ArticleFrontiers in pharmacology2024
Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- Narrow Therapeutic Index Drugs: Regulatory Frameworks, Bioequivalence, and Model-Informed Precision Dosing.Pharmaceutics · 2026Review
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Initial Dosage Optimization of Tacrolimus in Connective Tissue Disease-Associated Interstitial Lung Disease Patients: An Exploratory Study.Drug design, development and therapy · 2026Article
- Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.Frontiers in immunology · 2026Review
- Review
- Machine Learning-Based Algorithm for Tacrolimus Dose Optimization in Hospitalized Kidney Transplant Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.European journal of drug metabolism and pharmacokinetics · 2025Article
- Global research on the utilization of population pharmacokinetic model: a bibliometric analysis from 2000 to 2024.Frontiers in pharmacology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients. Methods: Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group. Results: The final PPK model, incorporating hematocrit and Conclusion: The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.
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Registered trials
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