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A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic dru...

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Autores principales: Zhu, Xiuqing, Huang, Wencan, Lu, Haoyang, Wang, Zhanzhang, Ni, Xiaojia, Hu, Jinqing, Deng, Shuhua, Tan, Yaqian, Li, Lu, Zhang, Ming, Qiu, Chang, Luo, Yayan, Chen, Hongzhen, Huang, Shanqing, Xiao, Tao, Shang, Dewei, Wen, Yuguan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946912/
https://www.ncbi.nlm.nih.gov/pubmed/33692435
http://dx.doi.org/10.1038/s41598-021-85157-x
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author Zhu, Xiuqing
Huang, Wencan
Lu, Haoyang
Wang, Zhanzhang
Ni, Xiaojia
Hu, Jinqing
Deng, Shuhua
Tan, Yaqian
Li, Lu
Zhang, Ming
Qiu, Chang
Luo, Yayan
Chen, Hongzhen
Huang, Shanqing
Xiao, Tao
Shang, Dewei
Wen, Yuguan
author_facet Zhu, Xiuqing
Huang, Wencan
Lu, Haoyang
Wang, Zhanzhang
Ni, Xiaojia
Hu, Jinqing
Deng, Shuhua
Tan, Yaqian
Li, Lu
Zhang, Ming
Qiu, Chang
Luo, Yayan
Chen, Hongzhen
Huang, Shanqing
Xiao, Tao
Shang, Dewei
Wen, Yuguan
author_sort Zhu, Xiuqing
collection PubMed
description The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL(−1) g(−1) day), as illustrated by a minimal bias (mean relative error (%) =  + 3%), good precision (MAE = 8.7 μg mL(−1) g(−1) day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions.
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spelling pubmed-79469122021-03-12 A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters Zhu, Xiuqing Huang, Wencan Lu, Haoyang Wang, Zhanzhang Ni, Xiaojia Hu, Jinqing Deng, Shuhua Tan, Yaqian Li, Lu Zhang, Ming Qiu, Chang Luo, Yayan Chen, Hongzhen Huang, Shanqing Xiao, Tao Shang, Dewei Wen, Yuguan Sci Rep Article The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL(−1) g(−1) day), as illustrated by a minimal bias (mean relative error (%) =  + 3%), good precision (MAE = 8.7 μg mL(−1) g(−1) day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions. Nature Publishing Group UK 2021-03-10 /pmc/articles/PMC7946912/ /pubmed/33692435 http://dx.doi.org/10.1038/s41598-021-85157-x Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhu, Xiuqing
Huang, Wencan
Lu, Haoyang
Wang, Zhanzhang
Ni, Xiaojia
Hu, Jinqing
Deng, Shuhua
Tan, Yaqian
Li, Lu
Zhang, Ming
Qiu, Chang
Luo, Yayan
Chen, Hongzhen
Huang, Shanqing
Xiao, Tao
Shang, Dewei
Wen, Yuguan
A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title_full A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title_fullStr A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title_full_unstemmed A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title_short A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
title_sort machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946912/
https://www.ncbi.nlm.nih.gov/pubmed/33692435
http://dx.doi.org/10.1038/s41598-021-85157-x
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