Population pharmacokinetics of high-dose methotrexate in patients with primary central nervous system lymphoma - Wei, Shifeng; Zhang, Sitian; Wang, Dan; Zhang, Dongjie; Lu, Qian; Mo, Jiayi; Yang, Zhilin; Guan, Leyi; He, Yingjun; Zhao, Zhigang; Mei, Shenghui (Frontiers in Pharmacology, 2025)
ObjectiveMethotrexate (MTX) serves as a cornerstone therapy for primary central nervous system lymphoma (PCNSL). However, the considerable intra- and inter-individual variability in its pharmacokinetic and therapeutic efficacy poses significant challenges to clinical application. This study aims to employ population pharmacokinetic (PPK) models to investigate the pharmacokinetics of MTX in Chinese patients with PCNSL, thereby facilitating personalized therapeutic strategies for these patients.MethodA retrospective dataset comprising 6074 MTX plasma concentrations from 752 adult patients with PCNSL receiving high-dose methotrexate (HD-MTX) therapy was employed to construct the PPK model, utilizing the nonlinear mixed-effects modeling approach. The pharmacokinetics of MTX were characterized using a three-compartment model in conjunction with a proportional residual model. Covariate effects on model parameters were evaluated using forward addition and backward elimination approaches. Model performance was assessed through goodness-of-fit, bootstrap analysis, and visual predictive checks.ResultIn the final PPK models, the estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), alanine aminotransferase (ALT), and a combined genotype of ABCC-ABCG-ADORA2A were identified as significant covariates impacting the clearance (CL) of MTX. Additionally, total protein (TP) was found to be a significant covariate influencing inter-compartmental clearance (Q). The relationship between pharmacokinetic parameters and covariates was quantified as follows: CL (L/h) = 8.45×(eGFR⁄101.8)0.67×(BUN⁄4.6)−0.08×(ALT⁄25)0.03×a (a = 0.91 for gene-model if ABCC-ABCG-ADORA2A mutation, otherwise a = 1); Q1 (L/h) = 0.04×(TP⁄58)b (b = −1.68 for nongene-model and b = −1.72 for gene-model). Bootstrap analysis and visual predictive checks demonstrated the stability and adequate predictive capacity of the final PPK models.ConclusionIn managing HD-MTX therapy for PCNSL patients, it is essential to consider pharmacokinetic factors such as eGFR, BUN, ALT, TP, and genetic polymorphisms. The PPK models developed will aid in optimizing and personalizing HD-MTX treatment for PCNSL patients.