evolution within acceptable bounds.

Conclusions:

• Control Barrier Functions offer a promising framework for safe and optimal cancer control.
• This methodology provides a robust approach to managing cancer dynamics.
• The developed controller effectively balances therapeutic goals with patient safety constraints.