Related Experiment Video
Updated: May 21, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
Published on: February 6, 2019
Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation
Tao Chen1, Norman F Kirkby, Raj Jena
1Division of Civil, Chemical and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK. t.chen@surrey.ac.uk
Abstract:
Model predictive control (MPC), originally developed in the community of industrial process control, is a potentially effective approach to optimal scheduling of cancer therapy. The basis of MPC is usually a state-space model (a system of ordinary differential equations), whereby existing studies usually assume that the entire states can be directly measured. This paper aims to demonstrate that when the system states are not fully measurable, in conjunction with model parameter discrepancy, MPC is still a useful method for cancer treatment. This aim is achieved through the application of moving horizon estimation (MHE), an optimisation-based method to jointly estimate the system states and parameters. The effectiveness of the MPC-MHE scheme is illustrated through scheduling the dose of tamoxifen for simulated tumour-bearing patients, and the impact of estimation horizon and magnitude of parameter discrepancy is also investigated.
Related Concept Videos
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses
Cancer Survival Analysis
Pharmacodynamic Models: Overview