Presentation Information
[C12-02]Optimal Control of Chemotherapy Scheduling for Tumor Growth Dynamics
*Yunil Roh1, Il Hyo Jung1 (1. Pusan National University (Korea))
Keywords:
Chemotherapy Schedule,Optimal Control,Tumor Growth
Cancer is a complex and heterogeneous disease that continues to pose a major global health challenge. While chemotherapy remains one of the most widely used treatment modalities, its clinical effectiveness is often limited by issues such as drug resistance, systemic toxicity, and suboptimal dosing strategies. These challenges underscore the need for quantitatively grounded approaches that can optimize treatment regimens and improve therapeutic outcomes.
Mathematical modeling offers a powerful framework to analyze and predict tumor dynamics, evaluate drug efficacy, and design better treatment strategies. Integrating pharmacokinetics/pharmacodynamics (PK/PD) with tumor growth models provides a mechanistic basis for capturing both the time-course of drug concentration and its biological effects on cancer cells. Such models enable a more comprehensive understanding of how dosing strategies influence treatment response.
In this talk, we construct a mathematical model that integrates PK/PD with tumor growth inhibition to assess treatment efficacy in a mechanistic and quantitative manner. The PK component captures the drug absorption and clearance, while the PD component models the drug’s impact on tumor cell dynamics. This coupled system reflects both biological and therapeutic processes.
We formulate an optimal control problem based on this model, where the control variables represent time-dependent drug dosing. The objective is to determine treatment schedules that minimize tumor burden and treatment cost, balancing therapeutic effects and drug-related toxicity. By applying Pontryagin’s Maximum Principle and conducting numerical simulations, we identify optimal dosing regimens and compare them with standard protocols.
Our results demonstrate that model-based optimal control can inform the design of personalized and more effective treatment strategies. This approach offers a promising direction for improving clinical decision-making in oncology through mathematically guided therapy optimization.
Mathematical modeling offers a powerful framework to analyze and predict tumor dynamics, evaluate drug efficacy, and design better treatment strategies. Integrating pharmacokinetics/pharmacodynamics (PK/PD) with tumor growth models provides a mechanistic basis for capturing both the time-course of drug concentration and its biological effects on cancer cells. Such models enable a more comprehensive understanding of how dosing strategies influence treatment response.
In this talk, we construct a mathematical model that integrates PK/PD with tumor growth inhibition to assess treatment efficacy in a mechanistic and quantitative manner. The PK component captures the drug absorption and clearance, while the PD component models the drug’s impact on tumor cell dynamics. This coupled system reflects both biological and therapeutic processes.
We formulate an optimal control problem based on this model, where the control variables represent time-dependent drug dosing. The objective is to determine treatment schedules that minimize tumor burden and treatment cost, balancing therapeutic effects and drug-related toxicity. By applying Pontryagin’s Maximum Principle and conducting numerical simulations, we identify optimal dosing regimens and compare them with standard protocols.
Our results demonstrate that model-based optimal control can inform the design of personalized and more effective treatment strategies. This approach offers a promising direction for improving clinical decision-making in oncology through mathematically guided therapy optimization.