Speaker
Description
Dynamic mathematical models of the hypothalamic–pituitary–thyroid (HPT) axis provide a framework for studying thyroid physiology. A widely used model originally developed in \cite{A, B} has been refined and extended over the past two decades \cite{D}. This model captures the HPT feedback loop, including TSH, T4, T3, and additional regulatory factors \cite{A, D}.
The model was extended to simulate Graves’ disease (GD) and the effect of methimazole (MMI) \cite{E}. In this formulation, the influence of TSH receptor antibodies (TRAb) was not modeled explicitly but was represented by an increased secretory capacity of the thyroid gland. The thyroid secretion rate was fixed at a pathologically elevated level, representing a persistent hyperthyroid state.
In GD, however, TRAb constitutes the primary disease-driving factor. In the present work, we extended the widely used model by integrating mechanisms describing antibody-driven stimulation of the thyroid gland. To achieve this, the model was combined with the DigiThy simulation framework \cite{F}, which has been evaluated against clinical data from patients with GD and explicitly models TRAb dynamics. This enables the simulation of heterogeneous disease courses, including remission and sustained euthyroid states after discontinuation of antithyroid medication.
The resulting combined model integrates detailed thyroid hormone regulation with explicit modeling of antibody dynamics and disease progression in GD.
Bibliography
@book{A,
author = {Dietrich, Johannes W.},
title = {Der Hypophysen-Schilddrüsen-Regelkreis: Entwicklung und klinische Anwendung eines nichtlinearen Modells},
year = {2002},
publisher = {Logos-Verlag},
address = {Berlin},
isbn = {9783897228504},
pages = {195}
}
@article{B,
title = {Thyrotropic Feedback Control: Evidence for an Additional Ultrashort Feedback Loop from Fractal Analysis},
author = {Dietrich, J. W. and Tesche, A. and Pickardt, C. R. and Mitzdorf, U.},
journal = {Cybernetics and Systems},
volume = {35},
number = {4},
pages = {315--331},
year = {2004},
month = jun,
publisher = {Informa UK Limited},
doi = {10.1080/01969720490443354},
url = {http://dx.doi.org/10.1080/01969720490443354},
issn = {1087-6553}
}
@article{D,
title = {Mathematical Modeling of the Pituitary–Thyroid Feedback Loop: Role of a TSH-T3-Shunt and Sensitivity Analysis},
volume = {9},
ISSN = {1664-2392},
url = {http://dx.doi.org/10.3389/fendo.2018.00091},
DOI = {10.3389/fendo.2018.00091},
journal = {Frontiers in Endocrinology},
publisher = {Frontiers Media SA},
author = {Berberich, Julian and Dietrich, Johannes W. and Hoermann, Rudolf and M\"{u}ller, Matthias A.},
year = {2018},
month = mar
}
@ARTICLE{E,
title = "Modeling and predictive control for the treatment of
hyperthyroidism",
author = "Wolff, Tobias M and Menzel, Maylin and Dietrich, Johannes W and
M{\"u}ller, Matthias A",
journal = "IFAC-PapersOnLine",
publisher = "Elsevier BV",
volume = 59,
number = 1,
pages = "235--240",
year = 2025,
language = "en"
}
@ARTICLE{F,
AUTHOR={Theiler-Schwetz, Verena and Benninger, Thomas and Trummer, Christian and Pilz, Stefan and Reichhartinger, Markus },
TITLE={Mathematical Modeling of Free Thyroxine Concentrations During Methimazole Treatment for Graves’ Disease: Development and Validation of a Computer-Aided Thyroid Treatment Method},
JOURNAL={Frontiers in Endocrinology},
VOLUME={13},
YEAR={2022},
URL={https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.841888},
DOI={10.3389/fendo.2022.841888},
ISSN={1664-2392},
ABSTRACT={<sec><title>Background</title>
Methimazole (MMI) is the first-line treatment for patients with Graves’ disease (GD). While there are empirical recommendations for initial MMI doses, there is no clear guidance for subsequent MMI dose titrations. We aimed to (a) develop a mathematical model capturing the dynamics of free thyroxine (FT4) during MMI treatment (b), validate this model by use of numerical simulation in comparison with real-life patient data (c), develop the software application Digital Thyroid (DigiThy) serving either as a practice tool for treating virtual patients or as a decision support system with dosing recommendations for MMI, and (d) validate this software framework by comparing the efficacy of its MMI dosing recommendations with that from clinical endocrinologists.
</sec><sec><title>Methods</title>Based on concepts of automatic control and by use of optimization techniques, we developed two first order ordinary differential equations for modeling FT4 dynamics during MMI treatment. Clinical data from patients with GD derived from the outpatient clinic of Endocrinology at the Medical University of Graz, Austria, were used to develop and validate this model. It was subsequently used to create the web-based software application DigiThy as a simulation environment for treating virtual patients and an autonomous computer-aided thyroid treatment (CATT) method providing MMI dosing recommendations.
</sec><sec><title>Results</title>Based on MMI doses, concentrations of FT4, thyroid-stimulating hormone (TSH), and TSH-receptor antibodies (TRAb), a mathematical model with 8 patient-specific constants was developed. Predicted FT4 concentrations were not significantly different compared to the available consecutively measured FT4 concentrations in 9 patients with GD (52 data pairs, p=0.607). Treatment success of MMI dosing recommendations in 41 virtually generated patients defined by achieved target FT4 concentrations preferably with low required MMI doses was similar between CATT and usual care. Statistically, CATT was significantly superior (p<0.001).
</sec><sec><title>Conclusions</title>Our mathematical model produced valid FT4 predictions during MMI treatment in GD and provided the basis for the DigiThy application already serving as a training tool for treating virtual patients. Clinical trial data are required to evaluate whether DigiThy can be approved as a decision support system with automatically generated MMI dosing recommendations.
</sec>}}