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Optimal control applications in the chemotherapy of multiple myeloma
Abstract:
There is an increasing interest in the use of therapeutic devices which deliver chemotherapeutic agents in a continuous manner. In this paper the Gompertz model of cancer growth with a loss term depending on a cancer chemotherapeutic agent is applied to human multiple myeloma. Three different performance criteria are introduced which measure the influence of the anti-cancer drug in driving the tumor population level to a desired target level. Engineering optimal control theory is used to produce expressions for the continuous-time optimal control. A comparison is made between the natures of the controller for the three problems considered. Parameter values used in the models are based on patient data. Results of the present study may be useful in the construction of algorithms for use with drug delivery devices that incorporate a microprocessor. Use of such devices may be useful in improving the treatment schedules and treatment outcome of cancer patients.
Insights
This study applies the Gompertz model to multiple myeloma, using optimal control theory to design continuous chemotherapy delivery. This research aids in developing smart drug delivery devices for improved cancer treatment outcomes.
Area of Science:
- Mathematical Oncology
- Biomedical Engineering
- Pharmacodynamics
Background:
- Continuous chemotherapy delivery is gaining interest for improved cancer treatment.
- Mathematical models are crucial for optimizing drug delivery strategies.
- Human multiple myeloma serves as a relevant model for studying chemotherapy efficacy.
Purpose of the Study:
- To apply the Gompertz model to human multiple myeloma, incorporating a chemotherapeutic agent loss term.
- To develop optimal control strategies for continuous drug delivery systems.
- To evaluate the influence of anti-cancer drugs in reaching a target tumor population level.
Main Methods:
- Utilizing the Gompertz model of cancer growth.
- Incorporating a loss term dependent on chemotherapeutic agents.
- Applying engineering optimal control theory for continuous-time optimal control.
- Analyzing three distinct performance criteria for drug efficacy.
Main Results:
- Expressions for continuous-time optimal control were derived.
- A comparison of controller behaviors across different performance criteria was conducted.
- Model parameters were informed by patient data for human multiple myeloma.
Conclusions:
- The derived optimal control expressions can inform algorithms for microprocessor-controlled drug delivery devices.
- Optimized drug delivery schedules may lead to improved treatment outcomes for cancer patients.
- This approach offers a pathway to enhance therapeutic device efficacy in oncology.