Finite-Set Model Predictive Control of Melanoma Cancer Treatment Using Signaling Pathway Inhibitor of Cancer Stem...

Area of Science:

• Oncology
• Mathematical Biology
• Pharmacology

Background:

• Optimizing cancer drug delivery is crucial for patient survival and minimizing side effects.
• Mathematical modeling and control theory offer personalized treatment planning.
• Drug resistance presents a significant challenge in cancer therapy, potentially leading to treatment failure.

Purpose of the Study:

• To propose a mathematical model for melanoma tumor cell growth dynamics.
• To investigate the efficacy of dual antiplatelet therapy (DAPT) in reducing tumor growth rate.
• To develop an individualized optimal drug delivery plan using finite-set model predictive control.

Main Methods:

• A mathematical model was developed to describe melanoma tumor cell growth.
• Model parameters were identified using experimental data from 13 male nude mice with induced melanoma cancer undergoing DAPT.
• Finite-set model predictive control was employed to determine optimal drug delivery strategies.

Main Results:

• The study successfully identified model parameters based on experimental data.
• The mathematical model demonstrated the ability to estimate the impact of DAPT on melanoma treatment.
• Individualized optimal drug delivery plans were generated for each animal model.

Conclusions:

• The proposed mathematical model effectively estimates drug effects in melanoma cancer treatment.
• Individualized optimal drug delivery planning is feasible using model predictive control.
• This approach holds promise for improving cancer therapy outcomes by addressing drug resistance and optimizing treatment regimens.