Optimal control theory as a method for designing multidrug adaptive therapy regimens.

Area of Science:

• Oncology
• Mathematical Biology
• Computational Science

Background:

• Cancer treatment resistance is a significant clinical challenge.
• Developing effective multidrug regimens requires complex design strategies.
• Evolutionarily informed therapies offer novel approaches to combat resistance.

Purpose of the Study:

• To utilize optimal control theory (OCT) for designing a two-drug adaptive cancer therapy regimen.
• To identify design principles for effective adaptive cancer treatment regimens.
• To compare OCT-designed regimens against existing treatment strategies.

Main Methods:

• A logistic differential equation model was developed for a tumor with four populations exhibiting varying drug resistance.
• Optimal control theory (OCT) was applied to guide the adaptive therapy regimen design.
• Simulations were performed to evaluate different regimen designs and compare their performance.

Main Results:

• OCT analysis provided rules for creating effective cancer therapy regimens.
• Regimens that maintained competition between resistant and sensitive tumor populations demonstrated superior performance.
• The effectiveness of the strategy was robust across various model parameters.
• OCT-designed regimens outperformed standard-of-care regimens in simulations.

Conclusions:

• Optimal control theory (OCT) is a valuable tool for designing multidrug adaptive cancer therapy regimens.
• Maintaining competition within the tumor microenvironment is crucial for effective treatment.
• OCT-guided adaptive therapy shows promise for improving cancer treatment outcomes.