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Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Affiliation
Department of Pharmacy ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Huang, Qiongbo;
Affiliation
Department of Pharmacy ,The First Affiliated Hospital of Sun Yat-sen University ,Guangzhou ,China
Lin, Xiaobin;
Affiliation
Department of Clinical Pharmacy ,Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital) ,Tongji Medical College ,Huazhong University of Science and Technology ,Wuhan ,China
Wang, Yang;
Affiliation
Department of Medical Big Data Center ,Guangdong Provincial People’s Hospital ,Guangdong Academy of Medical Sciences ,Guangzhou ,China
Chen, Xiujuan;
Affiliation
Department of Pharmacy ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Zheng, Wei;
Affiliation
Institute of Clinical Pharmacology ,School of Pharmaceutical Sciences ,Sun Yat-sen University ,Guangzhou ,China
Zhong, Xiaoli;
Affiliation
Department of Pharmacy ,The Affiliated Brain Hospital of Guangzhou Medical University ,Guangzhou ,China
Shang, Dewei;
Affiliation
Institute of Clinical Pharmacology ,School of Pharmaceutical Sciences ,Sun Yat-sen University ,Guangzhou ,China
Huang, Min;
Affiliation
Division of Nephrology ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Gao, Xia;
Affiliation
Division of Nephrology ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Deng, Hui;
Affiliation
Institute of Clinical Pharmacology ,School of Pharmaceutical Sciences ,Sun Yat-sen University ,Guangzhou ,China
Li, Jiali;
Affiliation
Department of Pharmacy ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Zeng, Fangling;
Affiliation
Department of Pharmacy ,Guangzhou Women and Children’s Medical Center ,Guangzhou Medical University ,Guangzhou ,China
Mo, Xiaolan

Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC. Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9 , LAMB2 , ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance. Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R 2 = 0.42). Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

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License Holder: Copyright © 2022 Huang, Lin, Wang, Chen, Zheng, Zhong, Shang, Huang, Gao, Deng, Li, Zeng and Mo.

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