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Precision medicine in hypothyroidism: an engineering approach to individualized levothyroxine dosing

  • Davide Manca ORCID logo EMAIL logo
Published/Copyright: April 2, 2025
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Abstract

This chapter presents an innovative engineering approach to precision medicine in the treatment of hypothyroidism. By leveraging mathematical modeling of the hypothalamic-pituitary-thyroid (HPT) axis, a novel algorithm, PzeroT, is developed for individualized optimization of levothyroxine dosing. The chapter explores the HPT axis physiology and pathology and the current state-of-the-art in hypothyroidism treatment. The core contribution lies in implementing a compartmental model of the HPT axis, which is the foundation for the PzeroT algorithm. This model-based approach enables the prediction of the optimal levothyroxine dose tailored to individual patient characteristics. The chapter further validates the PzeroT algorithm using real patient case studies, demonstrating its potential to improve hypothyroidism management by reducing treatment times and enhancing dose accuracy compared to standard clinical practices. This work, at the intersection of chemical engineering and medicine, exemplifies how engineering principles can advance precision medicine and paves the way for more personalized and effective treatment of thyroid disorders.


Corresponding author: Davide Manca, PSE-Lab, Process Systems Engineering Laboratory, Dipartimento di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy, E-mail:

Acknowledgments

The author acknowledges the valuable contribution of Federico Appiani and Giovanni Colombo, master’s degree students in Chemical Engineering at Politecnico di Milano, who worked with outstanding commitment and dedication to the discovery of thyroid pathologies and the modeling of the HPT axis, with particular attention to the condition of hypothyroidism and the identification of the optimal dose of levothyroxine.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Data available on request from the author.

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Received: 2024-10-15
Accepted: 2025-02-19
Published Online: 2025-04-02

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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