Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

Area of Science:

• Oncology
• Mathematical Biology
• Control Systems Engineering

Background:

• Tumorigenesis results from uncontrolled cell division, often managed with combination therapy.
• Concurrent drug administration can lead to significant toxicity and drug resistance.
• Sequential or alternating drug administration presents a promising alternative to simultaneous combination therapy.

Purpose of the Study:

• To investigate the feasibility of sequential drug administration for tumor treatment from a mathematical perspective.
• To propose a switched hybrid control systems framework for optimizing sequential drug delivery.
• To explore the impact of time-dependent drug switching strategies on tumor cell dynamics.

Main Methods:

• Utilizing a transit compartmentalized model to simulate tumor cell progression and death.
• Developing a time-dependent switching strategy for sequential drug administration.
• Applying a switched hybrid control systems framework to model the drug delivery dynamics.

Main Results:

• The proposed time-based drug switching logic effectively represses proliferating tumor cells.
• Simulation results demonstrate the efficacy of sequential drug intake in reducing tumor size.
• The framework provides a mathematical basis for the benefits of alternating drug administration.

Conclusions:

• Sequential drug administration, guided by a switched hybrid control systems framework, is a viable and effective strategy for cancer treatment.
• This approach offers a potential method to mitigate drug toxicity and overcome resistance.
• The study provides a novel mathematical framework for biomedical researchers and clinicians exploring sequential drug therapies.