Model predictive control for the prescription of antithyroid agents

TL;DR

This paper introduces a model predictive control approach to optimize the dosage of antithyroid medication, specifically methimazole, for hyperthyroidism treatment, moving beyond traditional trial-and-error methods.

Contribution

It extends a mathematical model of the pituitary-thyroid feedback loop to include medication intake and applies MPC for personalized dosage determination.

Findings

MPC effectively personalizes drug dosing.

The extended model accurately predicts thyroid hormone levels.

Potential to improve hyperthyroidism treatment outcomes.

Abstract

Although hyperthyroidism is a common disease, the pharmaceutical therapy is based on a trial-and-error approach. We extend a mathematical model of the pituitary-thyroid feedback loop such that the intake of one antithyroid agent, namely methimazole (MMI), can be considered and use a model predictive control (MPC) scheme to determine suitable dosages.