ArticleDrug design, development and therapy2026
An Integrated Population Pharmacokinetic and Machine Learning Model for Predicting Tacrolimus Exposure in Adult Patients with Nephrotic Syndrome.
Article in Drug design, development and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrated Transcriptomic and Machine Learning Analyses Identify KCNN3 and TLR10 as Candidate Cell-Type-Associated Molecules in Idiopathic Membranous Nephropathy.International journal of general medicine · 2026Article
- Initial Dosage Optimization of Tacrolimus in Connective Tissue Disease-Associated Interstitial Lung Disease Patients: An Exploratory Study.Drug design, development and therapy · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
Purpose: Tacrolimus (TAC) is a calcineurin inhibitor widely used for immunosuppressive therapy. It has a narrow therapeutic window and substantial interindividual variability, making therapeutic drug monitoring (TDM) essential. Traditional population pharmacokinetic (PPK) models have limited capacity to capture complex nonlinear relationships and multifactorial interactions. This study aimed to integrate PPK and machine learning (ML) to predict TAC plasma concentration. Patients and Methods: A total of 182 consecutive patients with nephrotic syndrome (NS) who received oral TAC therapy and underwent TDM and CYP3A5 genotyping at the First Affiliated Hospital of Xinjiang Medical University between January 2018 and December 2023 were enrolled. Individual PK parameters derived from the PPK model were incorporated as new features into ML models. Feature selection was performed using recursive feature elimination with cross-validation (RFECV), and the union of features selected by the three models with the highest Results: Incorporating PK parameters resulted in a consistent improvement in predictive performance compared with the model without PK parameters (R Conclusion: Incorporating PK parameters enhanced model predictive performance, providing quantitative support for individualized TAC dosing and facilitating safer and more precise clinical applications.
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