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

被引:16
作者
Huang, Qiongbo [1 ]
Lin, Xiaobin [2 ]
Wang, Yang [3 ]
Chen, Xiujuan [4 ]
Zheng, Wei [1 ]
Zhong, Xiaoli [5 ]
Shang, Dewei [6 ]
Huang, Min [5 ]
Gao, Xia [7 ]
Deng, Hui [7 ]
Li, Jiali [5 ]
Zeng, Fangling [1 ]
Mo, Xiaolan [1 ]
机构
[1] Guangzhou Med Univ, Guangzhou Women & Childrens Med Ctr, Dept Pharm, Guangzhou, Peoples R China
[2] Guangzhou Med Univ, Dept Pharm, Affiliated Brain Hosp 1, Guangzhou, Peoples R China
[3] Huazhong Univ Sci & Technol, Wuhan Childrens Hosp, Wuhan Maternal & Child Healthcare Hosp, Tongji Med Coll,Dept Clin Pharm, Wuhan, Peoples R China
[4] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Med Big Data Ctr, Guangzhou, Peoples R China
[5] Sun Yat sen Univ, Inst Clin Pharmacol, Sch Pharmaceut Sci, Guangzhou, Peoples R China
[6] Guangzhou Med Univ, Affiliated Brain Hosp, Dept Pharm, Guangzhou, Peoples R China
[7] Guangzhou Med Univ, Guangzhou Women & Childrens Med Ctr, Div Nephrol, Guangzhou, Peoples R China
关键词
tacrolimus; pediatric nephrotic syndrome; population pharmacokinetic; machine learning; gene polymorphisms; GUIDELINES;
D O I
10.3389/fphar.2022.942129
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 38 条
  • [1] ABDELRAZZAK Z, 1993, MOL PHARMACOL, V44, P707
  • [2] Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics
    Agrawal, Shipra
    Merchant, Michael L.
    Kino, Jiro
    Li, Ming
    Wilkey, Daniel W.
    Gaweda, Adam E.
    Brier, Michael E.
    Chanley, Melinda A.
    Gooding, Jessica R.
    Sumner, Susan J.
    Klein, Jon B.
    Smoyer, William E.
    Mahan, John
    Patel, Hiren
    Ransom, Richard F.
    Pan, Cynthia
    Geary, Denis F.
    Chang, Myra L.
    Gibson, Keisha L.
    Iorember, Franca M.
    Brophy, Patrick D.
    Srivastava, Tarak
    Greenbaum, Larry A.
    [J]. KIDNEY INTERNATIONAL REPORTS, 2020, 5 (01): : 66 - 80
  • [3] Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing
    Birdwell, K. A.
    Decker, B.
    Barbarino, J. M.
    Peterson, J. F.
    Stein, C. M.
    Sadee, W.
    Wang, D.
    Vinks, A. A.
    He, Y.
    Swen, J. J.
    Leeder, J. S.
    van Schaik, R. H. N.
    Thummel, K. E.
    Klein, T. E.
    Caudle, K. E.
    MacPhee, I. A. M.
    [J]. CLINICAL PHARMACOLOGY & THERAPEUTICS, 2015, 98 (01) : 19 - 24
  • [4] Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics
    Bon, C.
    Toutain, P. L.
    Concordet, D.
    Gehring, R.
    Martin-Jimenez, T.
    Smith, J.
    Pelligand, L.
    Martinez, M.
    Whittem, T.
    Riviere, J. E.
    Mochel, J. P.
    [J]. JOURNAL OF VETERINARY PHARMACOLOGY AND THERAPEUTICS, 2018, 41 (02) : 171 - 183
  • [5] Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?
    Campagne, Olivia
    Mager, Donald E.
    Tornatore, Kathleen M.
    [J]. JOURNAL OF CLINICAL PHARMACOLOGY, 2019, 59 (03) : 309 - 325
  • [6] Optimization of initial dosing scheme of tacrolimus in pediatric refractory nephrotic syndrome patients based on CYP3A5 genotype and coadministration with wuzhi-capsule
    Chen, Xiao
    Wang, Dong-Dong
    Xu, Hong
    Li, Zhi-Ping
    [J]. XENOBIOTICA, 2020, 50 (05) : 606 - 613
  • [7] Effects and safety evaluation of Wuzhi Capsules combined with tacrolimus for the treatment of kidney transplantation recipients
    Cheng, Fang
    Li, Qiang
    Wang, Jinglin
    Zeng, Fang
    Zhang, Yu
    [J]. JOURNAL OF CLINICAL PHARMACY AND THERAPEUTICS, 2021, 46 (06) : 1636 - 1649
  • [8] T cell anergy, exhaustion, senescence, and stemness in the tumor microenvironment
    Crespo, Joel
    Sun, Haoyu
    Welling, Theodore H.
    Tian, Zhigang
    Zou, Weiping
    [J]. CURRENT OPINION IN IMMUNOLOGY, 2013, 25 (02) : 214 - 221
  • [9] Decrease in hepatic cytochrome P450 after interleukin-2 Immunotherapy
    Elkahwaji, J
    Robin, MA
    Berson, A
    Tinel, M
    Lettéron, P
    Labbe, G
    Beaune, P
    Elias, D
    Rougier, P
    Escudier, B
    Duvillard, P
    Pessayre, D
    [J]. BIOCHEMICAL PHARMACOLOGY, 1999, 57 (08) : 951 - 954
  • [10] A Theoretical Physiologically-Based Pharmacokinetic Approach to Ascertain Covariates Explaining the Large Interpatient Variability in Tacrolimus Disposition
    Emoto, Chie
    Johnson, Trevor N.
    Hahn, David
    Christians, Uwe
    Alloway, Rita R.
    Vinks, Alexander A.
    Fukuda, Tsuyoshi
    [J]. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 2019, 8 (05): : 273 - 284