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- Title
Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.
- Authors
Yu-Ping Wang; Xiao-Ling Lu; Kun Shao; Hao-Qiang Shi; Pei-Jun Zhou; Bing Chen
- 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 CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance. Conclusion: The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.
- Subjects
KIDNEY transplantation; MACHINE learning; TACROLIMUS; PHARMACOKINETICS; CYTOCHROME P-450 CYP3A; FORECASTING
- Publication
Frontiers in Pharmacology, 2024, p1
- ISSN
1663-9812
- Publication type
Article
- DOI
10.3389/fphar.2024.1389271