Population Pharmacokinetics of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia - Gao, Xuan; Qian, Xiao-Wen; Zhu, Xiao-Hua; Yu, Yi; Miao, Hui; Meng, Jian-Hua; Jiang, Jun-Ye; Wang, Hong-Sheng; Zhai, Xiao-Wen (Frontiers in Pharmacology, 2021)
High-dose methotrexate (HD-MTX) is widely used in pediatric acute lymphoblastic leukemia (ALL) treatment regimens. In this study, we aimed to develop a population pharmacokinetic (PK) model of HD-MTX in Chinese pediatric patients with ALL for designing personalized dosage regimens. In total, 4,517 MTX serum concentration data for 311 pediatric patients with ALL, aged 0.75–15.2 years and under HD-MTX treatment, were retrospectively collected at a tertiary Children’s Hospital in China. The non-linear mixed-effect model was used to establish the population PK model, using NONMEM software. The potential covariate effects of age, body weight, and biochemical measurements (renal and liver function) on MTX PK disposition were investigated. The model was then evaluated using goodness-of-fit, visual predictive check. MTX PK disposition was described using a three-compartment model reasonable well. Body weight, implemented as a fixed allometric function on all clearance and volume of distribution parameters, showed a substantial improvement in model fit. The final population model demonstrated that the MTX clearance estimate in a typical child with body weight of 19 kg was 6.9 L/h and the central distribution of volume estimate was 20.7 L. The serum creatinine significantly affected the MTX clearance, with a 0.97% decrease in clearance per 1 μmol/L of serum creatinine. Other covariates (e.g., age, sex, bilirubin, albumin, aspartate transaminase, concomitant medication) did not significantly affect PK properties of MTX. The proposed population PK model could describe the MTX concentration data in Chinese pediatric patients with ALL. This population PK model combined with a maximum a posteriori Bayesian approach could be used to estimate individual PK parameters, and optimize personalized MTX therapy in target patients, thus aiming to reduce toxicity and improve treatment outcomes.