A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
Abstract 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 therap...
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2021-03-01
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doaj-36e436b9525e4b3b9f9f54c1a7914a6c2021-03-11T12:18:57ZengNature Publishing GroupScientific Reports2045-23222021-03-0111111410.1038/s41598-021-85157-xA machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parametersXiuqing Zhu0Wencan Huang1Haoyang Lu2Zhanzhang Wang3Xiaojia Ni4Jinqing Hu5Shuhua Deng6Yaqian Tan7Lu Li8Ming Zhang9Chang Qiu10Yayan Luo11Hongzhen Chen12Shanqing Huang13Tao Xiao14Dewei Shang15Yuguan Wen16Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, Guangzhou Bureau of Civil Affairs Psychiatric HospitalDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong Engineering Technology Research Center for Translational Medicine of Mental DisordersDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Abstract 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.https://doi.org/10.1038/s41598-021-85157-x |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiuqing Zhu Wencan Huang Haoyang Lu Zhanzhang Wang Xiaojia Ni Jinqing Hu Shuhua Deng Yaqian Tan Lu Li Ming Zhang Chang Qiu Yayan Luo Hongzhen Chen Shanqing Huang Tao Xiao Dewei Shang Yuguan Wen |
spellingShingle |
Xiuqing Zhu Wencan Huang Haoyang Lu Zhanzhang Wang Xiaojia Ni Jinqing Hu Shuhua Deng Yaqian Tan Lu Li Ming Zhang Chang Qiu Yayan Luo Hongzhen Chen Shanqing Huang Tao Xiao Dewei Shang Yuguan Wen A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters Scientific Reports |
author_facet |
Xiuqing Zhu Wencan Huang Haoyang Lu Zhanzhang Wang Xiaojia Ni Jinqing Hu Shuhua Deng Yaqian Tan Lu Li Ming Zhang Chang Qiu Yayan Luo Hongzhen Chen Shanqing Huang Tao Xiao Dewei Shang Yuguan Wen |
author_sort |
Xiuqing Zhu |
title |
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_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_sort |
machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-03-01 |
description |
Abstract 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. |
url |
https://doi.org/10.1038/s41598-021-85157-x |
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