Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model.

Area of Science:

• Pharmacology and Computational Biology
• Oncology and Control Systems Engineering

Background:

• Optimizing chemotherapy dosing is crucial for effective cancer treatment.
• Existing models may not fully capture the complexities of drug-cell interactions.
• Cell-cycle specific drug effects require advanced control strategies.

Purpose of the Study:

• To develop and evaluate a nonlinear model predictive control (NMPC) algorithm for tamoxifen dosing.
• To utilize a novel saturating-rate, cell-cycle model (SCM) for enhanced control.
• To assess the algorithm's performance in reducing tumor volume in simulated animals.

Main Methods:

• Development of a nonlinear model predictive control (NMPC) algorithm.
• Implementation of a novel saturating-rate, cell-cycle model (SCM) for tamoxifen.
• Simulation of tumor volume reduction over 4 months using daily measurements.

Main Results:

• The NMPC algorithm successfully decreased tumor volume along a reference trajectory in simulations.
• Controllers using the SCM and Gompertz model (GM) showed comparable drug delivery and convergence times.
• Performance was influenced by the cell-cycle phase of drug action.

Conclusions:

• The NMPC algorithm is well-suited for optimizing the dosing of chemotherapeutics like tamoxifen.
• The approach is adaptable for other regularly administered chemotherapeutic agents.
• This control strategy offers a potential advancement in personalized cancer therapy.