Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization
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 t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Royal Society
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6597767/ https://www.ncbi.nlm.nih.gov/pubmed/31238837 http://dx.doi.org/10.1098/rsif.2019.0083 |
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author | Meng, Fanwen Li, Enlin Yen, Paul Michael Leow, Melvin Khee Shing |
author_facet | Meng, Fanwen Li, Enlin Yen, Paul Michael Leow, Melvin Khee Shing |
author_sort | Meng, Fanwen |
collection | PubMed |
description | 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 IC(50). 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. |
format | Online Article Text |
id | pubmed-6597767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-65977672019-07-01 Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization Meng, Fanwen Li, Enlin Yen, Paul Michael Leow, Melvin Khee Shing J R Soc Interface Life Sciences–Mathematics interface 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 IC(50). 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. The Royal Society 2019-06 2019-06-26 /pmc/articles/PMC6597767/ /pubmed/31238837 http://dx.doi.org/10.1098/rsif.2019.0083 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Meng, Fanwen Li, Enlin Yen, Paul Michael Leow, Melvin Khee Shing Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title | Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title_full | Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title_fullStr | Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title_full_unstemmed | Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title_short | Hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
title_sort | hyperthyroidism in the personalized medicine era: the rise of mathematical optimization |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6597767/ https://www.ncbi.nlm.nih.gov/pubmed/31238837 http://dx.doi.org/10.1098/rsif.2019.0083 |
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