Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization.

Published

Journal Article

Thyroid over-activity or hyperthyroidism constitutes a significant morbidity afflicting the world. The current medical practice of dose titration of anti-thyroid drug (ATD) treatment for hyperthyroidism is relatively archaic, being based on arbitrary and time-consuming trending of thyroid function that requires multiple clinic monitoring visits before an optimal dose is found. This prompts a re-examination into more deterministic and efficient treatment approaches in the present personalized medicine era. Our research project seeks to develop a personalized medicine model that facilitates optimal drug dosing via the titration regimen. We analysed 49 patients' data consisting of drug dosage, time period and serum free thyroxine (FT4). Ordinary differential equation modelling was applied to describe the dynamic behaviour of FT4 concentration. With each patient's data, an optimization model was developed to determine parameters of synthesis rate, decay rate and IC50. We derived the closed-form time- and dose-dependent solution which allowed explicit estimates of personalized predicted FT4. Our equation system involving time, drug dosage and FT4 can be solved for any variable provided the values of the other two are known. Compared against actual FT4 data within a tolerance, we demonstrated the feasibility of predicting the FT4 subsequent to any prescribed dose of ATD with favourable accuracy using the initial three to five patient-visits' data respectively. This proposed mathematical model may assist clinicians in rapid determination of optimal ATD doses within allowable prescription limits to achieve any desired FT4 within a specified treatment period to accelerate the attainment of euthyroid targets.

Full Text

Duke Authors

Cited Authors

  • Meng, F; Li, E; Yen, PM; Leow, MKS

Published Date

  • June 28, 2019

Published In

Volume / Issue

  • 16 / 155

Start / End Page

  • 20190083 -

PubMed ID

  • 31238837

Pubmed Central ID

  • 31238837

Electronic International Standard Serial Number (EISSN)

  • 1742-5662

Digital Object Identifier (DOI)

  • 10.1098/rsif.2019.0083

Language

  • eng

Conference Location

  • England