Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study
Abstract Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration‐time curve (AUC), causing pat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Clinical and Translational Science |
| Online Access: | https://doi.org/10.1111/cts.70092 |
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| author | Ping Zheng Ting Pan Ya Gao Juan Chen Liren Li Yan Chen Dandan Fang Xuechun Li Fei Gao Yilei Li |
| author_facet | Ping Zheng Ting Pan Ya Gao Juan Chen Liren Li Yan Chen Dandan Fang Xuechun Li Fei Gao Yilei Li |
| author_sort | Ping Zheng |
| collection | DOAJ |
| description | Abstract Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration‐time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. Pediatric autoimmune patients' data were collected at Nanfang Hospital between June 2018 and June 2023. Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. A total of 614 MPA AUC0‐12h samples from 209 patients were enrolled. Among the 10 models evaluated, the Wide&Deep model exhibited the best predictive performance. The predictive performance of the Wide&Deep model using four and three blood concentration points was similar (R 2 ≈ 1 for four points; R 2 = 0.95 for three points). No significant difference in accuracy within ±30% was observed between models utilizing three and four blood concentration points (p = 0.06). This study demonstrates that in the Wide&Deep model, MPA exposure can be accurately estimated with three sampling points in children with autoimmune diseases. This model could help reduce discomfort in pediatric patients without reducing the accuracy of MPA exposure estimates in clinical practice. |
| format | Article |
| id | doaj-art-ce722c88d062446db726aed49fc2d7a4 |
| institution | Kabale University |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-ce722c88d062446db726aed49fc2d7a42025-01-24T08:17:46ZengWileyClinical and Translational Science1752-80541752-80622025-01-01181n/an/a10.1111/cts.70092Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective studyPing Zheng0Ting Pan1Ya Gao2Juan Chen3Liren Li4Yan Chen5Dandan Fang6Xuechun Li7Fei Gao8Yilei Li9Department of Pharmacy Nanfang Hospital, Southern Medical University Guangzhou ChinaSecond Affiliated Hospital to Naval Medical University Shanghai ChinaDepartment of Pharmacy Fuwai Hospital, Chinese Academy of Medical Sciences Beijing ChinaDepartment of Pharmacy Nanfang Hospital, Southern Medical University Guangzhou ChinaDepartment of Pharmacy Nanfang Hospital, Southern Medical University Guangzhou ChinaDepartment of Pharmacy Nanfang Hospital, Southern Medical University Guangzhou ChinaBeijing Medicinovo Technology Co. Ltd Beijing ChinaDalian Medicinovo Technology Co. Ltd Dalian ChinaBeijing Medicinovo Technology Co. Ltd Beijing ChinaDepartment of Pharmacy Nanfang Hospital, Southern Medical University Guangzhou ChinaAbstract Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration‐time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. Pediatric autoimmune patients' data were collected at Nanfang Hospital between June 2018 and June 2023. Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. A total of 614 MPA AUC0‐12h samples from 209 patients were enrolled. Among the 10 models evaluated, the Wide&Deep model exhibited the best predictive performance. The predictive performance of the Wide&Deep model using four and three blood concentration points was similar (R 2 ≈ 1 for four points; R 2 = 0.95 for three points). No significant difference in accuracy within ±30% was observed between models utilizing three and four blood concentration points (p = 0.06). This study demonstrates that in the Wide&Deep model, MPA exposure can be accurately estimated with three sampling points in children with autoimmune diseases. This model could help reduce discomfort in pediatric patients without reducing the accuracy of MPA exposure estimates in clinical practice.https://doi.org/10.1111/cts.70092 |
| spellingShingle | Ping Zheng Ting Pan Ya Gao Juan Chen Liren Li Yan Chen Dandan Fang Xuechun Li Fei Gao Yilei Li Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study Clinical and Translational Science |
| title | Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study |
| title_full | Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study |
| title_fullStr | Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study |
| title_full_unstemmed | Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study |
| title_short | Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study |
| title_sort | predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy a retrospective study |
| url | https://doi.org/10.1111/cts.70092 |
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