Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

被引:1
|
作者
Wang, Yu-Ping [1 ]
Lu, Xiao-Ling [1 ]
Shao, Kun [2 ]
Shi, Hao-Qiang [1 ]
Zhou, Pei-Jun [2 ]
Chen, Bing [1 ]
机构
[1] Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med, Dept Pharm, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Ruijin Hosp, Ctr Organ Transplantat, Sch Med, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
renal transplant recipients; population pharmacokinetic; machine learning; XGBboost; tacrolimus; CYP3A5; GENOTYPE; GENETIC-POLYMORPHISM; BAYESIAN-ESTIMATION; CLINICAL FACTORS; EARLY PERIOD; EXPOSURE; OUTCOMES; IMPACT;
D O I
10.3389/fphar.2024.1389271
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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.
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收藏
页数:11
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