Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Mo, Xiaolan; Chen, Xiujuan; Zeng, Huasong; Zheng, Wei; Ieong, Chifong; Li, Huixian; Huang, Qiongbo; Xu, Zichuan; Yang, Jinlian; Liang, Qianying; Liang, Huiying; Gao, Xia; Huang, Min; Li, Jiali
• Is Part Of: Pharmacotherapy. Volume 43:Issue 1(2023)
• Page Start: 43
• Page End: 52
• Publication Date: 2022-12-31
• Subjects: childhood nephrotic syndrome -- machine learning -- prognostic and predictive biomarkers -- single‐nucleotide polymorphism -- tacrolimus; Chemotherapy -- Periodicals; Pharmacology -- Periodicals; Drug Therapy -- Periodicals; Pharmacology -- Periodicals; 615.1
• DOI: 10.1002/phar.2749
• Languages: English
• ISSNs: 0277-0008

Abstract

Abstract: Study Objective: The pharmacokinetics and pharmacodynamics of tacrolimus (TAC) vary greatly among individuals, hindering its precise utilization. Moreover, effective models for the early prediction of TAC efficacy in patients with nephrotic syndrome (NS) are lacking. We aimed to identify key factors affecting TAC efficacy and develop efficacy prediction models for childhood NS using machine learning algorithms. Design: This was an observational cohort study of patients with pediatric refractory NS. Setting: Guangzhou Women and Children's Medical Center between June 2013 and December 2018. Patients: 203 patients with pediatric refractory NS were used for model generation and 35 patients were used for model validation. Intervention: All patients regularly received double immunosuppressive therapy comprising TAC and low‐dose prednisone or methylprednisolone. In this observational cohort study of 203 pediatric patients with refractory NS, clinical and genetic variables, including single‐nucleotide polymorphism (SNPs), were identified. TAC efficacy was evaluated 3 months after administration according to two different evaluation criteria: response or non‐response (Group 1) and complete remission, partial remission, or non‐remission (Group 2). Measurements: Logistic regression, extremely random trees, gradient boosting decision trees, random forest, and extreme gradient boosting algorithms were used to develop and validate the models. Prediction models were validated among a cohort of 35 patients with NS. Main Results: The random forest models performed best in both groups, and the area under the receiver operating characteristics curve of these two models was 80.7% (Group 1) and 80.3% (Group 2). These prediction models included urine erythrocyte count before administration, steroid types, and eight SNPs ( ITGB4 rs2290460, TRPC6 rs3824934, CTGF rs9399005, IL13 rs20541, NFKBIA rs8904, NFKBIA rs8016947, MAP3K11 rs7946115, and SMARCAL1 rs11886806). Conclusions: Two pre‐administration models with good predictive performance for TAC response of patients with NS were developed and validated using machine learning algorithms. These accurate models could assist clinicians in predicting TAC efficacy in pediatric patients with NS before utilization to avoid treatment failure or adverse effects.