Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach

Xiaolan Mo; Xiujuan Chen; Xianggui Wang; Xiaoli Zhong; Huiying Liang; Yuanyi Wei; Houliang Deng; Rong Hu; Tao Zhang; Yilu Chen; Xia Gao; Min Huang; Jiali Li

doi:10.2147/PGPM.S339318

Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach

Pharmgenomics Pers Med. 2022 Feb 22:15:143-155. doi: 10.2147/PGPM.S339318. eCollection 2022.

Authors

Xiaolan Mo^#^{1

2}, Xiujuan Chen^#³, Xianggui Wang^#², Xiaoli Zhong², Huiying Liang³, Yuanyi Wei¹, Houliang Deng¹, Rong Hu¹, Tao Zhang¹, Yilu Chen¹, Xia Gao⁴, Min Huang², Jiali Li²

Affiliations

¹ Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, People's Republic of China.
² Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
³ Department of clinical Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People's Republic of China.
⁴ Division of Nephrology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C₀/D) in Chinese children with refractory NS, and then develop and validate the TAC C₀/D prediction models.

Patients and methods: The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C₀/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients.

Results: GBDT algorithm performed best in the whole group (R²=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R²=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R²=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs (ACTN4 rs3745859, ACTN4 rs56113315, ACTN4 rs62121818, CTLA4 rs4553808, CYP3A5 rs776746, IL2RA rs12722489, INF2 rs1128880, MAP3K11 rs7946115, MYH9 rs2239781, and MYH9 rs4821478).

Conclusion: The association between the clinical and genetic variables and TAC C₀/D was described, and three TAC C₀/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.

Keywords: genetic polymorphism; machine learning; nephrotic syndrome; prediction model; tacrolimus.

Grants and funding

This research was supported by grants from the Natural Science Foundation of Guangdong Province (No. 2021A1515011308), Guangzhou Municipal Science and Technology Bureau (No. 202102010237), Traditional Chinese Medicine Bureau of Guangdong Province (No. 20201302), “Hospital pharmacy” Research fund of Guangdong Pharmaceutical Association (No. 2021A35), and Guangzhou Institute of Pediatrics/Guangzhou Women and Children’s Medical Center (No. GWCMC2020LH-3-003).