ArticleClinical and translational science2025
Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study.
Article in Clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Pre-Analytical Stability of Mycophenolic Acid and Its Glucuronide in Saliva Samples Stored in Cotton SalivettePharmaceutics · 2026Article
- Diversity in Translation: Equal Is Greater Than.Clinical and translational science · 2026Article
- Plasma-saliva correlations for total and unbound mycophenolic acid and its glucuronide in children with nephrotic syndrome.Pediatric nephrology (Berlin, Germany) · 2026Article
- Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study.Clinical and translational science · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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 AUC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.